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RGB-N | RGB-N-master/lib/compact_bilinear_pooling/compact_bilinear_pooling_test.py | from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
from compact_bilinear_pooling import compact_bilinear_pooling_layer
def bp(bottom1, bottom2, sum_pool=True):
assert(np.all(bottom1.shape[:3] == bottom2.shape[:3]))
batch_size, height, width = bottom1.shape[:3]
output_dim = bottom1.shape[-1] * bottom2.shape[-1]
bottom1_flat = bottom1.reshape((-1, bottom1.shape[-1]))
bottom2_flat = bottom2.reshape((-1, bottom2.shape[-1]))
output = np.empty((batch_size*height*width, output_dim), np.float32)
for n in range(len(output)):
output[n, ...] = np.outer(bottom1_flat[n], bottom2_flat[n]).reshape(-1)
output = output.reshape((batch_size, height, width, output_dim))
if sum_pool:
output = np.sum(output, axis=(1, 2))
return output
# Input and output tensors
# Input channels need to be specified for shape inference
input_dim1 = 2048
input_dim2 = 2048
output_dim = 16000
bottom1 = tf.placeholder(tf.float32, [None, None, None, input_dim1])
bottom2 = tf.placeholder(tf.float32, [None, None, None, input_dim2])
top = compact_bilinear_pooling_layer(bottom1, bottom2, output_dim, sum_pool=True)
grad = tf.gradients(top, [bottom1, bottom2])
def cbp(bottom1_value, bottom2_value):
sess = tf.get_default_session()
return sess.run(top, feed_dict={bottom1: bottom1_value,
bottom2: bottom2_value})
def cbp_with_grad(bottom1_value, bottom2_value):
sess = tf.get_default_session()
return sess.run([top]+grad, feed_dict={bottom1: bottom1_value,
bottom2: bottom2_value})
def test_kernel_approximation(batch_size, height, width):
print("Testing kernel approximation...")
# Input values
x = np.random.rand(batch_size, height, width, input_dim1).astype(np.float32)
y = np.random.rand(batch_size, height, width, input_dim2).astype(np.float32)
z = np.random.rand(batch_size, height, width, input_dim1).astype(np.float32)
w = np.random.rand(batch_size, height, width, input_dim2).astype(np.float32)
# Compact Bilinear Pooling results
cbp_xy = cbp(x, y)
cbp_zw = cbp(z, w)
# (Original) Bilinear Pooling results
bp_xy = bp(x, y)
bp_zw = bp(z, w)
# Check the kernel results of Compact Bilinear Pooling
# against Bilinear Pooling
cbp_kernel = np.sum(cbp_xy*cbp_zw, axis=1)
bp_kernel = np.sum(bp_xy*bp_zw, axis=1)
ratio = cbp_kernel / bp_kernel
print("ratio between Compact Bilinear Pooling (CBP) and Bilinear Pooling (BP):")
print(ratio)
assert(np.all(np.abs(ratio - 1) < 2e-2))
print("Passed.")
def test_large_input(batch_size, height, width):
print("Testing large input...")
# Input values
x = np.random.rand(batch_size, height, width, input_dim1).astype(np.float32)
y = np.random.rand(batch_size, height, width, input_dim2).astype(np.float32)
# Compact Bilinear Pooling results
_ = cbp_with_grad(x, y)
# Test passes iff no exception occurs.
print("Passed.")
def main():
sess = tf.InteractiveSession()
test_kernel_approximation(batch_size=2, height=3, width=4)
test_large_input(batch_size=64, height=14, width=14)
sess.close()
if __name__ == '__main__':
main()
| 3,295 | 34.44086 | 84 | py |
RGB-N | RGB-N-master/lib/compact_bilinear_pooling/__init__.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
| 248 | 34.571429 | 58 | py |
RGB-N | RGB-N-master/lib/compact_bilinear_pooling/sequential_fft/sequential_batch_fft_test.py | from __future__ import absolute_import, division, print_function
import tensorflow as tf
import numpy as np
from sequential_batch_fft_ops import sequential_batch_fft, sequential_batch_ifft
compute_size = 128
x = tf.placeholder(tf.complex64, [None, None])
x_128 = tf.placeholder(tf.complex128, [None, None])
# FFT
x_fft = sequential_batch_fft(x, compute_size)
x_fft_128 = sequential_batch_fft(x_128, compute_size)
x_fft_tf = tf.fft(x)
# IFFT
x_ifft = sequential_batch_ifft(x, compute_size)
x_ifft_128 = sequential_batch_ifft(x_128, compute_size)
x_ifft_tf = tf.ifft(x)
# Grads
gx_fft = tf.gradients(x_fft, x)[0]
gx_fft_128 = tf.gradients(x_fft_128, x_128)[0]
gx_fft_tf = tf.gradients(x_fft_tf, x)[0]
gx_ifft = tf.gradients(x_ifft, x)[0]
gx_ifft_128 = tf.gradients(x_ifft_128, x_128)[0]
gx_ifft_tf = tf.gradients(x_ifft_tf, x)[0]
def test_shape():
print("Testing shape...")
# Test Shape inference. Output shape should be
# the same as input shape
input_pl = tf.placeholder(tf.complex64, [1000, 16000])
output_fft = sequential_batch_fft(input_pl)
output_ifft = sequential_batch_ifft(input_pl)
g_fft = tf.gradients(output_fft, input_pl)[0]
g_ifft = tf.gradients(output_ifft, input_pl)[0]
assert(output_fft.get_shape() == input_pl.get_shape())
assert(output_ifft.get_shape() == input_pl.get_shape())
assert(g_fft.get_shape() == input_pl.get_shape())
assert(g_ifft.get_shape() == input_pl.get_shape())
print("Passed.")
def test_forward():
# Test forward and compare with tf.batch_fft and tf.batch_ifft
print("Testing forward...")
sess = tf.Session()
for dim in range(1000, 5000, 1000):
for batch_size in range(1, 10):
x_val = (np.random.randn(batch_size, dim) +
np.random.randn(batch_size, dim) * 1j).astype(np.complex64)
# Forward complex64
x_fft_val, x_ifft_tf_val = sess.run([x_fft, x_ifft], {x: x_val})
# Forward complex128
x_fft_128_val, x_ifft_128_val = sess.run([x_fft_128, x_ifft_128],
{x_128: x_val.astype(np.complex128)})
# Forward with reference tf.batch_fft and tf.batch_ifft
x_fft_tf_val, x_ifft_val = sess.run([x_fft_tf, x_ifft_tf], {x: x_val})
ref_sum_fft = np.sum(np.abs(x_fft_tf_val))
ref_sum_ifft = np.sum(np.abs(x_ifft_tf_val))
relative_diff_fft = np.sum(np.abs(x_fft_val - x_fft_tf_val)) / ref_sum_fft
relative_diff_ifft = np.sum(np.abs(x_ifft_val - x_ifft_tf_val)) / ref_sum_ifft
relative_diff_fft128 = np.sum(np.abs(x_fft_128_val - x_fft_tf_val)) / ref_sum_fft
relative_diff_ifft128 = np.sum(np.abs(x_ifft_128_val - x_ifft_tf_val)) / ref_sum_ifft
assert(relative_diff_fft < 1e-5)
assert(relative_diff_fft128 < 1e-5)
assert(relative_diff_ifft < 1e-5)
assert(relative_diff_ifft128 < 1e-5)
sess.close()
print("Passed.")
def test_gradient():
# Test Backward and compare with tf.batch_fft and tf.batch_ifft
print("Testing gradient...")
sess = tf.Session()
for dim in range(1000, 5000, 1000):
for batch_size in range(1, 10):
x_val = (np.random.randn(batch_size, dim) +
np.random.randn(batch_size, dim) * 1j).astype(np.complex64)
# Backward complex64
gx_fft_val, gx_ifft_tf_val = sess.run([gx_fft, gx_ifft], {x: x_val})
# Backward complex128
gx_fft_128_val, gx_ifft_128_val = sess.run([gx_fft_128, gx_ifft_128],
{x_128: x_val.astype(np.complex128)})
# Backward with reference tf.batch_fft and tf.batch_ifft
gx_fft_tf_val, gx_ifft_val = sess.run([gx_fft_tf, gx_ifft_tf], {x: x_val})
ref_sum_fft = np.sum(np.abs(gx_fft_tf_val))
ref_sum_ifft = np.sum(np.abs(gx_ifft_tf_val))
relative_diff_fft = np.sum(np.abs(gx_fft_val - gx_fft_tf_val)) / ref_sum_fft
relative_diff_ifft = np.sum(np.abs(gx_ifft_val - gx_ifft_tf_val)) / ref_sum_ifft
relative_diff_fft128 = np.sum(np.abs(gx_fft_128_val - gx_fft_tf_val)) / ref_sum_fft
relative_diff_ifft128 = np.sum(np.abs(gx_ifft_128_val - gx_ifft_tf_val)) / ref_sum_ifft
assert(relative_diff_fft < 1e-5)
assert(relative_diff_fft128 < 1e-5)
assert(relative_diff_ifft < 1e-5)
assert(relative_diff_ifft128 < 1e-5)
sess.close()
print("Passed.")
def test_large_input():
# Very large input size, where tf.batch_fft and tf.batch_ifft
# will run OOM
print("Testing large input...")
sess = tf.Session()
batch_size, dim = 64*16*16, 16000
print("Forwarding and Backwarding with input shape",
[batch_size, dim], "This may take a while...")
x_val = (np.random.randn(batch_size, dim) +
np.random.randn(batch_size, dim) * 1j).astype(np.complex64)
sess.run(tf.group(x_fft, x_ifft, gx_fft, gx_ifft), {x: x_val})
sess.close()
# Test passes iff no exception occurs.
print("Passed.")
if __name__ == "__main__":
test_shape()
test_forward()
test_gradient()
test_large_input()
| 5,280 | 38.706767 | 99 | py |
RGB-N | RGB-N-master/lib/compact_bilinear_pooling/sequential_fft/sequential_batch_fft_ops.py | from __future__ import absolute_import, division, print_function
import os.path as osp
import tensorflow as tf
from tensorflow.python.framework import ops
# load module
module = tf.load_op_library(osp.join(osp.dirname(__file__),
'build/sequential_batch_fft.so'))
sequential_batch_fft = module.sequential_batch_fft
sequential_batch_ifft = module.sequential_batch_ifft
# Shape registration is moved to C++ to be compatible with TensorFlow 1.0 API
# @tf.RegisterShape("SequentialBatchFFT")
# def _SequentialBatchFFTShape(op):
# return [op.inputs[0].get_shape()]
#
# @tf.RegisterShape("SequentialBatchIFFT")
# def _SequentialBatchIFFTShape(op):
# return [op.inputs[0].get_shape()]
@ops.RegisterGradient("SequentialBatchFFT")
def _SequentialBatchFFTGrad(op, grad):
if (grad.dtype == tf.complex64):
size = tf.cast(tf.shape(grad)[1], tf.float32)
return (sequential_batch_ifft(grad, op.get_attr("compute_size"))
* tf.complex(size, 0.))
else:
size = tf.cast(tf.shape(grad)[1], tf.float64)
return (sequential_batch_ifft(grad, op.get_attr("compute_size"))
* tf.complex(size, tf.zeros([], tf.float64)))
@ops.RegisterGradient("SequentialBatchIFFT")
def _SequentialBatchIFFTGrad(op, grad):
if (grad.dtype == tf.complex64):
rsize = 1. / tf.cast(tf.shape(grad)[1], tf.float32)
return (sequential_batch_fft(grad, op.get_attr("compute_size"))
* tf.complex(rsize, 0.))
else:
rsize = 1. / tf.cast(tf.shape(grad)[1], tf.float64)
return (sequential_batch_fft(grad, op.get_attr("compute_size"))
* tf.complex(rsize, tf.zeros([], tf.float64)))
| 1,700 | 36.8 | 77 | py |
RGB-N | RGB-N-master/lib/compact_bilinear_pooling/sequential_fft/__init__.py | from .sequential_batch_fft_ops import sequential_batch_fft, sequential_batch_ifft
| 82 | 40.5 | 81 | py |
RGB-N | RGB-N-master/lib/datasets/voc_eval.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import xml.etree.ElementTree as ET
import os
import pickle
import numpy as np
import pdb
def parse_rec(filename):
""" Parse a PASCAL VOC xml file """
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.find('name').text
obj_struct['pose'] = obj.find('pose').text
obj_struct['truncated'] = int(obj.find('truncated').text)
obj_struct['difficult'] = int(obj.find('difficult').text)
bbox = obj.find('bndbox')
obj_struct['bbox'] = [int(bbox.find('xmin').text),
int(bbox.find('ymin').text),
int(bbox.find('xmax').text),
int(bbox.find('ymax').text)]
objects.append(obj_struct)
return objects
def voc_ap(rec, prec, use_07_metric=False):
""" ap = voc_ap(rec, prec, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False).
"""
if use_07_metric:
# 11 point metric
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap = ap + p / 11.
else:
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def parse_txt(fileline,classname):
#classes=('__background__','person_au','person_tp','airplane_tp','airplane_au','dog_tp','dog_au',
#'train_tp','train_au','bed_tp','bed_au','refrigerator_tp','refrigerator_au')
#classes=('__background__','person_au','person_tp','tv_tp','tv_au','airplane_tp','airplane_au','dog_tp','dog_au',
#'bench_tp','bench_au','train_tp','train_au','broccoli_tp','broccoli_au','kite_tp','kite_au','bed_tp','bed_au','refrigerator_tp','refrigerator_au','bowl_tp','bowl_au')
classes=('__background__', 'tamper','authentic')
classes=('authentic', 'tamper')
#classes=('__background__', # always index 0
#'splicing','removal','manipulation')
#classes=('__background__','person_au','tv_au','airplane_au','dog_au',
#'bench_au','train_au','broccoli_au','kite_au','bed_au','refrigerator_au','bowl_au')
class_to_ind = dict(list(zip(classes, list(range(len(classes))))))
num_objs = int(len(fileline.split(' ')[1:])/5)
objects=[]
obj={}
#print(fileline.split())
#pdb.set_trace()
#object['name']=fileline.split(" ")[0]
for i in range(num_objs):
obj['bbox']=[float(fileline.split(' ')[5*i+1]),
float(fileline.split(' ')[5*i+2]),
float(fileline.split(' ')[5*i+3]),
float(fileline.split(' ')[5*i+4])]
try:
obj['cls']=class_to_ind[fileline.split(' ')[5*i+5]]
except:
#pdb.set_trace()
obj['cls']=int(fileline.split(' ')[5*i+5])
#obj['bbox']=[int(fileline.split(' ')[(classname-1)*5+1]),
#int(fileline.split(' ')[(classname-1)*5+2]),
#int(fileline.split(' ')[(classname-1)*5+3]),
#int(fileline.split(' ')[(classname-1)*5+4])]
#obj['cls']=int(fileline.split(' ')[(classname-1)*5+5])
objects.append(obj.copy())
return objects
def voc_eval(detpath,
detpath2,
annopath,
imagesetfile,
classname,
cachedir,
ovthresh=0.5,
use_07_metric=False,
fuse=False):
"""rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric])
Top level function that does the PASCAL VOC evaluation.
detpath: Path to detections
detpath.format(classname) should produce the detection results file.
annopath: Path to annotations
annopath.format(imagename) should be the xml annotations file.
imagesetfile: Text file containing the list of images, one image per line.
classname: Category name (duh)
cachedir: Directory for caching the annotations
[ovthresh]: Overlap threshold (default = 0.5)
[use_07_metric]: Whether to use VOC07's 11 point AP computation
(default False)
"""
# assumes detections are in detpath.format(classname)
# assumes annotations are in annopath.format(imagename)
# assumes imagesetfile is a text file with each line an image name
# cachedir caches the annotations in a pickle file
# first load gt
if not os.path.isdir(cachedir):
os.mkdir(cachedir)
cachefile = os.path.join(cachedir, 'annots.pkl')
# read list of images
with open(imagesetfile, 'r') as f:
lines = f.readlines()
imagenames = [x.strip() for x in lines]
if not os.path.isfile(cachefile):
# load annots
recs = {}
for i, imagename in enumerate(imagenames):
name=imagename.split(' ')[0]
recs[name] = parse_txt(imagename,classname)
#recs[imagename] = parse_rec(annopath.format(imagename))
if i % 100 == 0:
print('Reading annotation for {:d}/{:d}'.format(
i + 1, len(imagenames)))
# save
print('Saving cached annotations to {:s}'.format(cachefile))
#with open(cachefile, 'w') as f:
#pickle.dump(recs, f)
else:
# load
with open(cachefile, 'rb') as f:
try:
recs = pickle.load(f)
except:
recs = pickle.load(f, encoding='bytes')
# extract gt objects for this class
class_recs = {}
npos = 0
#pdb.set_trace()
for imagename in imagenames:
name=imagename.split(' ')[0]
R = [obj for obj in recs[name] if obj['cls'] == classname]
npos=npos+len(R)
bbox = np.array([x['bbox'] for x in R])
#difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
det = [False] * len(R)
class_recs[name] = {'bbox': bbox,
#'difficult': difficult,
'det': det}
# read dets
detfile = detpath.format(classname)
detfile_n = detpath2.format(classname)
#print(detfile)
with open(detfile, 'r') as f:
lines = f.readlines()
if os.path.isfile(detfile_n):
with open(detfile_n, 'r') as f_n:
n_lines = f_n.readlines()
n_splitlines = [x.strip().split(' ') for x in n_lines]
#print(n_splitlines)
image_n = [x[0] for x in n_splitlines]
confidence_n = np.array([float(x[1]) for x in n_splitlines])
BB_n = np.array([[float(z) for z in x[2:]] for x in n_splitlines])
splitlines = [x.strip().split(' ') for x in lines]
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
count=np.zeros(10)
noise_ct=0
select_final=np.array([True]*len(image_ids))
image_select=[]
if BB.shape[0] > 0 and fuse:
for k in range(len(image_ids)):
if image_ids[k] in image_select:
select_final[k]=False
continue
if image_ids[k] in image_n:
bb = BB[k, :].astype(float)
index=[i for i,ex in enumerate(image_n) if ex==image_ids[k]]
bb1 = BB_n[index, :].astype(float)
#print(index,bb1)
#pdb.set_trace()
c_n=confidence_n[index]
ix_min = np.maximum(bb1[:, 0], bb[0])
iy_min = np.maximum(bb1[:, 1], bb[1])
ix_max = np.minimum(bb1[:, 2], bb[2])
iy_max = np.minimum(bb1[:, 3], bb[3])
iw = np.maximum(ix_max - ix_min + 1., 0.)
ih = np.maximum(iy_max - iy_min + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(bb1[:, 2] - bb1[:, 0] + 1.) *
(bb1[:, 3] - bb1[:, 1] + 1.) - inters)
overlaps = inters / uni
ov_max = np.max(overlaps)
jmax = np.argmax(overlaps)
if ov_max>=0.5:
count[int(ov_max*10)]=count[int(ov_max*10)]+1
#print(confidence[k],c_n)
#confidence[k]=np.maximum(confidence[k],c_n[jmax])
confidence[k]=(confidence[k]+c_n[jmax])/2
#pdb.set_trace()
BB[k,:]=(confidence[k]*BB[k,:]+c_n[jmax]*bb1[jmax,:])/np.maximum(confidence[k]+c_n[jmax], np.finfo(np.float64).eps)
image_select.append(image_ids[k])
#if confidence[k]<c_n[jmax]-0.5:
#BB[k,:]=bb1[jmax,:]
#print(image_ids[k],confidence[k],c_n[jmax])
elif ov_max<0.5 and ov_max>0.1:
count[int(ov_max*10)]=count[int(ov_max*10)]+1
image_select.append(image_ids[k])
#select_final[k]=False
#BB[k,:]=(confidence[k]*BB[k,:]+c_n[jmax]*bb1[jmax,:])/(confidence[k]+c_n[jmax])
#pass
#select_final[k]=False
#confidence[k]=0.7*confidence[k]+0.3*c_n[jmax]
#if confidence[k]<c_n[jmax]:
#BB[k,:]=bb1[jmax,:]
#print(image_ids[k],confidence[k],c_n[jmax])
#confidence[k]=confidence[k]*max(ov_max+0.2,0.6)
else:
count[int(ov_max*10)]=count[int(ov_max*10)]+1
select_final[k]=False
#confidence[k]=confidence[k]*0.9
#if confidence[k]<c_n[jmax]:
#BB[k,:]=bb1[jmax,:]
#confidence[k]=c_n[jmax]*0.9
for nk in range(len(image_n)):
if image_n[nk] not in image_ids:
noise_ct=noise_ct+1
#image_ids.append(image_n[nk])
#select_final.append(select_final,True)
#confidence=np.append(confidence,confidence_n[nk])
#BB=np.vstack((BB,BB_n[nk,:]))
print('rgb no overlap: {:s}'.format(count))
print('noise no overlap: {:d}'.format(noise_ct))
image_ids=np.extract(select_final,image_ids)
confidence=np.extract(select_final,confidence)
BB=BB[select_final,:]
nd = len(image_ids)
#print(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
bb=[]
bb1=[]
#pdb.set_trace()
if BB.shape[0] > 0:
# sort by confidence
sorted_ind = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
#print(class_recs)
#print(sorted_ind)
# go down dets and mark TPs and FPs
for d in range(nd):
#print(bb1)
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
if fuse:
if image_ids[d] in image_n:
index=[i for i,ex in enumerate(image_n) if ex==image_ids[d]]
bb1 = BB_n[index, :].astype(float)
ix_min = np.maximum(bb1[:, 0], bb[0])
iy_min = np.maximum(bb1[:, 1], bb[1])
ix_max = np.minimum(bb1[:, 2], bb[2])
iy_max = np.minimum(bb1[:, 3], bb[3])
iw_n = np.maximum(ix_max - ix_min + 1., 0.)
ih_n = np.maximum(iy_max - iy_min + 1., 0.)
inters_n = iw_n * ih_n
# union
un = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(bb1[:, 2] - bb1[:, 0] + 1.) *
(bb1[:, 3] - bb1[:, 1] + 1.) - inters_n)
overlaps_n = inters_n / un
ov_max_n = np.max(overlaps_n)
ovmax = -np.inf
BBGT = R['bbox'].astype(float)
#print(BBGT)
#pdb.set_trace()
if BBGT.size > 0:
# compute overlaps
# intersection
#print(BBGT)
#print(bb)
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(BBGT[:, 2] - BBGT[:, 0] + 1.) *
(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
print("overlap:")
print(overlaps)
if ovmax > ovthresh:
#print(R['det'][jmax])
#if not R['difficult'][jmax]:
if not R['det'][jmax]:
#print(R['det'][jmax])
tp[d] = 1.
R['det'][jmax] = 1
else:
fp[d] = 1.
else:
print('fp:{:s}'.format(image_ids[d]))
if fuse:
print('score:{:f}, ovmax:{:f}'.format(-sorted_scores[d],ov_max_n))
fp[d] = 1.
# compute precision recall
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = voc_ap(rec, prec, use_07_metric)
return rec, prec, ap | 13,267 | 34.100529 | 173 | py |
RGB-N | RGB-N-master/lib/datasets/dist_fake.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
import pdb
from .voc_eval import voc_eval
from model.config import cfg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class dist_fake(imdb):
def __init__(self, image_set, year, dist_path=None):
imdb.__init__(self, image_set)
self._year = year
self._image_set = image_set.split('dist_')[1]
self._dist_path = self._get_default_path() if dist_path is None \
else dist_path
self._data_path=self._dist_path
self._classes = ('__background__', # always index 0
'tamper','authentic')
self._classes = ('authentic', # always index 0
'tamper')
#self.classes =('authentic', # always index 0
#'splicing','removal')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = {'.png','.jpg','.tif','.bmp','.JPG'}
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(os.path.splitext(self._image_index[i].split(' ')[0])[0])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
#image_path = os.path.join('/home-3/pengzhou@umd.edu/work/xintong/medifor/portrait/test_data',
#index + ext)
image_path = os.path.join(self._data_path,
index + ext)
image_path1=os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset/NC2016_Test0613',
index + ext)
if os.path.isfile(image_path):
return image_path
elif os.path.isfile(image_path1):
return image_path1
else:
continue
assert os.path.isfile(image_path) and os.path.isfile(image_path1), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path,
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
#print(image_index)
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'NC2016_Test0613')
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self.roidb_gt(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def roidb_gt(self,image_id):
num_objs = int(len(image_id.split(' ')[1:])/5)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix in range(num_objs):
bbox = image_id.split(' ')[ix*5+1:ix*5+5]
# Make pixel indexes 0-based
x1 = float(bbox[0])
y1 = float(bbox[1])
x2 = float(bbox[2])
y2 = float(bbox[3])
if x1<0:
x1=0
if y1<0:
y1=0
try:
cls=self._class_to_ind[image_id.split(' ')[ix*5+5]]
except:
if int(image_id.split(' ')[ix*5+5])==0:
print('authentic')
cls=2
else:
cls = int(image_id.split(' ')[ix*5+5])
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 ) * (y2 - y1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'JPGed':False,
'noised':False,
'seg_areas': seg_areas}
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'nist_' + self._image_set + '_{:s}.txt'
path = os.path.join(
'.',
filename)
return path
def _get_voc_noise_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'nist_' + self._image_set + '_{:s}_noise.txt'
path = os.path.join(
'.',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} VOC results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
print(filename)
with open(filename, 'w') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
#pdb.set_trace()
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(index.split(' ')[0], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
#pdb.set_trace()
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._dist_path,
'coco_multi' ,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._dist_path,
self._image_set + '.txt')
cachedir = os.path.join(self._dist_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
#use_07_metric = True if int(self._year) < 2010 else False
use_07_metric = False
print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__' or cls == self.classes[0]:
cls_ind=0
continue
else:
cls_ind=self._class_to_ind[cls]
#elif cls=='median_filtering':
#cls_ind=3
#continue
filename = self._get_voc_results_file_template().format(cls)
filename2 = self._get_voc_noise_results_file_template().format(cls)
print(cls_ind)
rec, prec, ap = voc_eval(
filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric,fuse=False)
aps += [ap]
print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
fig=plt.figure()
plt.plot(rec,prec)
fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
plt.xlabel('recall',fontsize=15)
plt.xlim((0,1.0))
plt.ylim((0,1.0))
plt.ylabel('precision',fontsize=15)
fig.savefig('{}.jpg'.format(cls))
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.dist_fake import dist_fake
d = dist_fake('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
| 13,392 | 34.619681 | 104 | py |
RGB-N | RGB-N-master/lib/datasets/pascal_voc.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import xml.etree.ElementTree as ET
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
from .voc_eval import voc_eval
from model.config import cfg
class pascal_voc(imdb):
def __init__(self, image_set, year, devkit_path=None):
imdb.__init__(self, 'voc_' + year + '_' + image_set)
self._year = year
self._image_set = image_set
self._devkit_path = self._get_default_path() if devkit_path is None \
else devkit_path
self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year)
self._classes = ('__background__', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = '.jpg'
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
self._salt = str(uuid.uuid4())
self._comp_id = 'comp4'
# PASCAL specific config options
self.config = {'cleanup': True,
'use_salt': True,
'use_diff': False,
'matlab_eval': False,
'rpn_file': None}
assert os.path.exists(self._devkit_path), \
'VOCdevkit path does not exist: {}'.format(self._devkit_path)
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(self._image_index[i])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
image_path = os.path.join(self._data_path, 'JPEGImages',
index + self._image_ext)
assert os.path.exists(image_path), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main',
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year)
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self._load_pascal_annotation(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt'
path = os.path.join(
self._devkit_path,
'results',
'VOC' + self._year,
'Main',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} VOC results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index, dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._devkit_path,
'VOC' + self._year,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._devkit_path,
'VOC' + self._year,
'ImageSets',
'Main',
self._image_set + '.txt')
cachedir = os.path.join(self._devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True if int(self._year) < 2010 else False
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
rec, prec, ap = voc_eval(
filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric)
aps += [ap]
print(('AP for {} = {:.4f}'.format(cls, ap)))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
if self.config['matlab_eval']:
self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.pascal_voc import pascal_voc
d = pascal_voc('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
| 11,180 | 35.301948 | 85 | py |
RGB-N | RGB-N-master/lib/datasets/imdb.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path as osp
import PIL
from utils.cython_bbox import bbox_overlaps
import numpy as np
import scipy.sparse
from model.config import cfg
class imdb(object):
"""Image database."""
def __init__(self, name, classes=None):
self._name = name
self._num_classes = 0
if not classes:
self._classes = []
else:
self._classes = classes
self._image_index = []
self._obj_proposer = 'gt'
self._roidb = None
self._roidb_handler = self.default_roidb
# Use this dict for storing dataset specific config options
self.config = {}
@property
def name(self):
return self._name
@property
def num_classes(self):
return len(self._classes)
@property
def classes(self):
return self._classes
@property
def image_index(self):
return self._image_index
@property
def roidb_handler(self):
return self._roidb_handler
@roidb_handler.setter
def roidb_handler(self, val):
self._roidb_handler = val
def set_proposal_method(self, method):
method = eval('self.' + method + '_roidb')
self.roidb_handler = method
@property
def roidb(self):
# A roidb is a list of dictionaries, each with the following keys:
# boxes
# gt_overlaps
# gt_classes
# flipped
if self._roidb is not None:
return self._roidb
self._roidb = self.roidb_handler()
return self._roidb
@property
def cache_path(self):
cache_path = osp.abspath(osp.join(cfg.DATA_DIR, 'cache'))
if not os.path.exists(cache_path):
os.makedirs(cache_path)
return cache_path
@property
def num_images(self):
return len(self.image_index)
def image_path_at(self, i):
raise NotImplementedError
def default_roidb(self):
raise NotImplementedError
def evaluate_detections(self, all_boxes, output_dir=None):
"""
all_boxes is a list of length number-of-classes.
Each list element is a list of length number-of-images.
Each of those list elements is either an empty list []
or a numpy array of detection.
all_boxes[class][image] = [] or np.array of shape #dets x 5
"""
raise NotImplementedError
def _get_widths(self):
return [PIL.Image.open(self.image_path_at(i)).size[0]
for i in range(self.num_images)]
def append_flipped_images(self):
num_images = self.num_images
widths = self._get_widths()
for i in range(num_images):
boxes = self.roidb[i]['boxes'].copy()
oldx1 = boxes[:, 0].copy()
oldx2 = boxes[:, 2].copy()
#print(oldx2,oldx1,widths[i])
if oldx2>=widths[i]:
oldx2=widths[i]-1
boxes[:, 0] = widths[i] - oldx2 - 1
boxes[:, 2] = widths[i] - oldx1 - 1
#print(boxes)
assert (boxes[:, 2] >= boxes[:, 0]).all()
entry = {'boxes': boxes,
'gt_overlaps': self.roidb[i]['gt_overlaps'],
'gt_classes': self.roidb[i]['gt_classes'],
'flipped': True,
'JPGed':False,
'noised':False}
self.roidb.append(entry)
self._image_index = self._image_index * 2
def append_noise_images(self):
num_images = self.num_images
widths = self._get_widths()
for i in range(num_images):
boxes = self.roidb[i]['boxes'].copy()
flipped= self.roidb[i]['flipped']
jpg= self.roidb[i]['JPGed']
entry = {'boxes': boxes,
'gt_overlaps': self.roidb[i]['gt_overlaps'],
'gt_classes': self.roidb[i]['gt_classes'],
'flipped':flipped,
'JPGed':jpg,
'noised': True}
self.roidb.append(entry)
self._image_index = self._image_index * 2
def append_jpg_images(self):
num_images = self.num_images
widths = self._get_widths()
for i in range(num_images):
boxes = self.roidb[i]['boxes'].copy()
flipped= self.roidb[i]['flipped']
noised= self.roidb[i]['noised']
entry = {'boxes': boxes,
'gt_overlaps': self.roidb[i]['gt_overlaps'],
'gt_classes': self.roidb[i]['gt_classes'],
'flipped':flipped,
'JPGed':True,
'noised':noised}
self.roidb.append(entry)
self._image_index = self._image_index * 2
def evaluate_recall(self, candidate_boxes=None, thresholds=None,
area='all', limit=None):
"""Evaluate detection proposal recall metrics.
Returns:
results: dictionary of results with keys
'ar': average recall
'recalls': vector recalls at each IoU overlap threshold
'thresholds': vector of IoU overlap thresholds
'gt_overlaps': vector of all ground-truth overlaps
"""
# Record max overlap value for each gt box
# Return vector of overlap values
areas = {'all': 0, 'small': 1, 'medium': 2, 'large': 3,
'96-128': 4, '128-256': 5, '256-512': 6, '512-inf': 7}
area_ranges = [[0 ** 2, 1e5 ** 2], # all
[0 ** 2, 32 ** 2], # small
[32 ** 2, 96 ** 2], # medium
[96 ** 2, 1e5 ** 2], # large
[96 ** 2, 128 ** 2], # 96-128
[128 ** 2, 256 ** 2], # 128-256
[256 ** 2, 512 ** 2], # 256-512
[512 ** 2, 1e5 ** 2], # 512-inf
]
assert area in areas, 'unknown area range: {}'.format(area)
area_range = area_ranges[areas[area]]
gt_overlaps = np.zeros(0)
num_pos = 0
for i in range(self.num_images):
# Checking for max_overlaps == 1 avoids including crowd annotations
# (...pretty hacking :/)
max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(axis=1)
gt_inds = np.where((self.roidb[i]['gt_classes'] > 0) &
(max_gt_overlaps == 1))[0]
gt_boxes = self.roidb[i]['boxes'][gt_inds, :]
gt_areas = self.roidb[i]['seg_areas'][gt_inds]
valid_gt_inds = np.where((gt_areas >= area_range[0]) &
(gt_areas <= area_range[1]))[0]
gt_boxes = gt_boxes[valid_gt_inds, :]
num_pos += len(valid_gt_inds)
if candidate_boxes is None:
# If candidate_boxes is not supplied, the default is to use the
# non-ground-truth boxes from this roidb
non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0]
boxes = self.roidb[i]['boxes'][non_gt_inds, :]
else:
boxes = candidate_boxes[i]
if boxes.shape[0] == 0:
continue
if limit is not None and boxes.shape[0] > limit:
boxes = boxes[:limit, :]
overlaps = bbox_overlaps(boxes.astype(np.float),
gt_boxes.astype(np.float))
_gt_overlaps = np.zeros((gt_boxes.shape[0]))
for j in range(gt_boxes.shape[0]):
# find which proposal box maximally covers each gt box
argmax_overlaps = overlaps.argmax(axis=0)
# and get the iou amount of coverage for each gt box
max_overlaps = overlaps.max(axis=0)
# find which gt box is 'best' covered (i.e. 'best' = most iou)
gt_ind = max_overlaps.argmax()
gt_ovr = max_overlaps.max()
assert (gt_ovr >= 0)
# find the proposal box that covers the best covered gt box
box_ind = argmax_overlaps[gt_ind]
# record the iou coverage of this gt box
_gt_overlaps[j] = overlaps[box_ind, gt_ind]
assert (_gt_overlaps[j] == gt_ovr)
# mark the proposal box and the gt box as used
overlaps[box_ind, :] = -1
overlaps[:, gt_ind] = -1
# append recorded iou coverage level
gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))
gt_overlaps = np.sort(gt_overlaps)
if thresholds is None:
step = 0.05
thresholds = np.arange(0.5, 0.95 + 1e-5, step)
recalls = np.zeros_like(thresholds)
# compute recall for each iou threshold
for i, t in enumerate(thresholds):
recalls[i] = (gt_overlaps >= t).sum() / float(num_pos)
# ar = 2 * np.trapz(recalls, thresholds)
ar = recalls.mean()
return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds,
'gt_overlaps': gt_overlaps}
def create_roidb_from_box_list(self, box_list, gt_roidb):
assert len(box_list) == self.num_images, \
'Number of boxes must match number of ground-truth images'
roidb = []
for i in range(self.num_images):
boxes = box_list[i]
num_boxes = boxes.shape[0]
overlaps = np.zeros((num_boxes, self.num_classes), dtype=np.float32)
if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0:
gt_boxes = gt_roidb[i]['boxes']
gt_classes = gt_roidb[i]['gt_classes']
gt_overlaps = bbox_overlaps(boxes.astype(np.float),
gt_boxes.astype(np.float))
argmaxes = gt_overlaps.argmax(axis=1)
maxes = gt_overlaps.max(axis=1)
I = np.where(maxes > 0)[0]
overlaps[I, gt_classes[argmaxes[I]]] = maxes[I]
overlaps = scipy.sparse.csr_matrix(overlaps)
roidb.append({
'boxes': boxes,
'gt_classes': np.zeros((num_boxes,), dtype=np.int32),
'gt_overlaps': overlaps,
'flipped': False,
'JPGed':False,
'noised':False
#'seg_areas': np.zeros((num_boxes,), dtype=np.float32),
})
return roidb
@staticmethod
def merge_roidbs(a, b):
assert len(a) == len(b)
for i in range(len(a)):
a[i]['boxes'] = np.vstack((a[i]['boxes'], b[i]['boxes']))
a[i]['gt_classes'] = np.hstack((a[i]['gt_classes'],
b[i]['gt_classes']))
a[i]['gt_overlaps'] = scipy.sparse.vstack([a[i]['gt_overlaps'],
b[i]['gt_overlaps']])
# a[i]['seg_areas'] = np.hstack((a[i]['seg_areas'],
#b[i]['seg_areas']))
return a
def competition_mode(self, on):
"""Turn competition mode on or off."""
pass
| 10,309 | 33.481605 | 74 | py |
RGB-N | RGB-N-master/lib/datasets/factory.py | # --------------------------------------------------------
# Tensorflow RGB-N
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
"""Factory method for easily getting imdbs by name."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
__sets = {}
from datasets.pascal_voc import pascal_voc
from datasets.coco import coco
from datasets.casia import casia
from datasets.dist_fake import dist_fake
from datasets.nist import nist
from datasets.dvmm import dvmm
from datasets.swapme import swapme
import numpy as np
# Set up voc_<year>_<split>
for year in ['2007', '2012']:
for split in ['train', 'val', 'trainval', 'test']:
name = 'voc_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: pascal_voc(split, year))
# Set up coco_2014_<split>
for year in ['2014']:
for split in ['train', 'val', 'minival', 'valminusminival', 'trainval']:
name = 'coco_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: coco(split, year))
# Set up coco_2015_<split>
for year in ['2015']:
for split in ['test', 'test-dev']:
name = 'COCO_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: coco(split, year))
dvmm_path='/vulcan/scratch/pengzhou/dataset/4cam_splc'
for split in ['dist_train', 'dist_test']:
name = split
__sets[name] = (lambda split=split: dvmm(split,2007,dvmm_path))
dso_path='/vulcan/scratch/pengzhou/dataset/COVERAGE'
for split in ['dist_cover_train_single', 'dist_cover_test_single']:
name = split
__sets[name] = (lambda split=split: dist_fake(split,2007,dso_path))
nist_path='/vulcan/scratch/pengzhou/dataset/NC2016_Test0613'
for split in ['dist_NIST_train_new_2', 'dist_NIST_test_new_2']:
name = split
__sets[name] = (lambda split=split: nist(split,2007,nist_path))
casia_path='/vulcan/scratch/pengzhou/dataset/CASIA2'
#for split in ['casia_train_all_single', 'casia_test_all_1']:
for split in ['casia_train_all_single', 'casia_test_all_single']:
name = split
__sets[name] = (lambda split=split: casia(split,2007,casia_path))
coco_path='/vulcan/scratch/pengzhou/dataset/filter_tamper'
for split in ['coco_train_filter_single', 'coco_test_filter_single']:
name = split
__sets[name] = (lambda split=split: coco(split,2007,coco_path))
swapme_path='/home-3/pengzhou@umd.edu/work/xintong/medifor/images/dataset_1k_final'
for split in ['face_faceswap_rcnn_train_only', 'face_faceswap_rcnn_test']:
name = split
__sets[name] = (lambda split=split: swapme(split,2007,swapme_path))
def get_imdb(name):
"""Get an imdb (image database) by name."""
if name not in __sets:
raise KeyError('Unknown dataset: {}'.format(name))
return __sets[name]()
def list_imdbs():
"""List all registered imdbs."""
return list(__sets.keys())
| 2,928 | 34.719512 | 83 | py |
RGB-N | RGB-N-master/lib/datasets/casia.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
from .voc_eval import voc_eval
from model.config import cfg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class casia(imdb):
def __init__(self, image_set, year, dist_path=None):
imdb.__init__(self, image_set)
self._year = year
self._image_set = image_set.split('casia_')[1]
self._dist_path = self._get_default_path() if dist_path is None \
else dist_path
self._data_path=self._dist_path
self._classes = ('__background__', # always index 0
'tamper','authentic')
self._classes = ('authentic', # always index 0
'tamper')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = {'.png','.jpg','.tif','.bmp','.JPG'}
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(os.path.splitext(self._image_index[i].split(' ')[0])[0])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
#image_path = os.path.join('/home-3/pengzhou@umd.edu/work/xintong/medifor/portrait/test_data',
#index + ext)
image_path = os.path.join(self._data_path,
index + ext)
image_path1=os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset/cocostuff/coco/train2014',
index + ext)
if os.path.isfile(image_path):
return image_path
elif os.path.isfile(image_path1):
return image_path1
else:
continue
assert os.path.isfile(image_path) and os.path.isfile(image_path1), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path,
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
#print(image_index)
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'CASIA1')
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self.roidb_gt(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def roidb_gt(self,image_id):
num_objs = int(len(image_id.split(' ')[1:])/5)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix in range(num_objs):
bbox = image_id.split(' ')[ix*5+1:ix*5+5]
# Make pixel indexes 0-based
x1 = float(bbox[0]) -1
y1 = float(bbox[1]) -1
x2 = float(bbox[2]) -1
y2 = float(bbox[3]) -1
if x1<0:
x1=0
if y1<0:
y1=0
try:
cls=self._class_to_ind[image_id.split(' ')[ix*5+5]]
except:
if int(image_id.split(' ')[ix*5+5])==0:
print('authentic')
#cls=2
else:
cls = int(image_id.split(' ')[ix*5+5])
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 ) * (y2 - y1)
#print(image_id)
#print(boxes)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'JPGed': False,
'noised': False,
'seg_areas': seg_areas}
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'JPGed':False,
'noised':False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'casia_' + self._image_set + '_{:s}.txt'
path = os.path.join(
'.',
filename)
return path
def _get_voc_noise_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'casia_' + self._image_set + '_{:s}_noise.txt'
path = os.path.join(
'.',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} VOC results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
print(filename)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index.split(' ')[0], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._dist_path,
'coco_multi' ,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._dist_path,
self._image_set + '.txt')
cachedir = os.path.join(self._dist_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
#use_07_metric = True if int(self._year) < 2010 else False
use_07_metric = False
print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__' or cls == self.classes[0]:
cls_ind=0
continue
else:
cls_ind=self._class_to_ind[cls]
#elif cls=='median_filtering':
#cls_ind=3
#continue
filename = self._get_voc_results_file_template().format(cls)
filename2 = self._get_voc_noise_results_file_template().format(cls)
print(cls_ind)
rec, prec, ap = voc_eval(
filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric,fuse=False)
aps += [ap]
print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
fig=plt.figure()
plt.plot(rec,prec)
fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
plt.xlabel('recall',fontsize=15)
plt.xlim((0,1.0))
plt.ylim((0,1.0))
plt.ylabel('precision',fontsize=15)
fig.savefig('{}.jpg'.format(cls))
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.casia import casia
d = casia('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
| 13,348 | 34.597333 | 105 | py |
RGB-N | RGB-N-master/lib/datasets/swapme.py | # --------------------------------------------------------
# Tensorflow RGB-N
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
from .voc_eval import voc_eval
from model.config import cfg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class swapme(imdb):
def __init__(self, image_set, year, dist_path=None):
imdb.__init__(self, image_set)
self._year = year
self._image_set = image_set.split('face_')[1]
self._dist_path = self._get_default_path() if dist_path is None \
else dist_path
self._data_path=self._dist_path
self._classes = ('__background__', # always index 0
'tamper','authentic')
#self._classes = ('authentic', # always index 0
#'tamper')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = {'.png','.jpg','.tif','.bmp','.JPG'}
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(os.path.splitext(self._image_index[i].split(' ')[0])[0])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
#image_path = os.path.join('/home-3/pengzhou@umd.edu/work/xintong/medifor/portrait/test_data',
#index + ext)
image_path = os.path.join(self._data_path,
index + ext)
image_path1=os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset/DATA2',
index.split('/')[1] + ext)
if os.path.isfile(image_path):
return image_path
elif os.path.isfile(image_path1):
return image_path1
else:
continue
assert os.path.isfile(image_path) and os.path.isfile(image_path1), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset',
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
#print(image_index)
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'swapme')
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self.roidb_gt(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def roidb_gt(self,image_id):
num_objs = int(len(image_id.split(' ')[1:])/5)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix in range(num_objs):
bbox = image_id.split(' ')[ix*5+1:ix*5+5]
# Make pixel indexes 0-based
x1 = float(bbox[0]) -1
y1 = float(bbox[1]) -1
x2 = float(bbox[2]) -1
y2 = float(bbox[3]) -1
if x1<0:
x1=0
if y1<0:
y1=0
try:
cls=self._class_to_ind[image_id.split(' ')[ix*5+5]]
except:
if int(image_id.split(' ')[ix*5+5])==0:
print('authentic')
cls=2
else:
cls = int(image_id.split(' ')[ix*5+5])
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 ) * (y2 - y1)
#print(image_id)
#print(boxes)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'casia_' + self._image_set + '_{:s}.txt'
path = os.path.join(
'.',
filename)
return path
def _get_voc_noise_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'casia_' + self._image_set + '_{:s}_noise.txt'
path = os.path.join(
'.',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} VOC results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
print(filename)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index.split(' ')[0], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
'/home-3/pengzhou@umd.edu/work/pengzhou/dataset',
'coco_multi' ,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
'/home-3/pengzhou@umd.edu/work/pengzhou/dataset',
self._image_set + '.txt')
cachedir = os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset', 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
#use_07_metric = True if int(self._year) < 2010 else False
use_07_metric = False
print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__' or cls == self.classes[0]:
cls_ind=0
continue
else:
cls_ind=self._class_to_ind[cls]
#elif cls=='median_filtering':
#cls_ind=3
#continue
filename = self._get_voc_results_file_template().format(cls)
filename2 = self._get_voc_noise_results_file_template().format(cls)
print(cls_ind)
rec, prec, ap = voc_eval(
filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric,fuse=False)
aps += [ap]
print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
fig=plt.figure()
plt.plot(rec,prec)
fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
plt.xlabel('recall',fontsize=15)
plt.xlim((0,1.0))
plt.ylim((0,1.0))
plt.ylabel('precision',fontsize=15)
fig.savefig('{}.jpg'.format(cls))
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.swapme import swapme
d = swapme('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
| 13,342 | 35.062162 | 104 | py |
RGB-N | RGB-N-master/lib/datasets/ds_utils.py | # --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def unique_boxes(boxes, scale=1.0):
"""Return indices of unique boxes."""
v = np.array([1, 1e3, 1e6, 1e9])
hashes = np.round(boxes * scale).dot(v)
_, index = np.unique(hashes, return_index=True)
return np.sort(index)
def xywh_to_xyxy(boxes):
"""Convert [x y w h] box format to [x1 y1 x2 y2] format."""
return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1))
def xyxy_to_xywh(boxes):
"""Convert [x1 y1 x2 y2] box format to [x y w h] format."""
return np.hstack((boxes[:, 0:2], boxes[:, 2:4] - boxes[:, 0:2] + 1))
def validate_boxes(boxes, width=0, height=0):
"""Check that a set of boxes are valid."""
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
assert (x1 >= 0).all()
assert (y1 >= 0).all()
assert (x2 >= x1).all()
assert (y2 >= y1).all()
assert (x2 < width).all()
assert (y2 < height).all()
def filter_small_boxes(boxes, min_size):
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]
keep = np.where((w >= min_size) & (h > min_size))[0]
return keep
| 1,402 | 27.06 | 70 | py |
RGB-N | RGB-N-master/lib/datasets/__init__.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
| 248 | 34.571429 | 58 | py |
RGB-N | RGB-N-master/lib/datasets/coco.py | # --------------------------------------------------------
# Tensorflow RGB-N
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou, based on the code of Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
from .voc_eval import voc_eval
from model.config import cfg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class coco(imdb):
def __init__(self, image_set, year, dist_path=None):
imdb.__init__(self, image_set)
self._year = year
self._image_set = image_set.split('coco_')[1]
self._dist_path = self._get_default_path() if dist_path is None \
else dist_path
self._data_path=self._dist_path
#self._data_path = os.path.join(self._dist_path, image_set)
self._classes = ('__background__', # always index 0
'tamper','authentic')
self._classes = ('authentic', # always index 0
'tamper')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
#self._image_ext = {'.jpg','.tif'}
self._image_ext = {'.png','.jpg','.tif','.bmp','.JPG'}
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(os.path.splitext(self._image_index[i].split(' ')[0])[0])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
#image_path = os.path.join('/home-3/pengzhou@umd.edu/work/xintong/medifor/portrait/test_data',
#index + ext)
image_path = os.path.join(self._data_path,
index + ext)
image_path1=os.path.join('../dataset/train2014',
index + ext)
if os.path.isfile(image_path):
return image_path
elif os.path.isfile(image_path1):
return image_path1
else:
continue
assert os.path.isfile(image_path) and os.path.isfile(image_path1), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path,
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
#return os.path.join(cfg.DATA_DIR, 'CASIA2')
return os.path.join(cfg.DATA_DIR, 'cocostuff/coco/splicing')
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self.roidb_gt(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def roidb_gt(self,image_id):
num_objs = int(len(image_id.split(' ')[1:])/5)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix in range(num_objs):
bbox = image_id.split(' ')[ix*5+1:ix*5+5]
#print(bbox)
# Make pixel indexes 0-based
x1 = float(bbox[0]) -1
y1 = float(bbox[1]) -1
x2 = float(bbox[2]) -1
y2 = float(bbox[3]) -1
if x1<0:
x1=0
if y1<0:
y1=0
try:
cls=self._class_to_ind[image_id.split(' ')[ix*5+5]]
except:
cls = int(image_id.split(' ')[ix*5+5])
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 ) * (y2 - y1)
#print(image_id)
#print(boxes)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'JPGed': False,
'noised':False,
'seg_areas': seg_areas}
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'coco_' + self._image_set + '_{:s}.txt'
path = os.path.join(
'.',
filename)
return path
def _get_voc_noise_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'coco_' + self._image_set + '_{:s}_noise.txt'
path = os.path.join(
'.',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} det results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
print(filename)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
#print(dets)
#print(index)
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index.split(' ')[0], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._dist_path,
'coco_multi' ,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._dist_path,
self._image_set + '.txt')
cachedir = os.path.join(self._dist_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
#use_07_metric = True if int(self._year) < 2010 else False
use_07_metric = False
print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__' or cls == self.classes[0]:
cls_ind=0
continue
else:
cls_ind=self._class_to_ind[cls]
#elif cls=='median_filtering':
#cls_ind=3
#continue
filename = self._get_voc_results_file_template().format(cls)
filename2 = self._get_voc_noise_results_file_template().format(cls)
#print(cls_ind)
rec, prec, ap = voc_eval(
filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric,fuse=False)
aps += [ap]
print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
fig=plt.figure()
plt.plot(rec,prec)
fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
plt.xlabel('recall',fontsize=15)
plt.xlim((0,1.0))
plt.ylim((0,1.0))
plt.ylabel('precision',fontsize=15)
fig.savefig('{}.png'.format(cls))
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._dist_path,
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__' or cls == self.classes[0]:
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.coco import coco
d = coco('train', '2007')
res = d.roidb
from IPython import embed;
embed()
| 13,334 | 34.65508 | 104 | py |
RGB-N | RGB-N-master/lib/datasets/nist.py | # --------------------------------------------------------
# Tensorflow RGB-N
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
import pdb
from .voc_eval import voc_eval
from model.config import cfg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class nist(imdb):
def __init__(self, image_set, year, dist_path=None):
imdb.__init__(self, image_set)
self._year = year
self._image_set = image_set.split('dist_')[1]
self._dist_path = self._get_default_path() if dist_path is None \
else dist_path
self._data_path=self._dist_path
self._classes = ('__background__', # always index 0
'tamper','authentic')
self._classes = ('__background__', # always index 0
'splicing','removal','manipulation')
self._classes = ('authentic', # always index 0
'tamper')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = {'.png','.jpg','.tif','.bmp','.JPG'}
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(os.path.splitext(self._image_index[i].split(' ')[0])[0])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
#image_path = os.path.join('/home-3/pengzhou@umd.edu/work/xintong/medifor/portrait/test_data',
#index + ext)
image_path = os.path.join(self._data_path,
index + ext)
image_path1=os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset/NC2016_Test0613',
index + ext)
if os.path.isfile(image_path):
return image_path
elif os.path.isfile(image_path1):
return image_path1
else:
continue
assert os.path.isfile(image_path) and os.path.isfile(image_path1), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path,
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
#print(image_index)
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'NC2016_Test0613')
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self.roidb_gt(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def roidb_gt(self,image_id):
num_objs = int(len(image_id.split(' ')[1:])/5)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix in range(num_objs):
bbox = image_id.split(' ')[ix*5+1:ix*5+5]
# Make pixel indexes 0-based
x1 = float(bbox[0])
y1 = float(bbox[1])
x2 = float(bbox[2])
y2 = float(bbox[3])
if x1<0:
x1=0
if y1<0:
y1=0
try:
cls=self._class_to_ind[image_id.split(' ')[ix*5+5]]
except:
if int(image_id.split(' ')[ix*5+5])==0:
print('authentic')
cls=2
else:
cls = int(image_id.split(' ')[ix*5+5])
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 ) * (y2 - y1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'JPGed': False,
'noised':False,
'seg_areas': seg_areas}
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'nist_' + self._image_set + '_{:s}.txt'
path = os.path.join(
'.',
filename)
return path
def _get_voc_noise_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'nist_' + self._image_set + '_{:s}_noise.txt'
path = os.path.join(
'.',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__' or cls == self.classes[0]:
continue
print('Writing {} VOC results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
print(filename)
with open(filename, 'w') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
#pdb.set_trace()
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(index.split(' ')[0], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
#pdb.set_trace()
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._dist_path,
'coco_multi' ,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._dist_path,
self._image_set + '.txt')
cachedir = os.path.join(self._dist_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
#use_07_metric = True if int(self._year) < 2010 else False
use_07_metric = False
print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__' or cls == self.classes[0]:
cls_ind=0
continue
else:
cls_ind=self._class_to_ind[cls]
#elif cls=='median_filtering':
#cls_ind=3
#continue
filename = self._get_voc_results_file_template().format(cls)
filename2 = self._get_voc_noise_results_file_template().format(cls)
print(cls_ind)
rec, prec, ap = voc_eval(
filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric,fuse=False)
aps += [ap]
print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
fig=plt.figure()
plt.plot(rec,prec)
fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
plt.xlabel('recall',fontsize=15)
plt.xlim((0,1.0))
plt.ylim((0,1.0))
plt.ylabel('precision',fontsize=15)
fig.savefig('{}.png'.format(cls))
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__' or cls == self.classes[0]:
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.dist_fake import dist_fake
d = nist('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
| 13,431 | 34.818667 | 104 | py |
RGB-N | RGB-N-master/lib/datasets/dvmm.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import pickle
import subprocess
import uuid
import pdb
from .voc_eval import voc_eval
from model.config import cfg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class dvmm(imdb):
def __init__(self, image_set, year, dist_path=None):
imdb.__init__(self, image_set)
self._year = year
self._image_set = image_set.split('dist_')[1]
self._dist_path = self._get_default_path() if dist_path is None \
else dist_path
self._data_path=self._dist_path
self._classes = ('__background__', # always index 0
'tamper','authentic')
self._classes = ('authentic', # always index 0
'tamper')
#self.classes =('authentic', # always index 0
#'splicing','removal')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = {'.png','.jpg','.tif','.bmp','.JPG'}
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(os.path.splitext(self._image_index[i].split(' ')[0])[0])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
for ext in self._image_ext:
#image_path = os.path.join('/home-3/pengzhou@umd.edu/work/xintong/medifor/portrait/test_data',
#index + ext)
image_path = os.path.join(self._data_path,
index + ext)
image_path1=os.path.join('/home-3/pengzhou@umd.edu/work/pengzhou/dataset/NC2016_Test0613',
index + ext)
if os.path.isfile(image_path):
return image_path
elif os.path.isfile(image_path1):
return image_path1
else:
continue
assert os.path.isfile(image_path) and os.path.isfile(image_path1), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path,
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
#print(image_index)
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'NC2016_Test0613')
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
try:
roidb = pickle.load(fid)
except:
roidb = pickle.load(fid, encoding='bytes')
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [self.roidb_gt(index)
for index in self.image_index]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def rpn_roidb(self):
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
rpn_roidb = self._load_rpn_roidb(gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)
else:
roidb = self._load_rpn_roidb(None)
return roidb
def roidb_gt(self,image_id):
num_objs = int(len(image_id.split(' ')[1:])/5)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix in range(num_objs):
bbox = image_id.split(' ')[ix*5+1:ix*5+5]
# Make pixel indexes 0-based
x1 = float(bbox[0])
y1 = float(bbox[1])
x2 = float(bbox[2])
y2 = float(bbox[3])
if x1<0:
x1=0
if y1<0:
y1=0
try:
cls=self._class_to_ind[image_id.split(' ')[ix*5+5]]
except:
if int(image_id.split(' ')[ix*5+5])==0:
print('authentic')
#cls=2
else:
cls = int(image_id.split(' ')[ix*5+5])
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 ) * (y2 - y1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _load_rpn_roidb(self, gt_roidb):
filename = self.config['rpn_file']
print('loading {}'.format(filename))
assert os.path.exists(filename), \
'rpn data not found at: {}'.format(filename)
with open(filename, 'rb') as f:
box_list = pickle.load(f)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
if not self.config['use_diff']:
# Exclude the samples labeled as difficult
non_diff_objs = [
obj for obj in objs if int(obj.find('difficult').text) == 0]
# if len(non_diff_objs) != len(objs):
# print 'Removed {} difficult objects'.format(
# len(objs) - len(non_diff_objs))
objs = non_diff_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
# "Seg" area for pascal is just the box area
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
bbox = obj.find('bndbox')
# Make pixel indexes 0-based
x1 = float(bbox.find('xmin').text) - 1
y1 = float(bbox.find('ymin').text) - 1
x2 = float(bbox.find('xmax').text) - 1
y2 = float(bbox.find('ymax').text) - 1
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas}
def _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'dvmm_' + self._image_set + '_{:s}.txt'
path = os.path.join(
'.',
filename)
return path
def _get_voc_noise_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = 'dvmm_' + self._image_set + '_{:s}_noise.txt'
path = os.path.join(
'.',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} VOC results file'.format(cls))
filename = self._get_voc_results_file_template().format(cls)
print(filename)
with open(filename, 'w') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
#pdb.set_trace()
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(index.split(' ')[0], dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
#pdb.set_trace()
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._dist_path,
'coco_multi' ,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._dist_path,
self._image_set + '.txt')
cachedir = os.path.join(self._dist_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
#use_07_metric = True if int(self._year) < 2010 else False
use_07_metric = False
print('dist metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__' or cls == self.classes[0]:
cls_ind=0
continue
else:
cls_ind=self._class_to_ind[cls]
#elif cls=='median_filtering':
#cls_ind=3
#continue
filename = self._get_voc_results_file_template().format(cls)
filename2 = self._get_voc_noise_results_file_template().format(cls)
print(cls_ind)
rec, prec, ap = voc_eval(
filename,filename2, annopath, imagesetfile, cls_ind, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric,fuse=False)
aps += [ap]
print(('AP for {} = {:.4f},recall = {:.4f}, precision = {:.4f}'.format(cls, ap,rec[-1],prec[-1])))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
fig=plt.figure()
plt.plot(rec,prec)
fig.suptitle('PR curve for {} detection'.format(cls),fontsize=20)
plt.xlabel('recall',fontsize=15)
plt.xlim((0,1.0))
plt.ylim((0,1.0))
plt.ylabel('precision',fontsize=15)
fig.savefig('{}.jpg'.format(cls))
print(('Mean AP = {:.4f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print(('{:.3f}'.format(ap)))
print(('{:.3f}'.format(np.mean(aps))))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print('-----------------------------------------------------')
print('Computing results with the official MATLAB eval code.')
print('-----------------------------------------------------')
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print(('Running:\n{}'.format(cmd)))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
#if self.config['matlab_eval']:
#self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
#os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.dvmm import dvmm
d = dvmm('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
| 13,318 | 34.612299 | 104 | py |
RGB-N | RGB-N-master/lib/datasets/tools/mcg_munge.py | import os
import sys
"""Hacky tool to convert file system layout of MCG boxes downloaded from
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/
so that it's consistent with those computed by Jan Hosang (see:
http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-
computing/research/object-recognition-and-scene-understanding/how-
good-are-detection-proposals-really/)
NB: Boxes from the MCG website are in (y1, x1, y2, x2) order.
Boxes from Hosang et al. are in (x1, y1, x2, y2) order.
"""
def munge(src_dir):
# stored as: ./MCG-COCO-val2014-boxes/COCO_val2014_000000193401.mat
# want: ./MCG/mat/COCO_val2014_0/COCO_val2014_000000141/COCO_val2014_000000141334.mat
files = os.listdir(src_dir)
for fn in files:
base, ext = os.path.splitext(fn)
# first 14 chars / first 22 chars / all chars + .mat
# COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat
first = base[:14]
second = base[:22]
dst_dir = os.path.join('MCG', 'mat', first, second)
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
src = os.path.join(src_dir, fn)
dst = os.path.join(dst_dir, fn)
print 'MV: {} -> {}'.format(src, dst)
os.rename(src, dst)
if __name__ == '__main__':
# src_dir should look something like:
# src_dir = 'MCG-COCO-val2014-boxes'
src_dir = sys.argv[1]
munge(src_dir)
| 1,451 | 36.230769 | 94 | py |
RGB-N | RGB-N-master/lib/layer_utils/proposal_layer.py | # --------------------------------------------------------
# Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from model.config import cfg
from model.bbox_transform import bbox_transform_inv, clip_boxes
from model.nms_wrapper import nms
def proposal_layer(rpn_cls_prob, rpn_bbox_pred, im_info, cfg_key, _feat_stride, anchors, num_anchors):
"""A simplified version compared to fast/er RCNN
For details please see the technical report
"""
if type(cfg_key) == bytes:
cfg_key = cfg_key.decode('utf-8')
pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
im_info = im_info[0]
# Get the scores and bounding boxes
scores = rpn_cls_prob[:, :, :, num_anchors:]
rpn_bbox_pred = rpn_bbox_pred.reshape((-1, 4))
scores = scores.reshape((-1, 1))
proposals = bbox_transform_inv(anchors, rpn_bbox_pred)
proposals = clip_boxes(proposals, im_info[:2])
# Pick the top region proposals
order = scores.ravel().argsort()[::-1]
if pre_nms_topN > 0:
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores = scores[order]
# Non-maximal suppression
keep = nms(np.hstack((proposals, scores)), nms_thresh)
# Pick th top region proposals after NMS
if post_nms_topN > 0:
keep = keep[:post_nms_topN]
proposals = proposals[keep, :]
scores = scores[keep]
# Only support single image as input
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
return blob, scores
| 1,850 | 32.654545 | 102 | py |
RGB-N | RGB-N-master/lib/layer_utils/proposal_top_layer.py | # --------------------------------------------------------
# Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from model.config import cfg
from model.bbox_transform import bbox_transform_inv, clip_boxes
import numpy.random as npr
def proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, im_info, _feat_stride, anchors, num_anchors):
"""A layer that just selects the top region proposals
without using non-maximal suppression,
For details please see the technical report
"""
rpn_top_n = cfg.TEST.RPN_TOP_N
im_info = im_info[0]
scores = rpn_cls_prob[:, :, :, num_anchors:]
rpn_bbox_pred = rpn_bbox_pred.reshape((-1, 4))
scores = scores.reshape((-1, 1))
length = scores.shape[0]
if length < rpn_top_n:
# Random selection, maybe unnecessary and loses good proposals
# But such case rarely happens
top_inds = npr.choice(length, size=rpn_top_n, replace=True)
else:
top_inds = scores.argsort(0)[::-1]
top_inds = top_inds[:rpn_top_n]
top_inds = top_inds.reshape(rpn_top_n, )
# Do the selection here
anchors = anchors[top_inds, :]
rpn_bbox_pred = rpn_bbox_pred[top_inds, :]
scores = scores[top_inds]
# Convert anchors into proposals via bbox transformations
proposals = bbox_transform_inv(anchors, rpn_bbox_pred)
# Clip predicted boxes to image
proposals = clip_boxes(proposals, im_info[:2])
# Output rois blob
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
return blob, scores
| 1,868 | 32.981818 | 97 | py |
RGB-N | RGB-N-master/lib/layer_utils/generate_anchors.py | # --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
# Verify that we compute the same anchors as Shaoqing's matlab implementation:
#
# >> load output/rpn_cachedir/faster_rcnn_VOC2007_ZF_stage1_rpn/anchors.mat
# >> anchors
#
# anchors =
#
# -83 -39 100 56
# -175 -87 192 104
# -359 -183 376 200
# -55 -55 72 72
# -119 -119 136 136
# -247 -247 264 264
# -35 -79 52 96
# -79 -167 96 184
# -167 -343 184 360
# array([[ -83., -39., 100., 56.],
# [-175., -87., 192., 104.],
# [-359., -183., 376., 200.],
# [ -55., -55., 72., 72.],
# [-119., -119., 136., 136.],
# [-247., -247., 264., 264.],
# [ -35., -79., 52., 96.],
# [ -79., -167., 96., 184.],
# [-167., -343., 184., 360.]])
def generate_anchors(base_size=16, ratios=[0.5, 1, 2],
scales=2 ** np.arange(3, 6)):
"""
Generate anchor (reference) windows by enumerating aspect ratios X
scales wrt a reference (0, 0, 15, 15) window.
"""
base_anchor = np.array([1, 1, base_size, base_size]) - 1
ratio_anchors = _ratio_enum(base_anchor, ratios)
anchors = np.vstack([_scale_enum(ratio_anchors[i, :], scales)
for i in range(ratio_anchors.shape[0])])
return anchors
def _whctrs(anchor):
"""
Return width, height, x center, and y center for an anchor (window).
"""
w = anchor[2] - anchor[0] + 1
h = anchor[3] - anchor[1] + 1
x_ctr = anchor[0] + 0.5 * (w - 1)
y_ctr = anchor[1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def _mkanchors(ws, hs, x_ctr, y_ctr):
"""
Given a vector of widths (ws) and heights (hs) around a center
(x_ctr, y_ctr), output a set of anchors (windows).
"""
ws = ws[:, np.newaxis]
hs = hs[:, np.newaxis]
anchors = np.hstack((x_ctr - 0.5 * (ws - 1),
y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1),
y_ctr + 0.5 * (hs - 1)))
return anchors
def _ratio_enum(anchor, ratios):
"""
Enumerate a set of anchors for each aspect ratio wrt an anchor.
"""
w, h, x_ctr, y_ctr = _whctrs(anchor)
size = w * h
size_ratios = size / ratios
ws = np.round(np.sqrt(size_ratios))
hs = np.round(ws * ratios)
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
def _scale_enum(anchor, scales):
"""
Enumerate a set of anchors for each scale wrt an anchor.
"""
w, h, x_ctr, y_ctr = _whctrs(anchor)
ws = w * scales
hs = h * scales
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
if __name__ == '__main__':
import time
t = time.time()
a = generate_anchors()
print(time.time() - t)
print(a)
from IPython import embed;
embed()
| 3,129 | 25.525424 | 78 | py |
RGB-N | RGB-N-master/lib/layer_utils/proposal_target_layer.py | # --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick, Sean Bell and Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import numpy.random as npr
from model.config import cfg
from model.bbox_transform import bbox_transform
from utils.cython_bbox import bbox_overlaps
def proposal_target_layer(rpn_rois, rpn_scores, gt_boxes, _num_classes):
"""
Assign object detection proposals to ground-truth targets. Produces proposal
classification labels and bounding-box regression targets.
"""
# Proposal ROIs (0, x1, y1, x2, y2) coming from RPN
# (i.e., rpn.proposal_layer.ProposalLayer), or any other source
all_rois = rpn_rois
all_scores = rpn_scores
# Include ground-truth boxes in the set of candidate rois
if cfg.TRAIN.USE_GT:
zeros = np.zeros((gt_boxes.shape[0], 1), dtype=gt_boxes.dtype)
all_rois = np.vstack(
(all_rois, np.hstack((zeros, gt_boxes[:, :-1])))
)
# not sure if it a wise appending, but anyway i am not using it
all_scores = np.vstack((all_scores, zeros))
num_images = 1
rois_per_image = cfg.TRAIN.BATCH_SIZE / num_images
fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image)
# Sample rois with classification labels and bounding box regression
# targets
labels, rois, roi_scores, bbox_targets, bbox_inside_weights = _sample_rois(
all_rois, all_scores, gt_boxes, fg_rois_per_image,
rois_per_image, _num_classes)
rois = rois.reshape(-1, 5)
roi_scores = roi_scores.reshape(-1)
labels = labels.reshape(-1, 1)
bbox_targets = bbox_targets.reshape(-1, _num_classes * 4)
bbox_inside_weights = bbox_inside_weights.reshape(-1, _num_classes * 4)
bbox_outside_weights = np.array(bbox_inside_weights > 0).astype(np.float32)
return rois, roi_scores, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights
def _get_bbox_regression_labels(bbox_target_data, num_classes):
"""Bounding-box regression targets (bbox_target_data) are stored in a
compact form N x (class, tx, ty, tw, th)
This function expands those targets into the 4-of-4*K representation used
by the network (i.e. only one class has non-zero targets).
Returns:
bbox_target (ndarray): N x 4K blob of regression targets
bbox_inside_weights (ndarray): N x 4K blob of loss weights
"""
clss = bbox_target_data[:, 0]
bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32)
bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
if num_classes<=2:
inds = np.where(clss > 0)[0]
else:
inds = np.where(clss > 0)[0]
for ind in inds:
cls = clss[ind]
start = int(4 * cls)
end = start + 4
bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
return bbox_targets, bbox_inside_weights
def _compute_targets(ex_rois, gt_rois, labels):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 4
targets = bbox_transform(ex_rois, gt_rois)
if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
# Optionally normalize targets by a precomputed mean and stdev
targets = ((targets - np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS))
/ np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS))
return np.hstack(
(labels[:, np.newaxis], targets)).astype(np.float32, copy=False)
def _sample_rois(all_rois, all_scores, gt_boxes, fg_rois_per_image, rois_per_image, num_classes):
"""Generate a random sample of RoIs comprising foreground and background
examples.
"""
# overlaps: (rois x gt_boxes)
overlaps = bbox_overlaps(
np.ascontiguousarray(all_rois[:, 1:5], dtype=np.float),
np.ascontiguousarray(gt_boxes[:, :4], dtype=np.float))
gt_assignment = overlaps.argmax(axis=1)
max_overlaps = overlaps.max(axis=1)
labels = gt_boxes[gt_assignment, 4]
# Select foreground RoIs as those with >= FG_THRESH overlap
fg_inds = np.where(max_overlaps >= cfg.TRAIN.FG_THRESH)[0]
# Guard against the case when an image has fewer than fg_rois_per_image
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_inds = np.where((max_overlaps < cfg.TRAIN.BG_THRESH_HI) &
(max_overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
# Small modification to the original version where we ensure a fixed number of regions are sampled
if fg_inds.size > 0 and bg_inds.size > 0:
fg_rois_per_image = min(fg_rois_per_image, fg_inds.size)
fg_inds = npr.choice(fg_inds, size=int(fg_rois_per_image), replace=False)
bg_rois_per_image = rois_per_image - fg_rois_per_image
to_replace = bg_inds.size < bg_rois_per_image
bg_inds = npr.choice(bg_inds, size=int(bg_rois_per_image), replace=to_replace)
elif fg_inds.size > 0:
to_replace = fg_inds.size < rois_per_image
fg_inds = npr.choice(fg_inds, size=int(rois_per_image), replace=to_replace)
fg_rois_per_image = rois_per_image
elif bg_inds.size > 0:
to_replace = bg_inds.size < rois_per_image
bg_inds = npr.choice(bg_inds, size=int(rois_per_image), replace=to_replace)
fg_rois_per_image = 0
else:
import pdb
pdb.set_trace()
# The indices that we're selecting (both fg and bg)
keep_inds = np.append(fg_inds, bg_inds)
# Select sampled values from various arrays:
labels = labels[keep_inds]
# Clamp labels for the background RoIs to 0
labels[int(fg_rois_per_image):] = 0
rois = all_rois[keep_inds]
roi_scores = all_scores[keep_inds]
bbox_target_data = _compute_targets(
rois[:, 1:5], gt_boxes[gt_assignment[keep_inds], :4], labels)
bbox_targets, bbox_inside_weights = \
_get_bbox_regression_labels(bbox_target_data, num_classes)
return labels, rois, roi_scores, bbox_targets, bbox_inside_weights
| 6,081 | 37.987179 | 100 | py |
RGB-N | RGB-N-master/lib/layer_utils/snippets.py | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import numpy.random as npr
from model.config import cfg
from layer_utils.generate_anchors import generate_anchors
from model.bbox_transform import bbox_transform_inv, clip_boxes
from utils.cython_bbox import bbox_overlaps
def generate_anchors_pre(height, width, feat_stride, anchor_scales=(8,16,32), anchor_ratios=(0.5,1,2)):
""" A wrapper function to generate anchors given different scales
Also return the number of anchors in variable 'length'
"""
anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
A = anchors.shape[0]
shift_x = np.arange(0, width) * feat_stride
shift_y = np.arange(0, height) * feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
K = shifts.shape[0]
# width changes faster, so here it is H, W, C
anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False)
length = np.int32(anchors.shape[0])
return anchors, length
| 1,473 | 42.352941 | 103 | py |
RGB-N | RGB-N-master/lib/layer_utils/anchor_target_layer.py | # --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from model.config import cfg
import numpy as np
import numpy.random as npr
from utils.cython_bbox import bbox_overlaps
from model.bbox_transform import bbox_transform
def anchor_target_layer(rpn_cls_score, gt_boxes, im_info, _feat_stride, all_anchors, num_anchors):
"""Same as the anchor target layer in original Fast/er RCNN """
A = num_anchors
total_anchors = all_anchors.shape[0]
K = total_anchors / num_anchors
im_info = im_info[0]
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
# map of shape (..., H, W)
height, width = rpn_cls_score.shape[1:3]
# only keep anchors inside the image
inds_inside = np.where(
(all_anchors[:, 0] >= -_allowed_border) &
(all_anchors[:, 1] >= -_allowed_border) &
(all_anchors[:, 2] < im_info[1] + _allowed_border) & # width
(all_anchors[:, 3] < im_info[0] + _allowed_border) # height
)[0]
# keep only inside anchors
anchors = all_anchors[inds_inside, :]
# label: 1 is positive, 0 is negative, -1 is dont care
labels = np.empty((len(inds_inside),), dtype=np.float32)
labels.fill(-1)
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt)
overlaps = bbox_overlaps(
np.ascontiguousarray(anchors, dtype=np.float),
np.ascontiguousarray(gt_boxes, dtype=np.float))
argmax_overlaps = overlaps.argmax(axis=1)
max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
gt_argmax_overlaps = overlaps.argmax(axis=0)
gt_max_overlaps = overlaps[gt_argmax_overlaps,
np.arange(overlaps.shape[1])]
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
# first set the negatives
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1
# fg label: above threshold IOU
labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1
if cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels last so that negative labels can clobber positives
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
# subsample positive labels if we have too many
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
fg_inds = np.where(labels == 1)[0]
if len(fg_inds) > num_fg:
disable_inds = npr.choice(
fg_inds, size=(len(fg_inds) - num_fg), replace=False)
labels[disable_inds] = -1
# subsample negative labels if we have too many
num_bg = cfg.TRAIN.RPN_BATCHSIZE - np.sum(labels == 1)
bg_inds = np.where(labels == 0)[0]
if len(bg_inds) > num_bg:
disable_inds = npr.choice(
bg_inds, size=(len(bg_inds) - num_bg), replace=False)
labels[disable_inds] = -1
bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])
bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
# only the positive ones have regression targets
bbox_inside_weights[labels == 1, :] = np.array(cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS)
bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0:
# uniform weighting of examples (given non-uniform sampling)
num_examples = np.sum(labels >= 0)
positive_weights = np.ones((1, 4)) * 1.0 / num_examples
negative_weights = np.ones((1, 4)) * 1.0 / num_examples
else:
assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) &
(cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1))
positive_weights = (cfg.TRAIN.RPN_POSITIVE_WEIGHT /
np.sum(labels == 1))
negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) /
np.sum(labels == 0))
bbox_outside_weights[labels == 1, :] = positive_weights
bbox_outside_weights[labels == 0, :] = negative_weights
# map up to original set of anchors
labels = _unmap(labels, total_anchors, inds_inside, fill=-1)
bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)
bbox_inside_weights = _unmap(bbox_inside_weights, total_anchors, inds_inside, fill=0)
bbox_outside_weights = _unmap(bbox_outside_weights, total_anchors, inds_inside, fill=0)
# labels
labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2)
labels = labels.reshape((1, 1, A * height, width))
rpn_labels = labels
# bbox_targets
bbox_targets = bbox_targets \
.reshape((1, height, width, A * 4))
rpn_bbox_targets = bbox_targets
# bbox_inside_weights
bbox_inside_weights = bbox_inside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_inside_weights = bbox_inside_weights
# bbox_outside_weights
bbox_outside_weights = bbox_outside_weights \
.reshape((1, height, width, A * 4))
rpn_bbox_outside_weights = bbox_outside_weights
return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights
def _unmap(data, count, inds, fill=0):
""" Unmap a subset of item (data) back to the original set of items (of
size count) """
if len(data.shape) == 1:
ret = np.empty((count,), dtype=np.float32)
ret.fill(fill)
ret[inds] = data
else:
ret = np.empty((count,) + data.shape[1:], dtype=np.float32)
ret.fill(fill)
ret[inds, :] = data
return ret
def _compute_targets(ex_rois, gt_rois):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 5
return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False)
| 6,031 | 35.780488 | 98 | py |
RGB-N | RGB-N-master/lib/layer_utils/__init__.py | 0 | 0 | 0 | py |
|
RGB-N | RGB-N-master/lib/utils/nms.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import numpy as np
def nms(dets, thresh):
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
| 1,008 | 25.552632 | 59 | py |
RGB-N | RGB-N-master/lib/utils/timer.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import time
class Timer(object):
"""A simple timer."""
def __init__(self):
self.total_time = 0.
self.calls = 0
self.start_time = 0.
self.diff = 0.
self.average_time = 0.
def tic(self):
# using time.time instead of time.clock because time time.clock
# does not normalize for multithreading
self.start_time = time.time()
def toc(self, average=True):
self.diff = time.time() - self.start_time
self.total_time += self.diff
self.calls += 1
self.average_time = self.total_time / self.calls
if average:
return self.average_time
else:
return self.diff
| 948 | 27.757576 | 71 | py |
RGB-N | RGB-N-master/lib/utils/boxes_grid.py | # --------------------------------------------------------
# Subcategory CNN
# Copyright (c) 2015 CVGL Stanford
# Licensed under The MIT License [see LICENSE for details]
# Written by Yu Xiang
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import math
from model.config import cfg
def get_boxes_grid(image_height, image_width):
"""
Return the boxes on image grid.
"""
# height and width of the heatmap
if cfg.NET_NAME == 'CaffeNet':
height = np.floor((image_height * max(cfg.TRAIN.SCALES) - 1) / 4.0 + 1)
height = np.floor((height - 1) / 2.0 + 1 + 0.5)
height = np.floor((height - 1) / 2.0 + 1 + 0.5)
width = np.floor((image_width * max(cfg.TRAIN.SCALES) - 1) / 4.0 + 1)
width = np.floor((width - 1) / 2.0 + 1 + 0.5)
width = np.floor((width - 1) / 2.0 + 1 + 0.5)
elif cfg.NET_NAME == 'VGGnet':
height = np.floor(image_height * max(cfg.TRAIN.SCALES) / 2.0 + 0.5)
height = np.floor(height / 2.0 + 0.5)
height = np.floor(height / 2.0 + 0.5)
height = np.floor(height / 2.0 + 0.5)
width = np.floor(image_width * max(cfg.TRAIN.SCALES) / 2.0 + 0.5)
width = np.floor(width / 2.0 + 0.5)
width = np.floor(width / 2.0 + 0.5)
width = np.floor(width / 2.0 + 0.5)
else:
assert (1), 'The network architecture is not supported in utils.get_boxes_grid!'
# compute the grid box centers
h = np.arange(height)
w = np.arange(width)
y, x = np.meshgrid(h, w, indexing='ij')
centers = np.dstack((x, y))
centers = np.reshape(centers, (-1, 2))
num = centers.shape[0]
# compute width and height of grid box
area = cfg.TRAIN.KERNEL_SIZE * cfg.TRAIN.KERNEL_SIZE
aspect = cfg.TRAIN.ASPECTS # height / width
num_aspect = len(aspect)
widths = np.zeros((1, num_aspect), dtype=np.float32)
heights = np.zeros((1, num_aspect), dtype=np.float32)
for i in range(num_aspect):
widths[0, i] = math.sqrt(area / aspect[i])
heights[0, i] = widths[0, i] * aspect[i]
# construct grid boxes
centers = np.repeat(centers, num_aspect, axis=0)
widths = np.tile(widths, num).transpose()
heights = np.tile(heights, num).transpose()
x1 = np.reshape(centers[:, 0], (-1, 1)) - widths * 0.5
x2 = np.reshape(centers[:, 0], (-1, 1)) + widths * 0.5
y1 = np.reshape(centers[:, 1], (-1, 1)) - heights * 0.5
y2 = np.reshape(centers[:, 1], (-1, 1)) + heights * 0.5
boxes_grid = np.hstack((x1, y1, x2, y2)) / cfg.TRAIN.SPATIAL_SCALE
return boxes_grid, centers[:, 0], centers[:, 1]
| 2,599 | 34.135135 | 84 | py |
RGB-N | RGB-N-master/lib/utils/blob.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Blob helper functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import cv2
def im_list_to_blob(ims):
"""Convert a list of images into a network input.
Assumes images are already prepared (means subtracted, BGR order, ...).
"""
max_shape = np.array([im.shape for im in ims]).max(axis=0)
num_images = len(ims)
blob = np.zeros((num_images, max_shape[0], max_shape[1], 3),
dtype=np.float32)
for i in range(num_images):
im = ims[i]
blob[i, 0:im.shape[0], 0:im.shape[1], :] = im
return blob
def prep_im_for_blob(im, pixel_means, target_size, max_size):
"""Mean subtract and scale an image for use in a blob."""
im = im.astype(np.float32, copy=False)
im -= pixel_means
im_shape = im.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > max_size:
im_scale = float(max_size) / float(im_size_max)
im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
return im, im_scale
def prep_noise_for_blob(im, pixel_means, target_size, max_size):
"""Mean subtract and scale an image for use in a blob."""
im = im.astype(np.float32, copy=False)
im -= pixel_means
im_shape = im.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > max_size:
im_scale = float(max_size) / float(im_size_max)
im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
return im, im_scale
| 2,135 | 32.904762 | 73 | py |
RGB-N | RGB-N-master/lib/utils/__init__.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
| 248 | 34.571429 | 58 | py |
RGB-N | RGB-N-master/lib/model/test.py | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import cv2
try:
import cPickle as pickle
except ImportError:
import pickle
import os
import math
from utils.timer import Timer
from utils.cython_nms import nms, nms_new
from utils.boxes_grid import get_boxes_grid
from utils.blob import im_list_to_blob
import pdb
from model.config import cfg, get_output_dir
from model.bbox_transform import clip_boxes, bbox_transform_inv
def _get_image_blob(im):
"""Converts an image into a network input.
Arguments:
im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
in the image pyramid
"""
row,col,ch= im.shape
mean = 0
var = 10
sigma = var**0.5
gauss = np.random.normal(mean,sigma,(row,col,ch))
gauss = gauss.reshape(row,col,ch)
# jpeg
#cv2.imwrite('b.jpg',im,[cv2.IMWRITE_JPEG_QUALITY, 70])
#pdb.set_trace()
#im=cv2.imread('b.jpg')
im_orig = im.astype(np.float32, copy=True)
#im_orig = im_orig + gauss
im_orig -= cfg.PIXEL_MEANS
im_shape = im_orig.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
processed_ims = []
processed_noise = []
im_scale_factors = []
for target_size in cfg.TEST.SCALES:
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
noise=im
im_scale_factors.append(im_scale)
processed_ims.append(im)
processed_noise.append(noise)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
noise_blob = im_list_to_blob(processed_noise)
return blob,noise_blob, np.array(im_scale_factors)
def _get_blobs(im):
"""Convert an image and RoIs within that image into network inputs."""
blobs = {}
blobs['data'],blobs['noise'], im_scale_factors = _get_image_blob(im)
return blobs, im_scale_factors
def _clip_boxes(boxes, im_shape):
"""Clip boxes to image boundaries."""
# x1 >= 0
boxes[:, 0::4] = np.maximum(boxes[:, 0::4], 0)
# y1 >= 0
boxes[:, 1::4] = np.maximum(boxes[:, 1::4], 0)
# x2 < im_shape[1]
boxes[:, 2::4] = np.minimum(boxes[:, 2::4], im_shape[1] - 1)
# y2 < im_shape[0]
boxes[:, 3::4] = np.minimum(boxes[:, 3::4], im_shape[0] - 1)
return boxes
def _rescale_boxes(boxes, inds, scales):
"""Rescale boxes according to image rescaling."""
for i in range(boxes.shape[0]):
boxes[i,:] = boxes[i,:] / scales[int(inds[i])]
return boxes
def im_detect(sess, net, im):
blobs, im_scales = _get_blobs(im)
assert len(im_scales) == 1, "Only single-image batch implemented"
im_blob = blobs['data']
# seems to have height, width, and image scales
# still not sure about the scale, maybe full image it is 1.
blobs['im_info'] = np.array([[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], dtype=np.float32)
try:
scores1, scores, bbox_pred, rois,feat,s = net.test_image(sess, blobs['data'], blobs['im_info'])
except:
scores1, scores, bbox_pred, rois,feat,s = net.test_image(sess, blobs['data'],blobs['noise'], blobs['im_info'])
boxes = rois[:, 1:5] / im_scales[0]
# print(scores.shape, bbox_pred.shape, rois.shape, boxes.shape)
scores = np.reshape(scores, [scores.shape[0], -1])
bbox_pred = np.reshape(bbox_pred, [bbox_pred.shape[0], -1])
if cfg.TEST.BBOX_REG:
# Apply bounding-box regression deltas
box_deltas = bbox_pred
pred_boxes = bbox_transform_inv(boxes, box_deltas)
pred_boxes = _clip_boxes(pred_boxes, im.shape)
else:
# Simply repeat the boxes, once for each class
pred_boxes = np.tile(boxes, (1, scores.shape[1]))
return scores, pred_boxes,feat,s
def apply_nms(all_boxes, thresh):
"""Apply non-maximum suppression to all predicted boxes output by the
test_net method.
"""
num_classes = len(all_boxes)
num_images = len(all_boxes[0])
nms_boxes = [[[] for _ in range(num_images)] for _ in range(num_classes)]
for cls_ind in range(num_classes):
for im_ind in range(num_images):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
inds = np.where((x2 > x1) & (y2 > y1) & (scores > cfg.TEST.DET_THRESHOLD))[0]
dets = dets[inds,:]
if dets == []:
continue
keep = nms(dets, thresh)
if len(keep) == 0:
continue
nms_boxes[cls_ind][im_ind] = dets[keep, :].copy()
return nms_boxes
def test_net(sess, net, imdb, weights_filename, max_per_image=100, thresh=0.0):
np.random.seed(cfg.RNG_SEED)
"""Test a Fast R-CNN network on an image database."""
num_images = len(imdb.image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
output_dir = get_output_dir(imdb, weights_filename)
if os.path.isfile(os.path.join(output_dir, 'detections.pkl')):
all_boxes=pickle.load(open(os.path.join(output_dir, 'detections.pkl'),'r'))
else:
# timers
_t = {'im_detect' : Timer(), 'misc' : Timer()}
for i in range(num_images):
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic()
scores, boxes,_ ,_ = im_detect(sess, net, im)
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
for j in range(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j*4:(j+1)*4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack([all_boxes[j][i][:, -1]
for j in range(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
_t['misc'].toc()
print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time))
det_file = os.path.join(output_dir, 'detections_{:f}.pkl'.format(10))
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
imdb.evaluate_detections(all_boxes, output_dir)
| 7,355 | 32.589041 | 114 | py |
RGB-N | RGB-N-master/lib/model/bbox_transform.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import pdb
from decimal import Decimal
def bbox_transform(ex_rois, gt_rois):
ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0
ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0
ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths
ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights
gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0
gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0
gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths
gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights
targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths
targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights
targets_dw = np.log(gt_widths / ex_widths)
targets_dh = np.log(gt_heights / ex_heights)
targets = np.vstack(
(targets_dx, targets_dy, targets_dw, targets_dh)).transpose()
return targets
def bbox_transform_inv(boxes, deltas):
if boxes.shape[0] == 0:
return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype)
boxes = boxes.astype(deltas.dtype, copy=False)
widths = boxes[:, 2] - boxes[:, 0] + 1.0
heights = boxes[:, 3] - boxes[:, 1] + 1.0
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
dx = deltas[:, 0::4]
dy = deltas[:, 1::4]
dw = deltas[:, 2::4]
dh = deltas[:, 3::4]
dh.astype(Decimal)
dw.astype(Decimal)
pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
pred_w = np.exp(dw) * widths[:, np.newaxis]
pred_h = np.exp(dh) * heights[:, np.newaxis]
pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype)
# x1
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
# y1
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
# x2
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w
# y2
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h
return pred_boxes
def clip_boxes(boxes, im_shape):
"""
Clip boxes to image boundaries.
"""
# x1 >= 0
boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0)
# y1 >= 0
boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0)
# x2 < im_shape[1]
boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0)
# y2 < im_shape[0]
boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0)
return boxes
| 2,622 | 30.22619 | 77 | py |
RGB-N | RGB-N-master/lib/model/nms_wrapper.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from model.config import cfg
from nms.gpu_nms import gpu_nms
from nms.cpu_nms import cpu_nms
from nms.py_cpu_nms import py_cpu_nms
def nms(dets, thresh, force_cpu=False):
"""Dispatch to either CPU or GPU NMS implementations."""
if dets.shape[0] == 0:
return []
if cfg.USE_GPU_NMS and not force_cpu:
return gpu_nms(dets, thresh, device_id=cfg.GPU_ID)
else:
return cpu_nms(dets, thresh)
| 764 | 30.875 | 58 | py |
RGB-N | RGB-N-master/lib/model/config.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path as osp
import numpy as np
# `pip install easydict` if you don't have it
from easydict import EasyDict as edict
__C = edict()
# Consumers can get config by:
# from fast_rcnn_config import cfg
cfg = __C
#
# Training options
#
__C.TRAIN = edict()
# Initial learning rate
__C.TRAIN.LEARNING_RATE = 0.001
# Momentum
__C.TRAIN.MOMENTUM = 0.9
# Weight decay, for regularization
__C.TRAIN.WEIGHT_DECAY = 0.0005
# Factor for reducing the learning rate
__C.TRAIN.GAMMA = 0.1
# Step size for reducing the learning rate, currently only support one step
__C.TRAIN.STEPSIZE = 30000
# Iteration intervals for showing the loss during training, on command line interface
__C.TRAIN.DISPLAY = 10
# Whether to double the learning rate for bias
__C.TRAIN.DOUBLE_BIAS = True
# Whether to initialize the weights with truncated normal distribution
__C.TRAIN.TRUNCATED = False
# Whether to have weight decay on bias as well
__C.TRAIN.BIAS_DECAY = False
# Whether to add ground truth boxes to the pool when sampling regions
__C.TRAIN.USE_GT = False
# Whether to use aspect-ratio grouping of training images, introduced merely for saving
# GPU memory
__C.TRAIN.ASPECT_GROUPING = False
# The number of snapshots kept, older ones are deleted to save space
__C.TRAIN.SNAPSHOT_KEPT = 10
# The time interval for saving tensorflow summaries
__C.TRAIN.SUMMARY_INTERVAL = 180
# Scale to use during training (can NOT list multiple scales)
# The scale is the pixel size of an image's shortest side
__C.TRAIN.SCALES = (600,)
# Max pixel size of the longest side of a scaled input image
__C.TRAIN.MAX_SIZE = 1000
# Images to use per minibatch
__C.TRAIN.IMS_PER_BATCH = 1
# Minibatch size (number of regions of interest [ROIs])
__C.TRAIN.BATCH_SIZE = 128
# Fraction of minibatch that is labeled foreground (i.e. class > 0)
__C.TRAIN.FG_FRACTION = 0.25
# Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)
__C.TRAIN.FG_THRESH = 0.5
# Overlap threshold for a ROI to be considered background (class = 0 if
# overlap in [LO, HI))
__C.TRAIN.BG_THRESH_HI = 0.5
__C.TRAIN.BG_THRESH_LO = 0.1
# Use horizontally-flipped images during training?
__C.TRAIN.USE_FLIPPED = True
# Train bounding-box regressors
__C.TRAIN.BBOX_REG = True
# Overlap required between a ROI and ground-truth box in order for that ROI to
# be used as a bounding-box regression training example
__C.TRAIN.BBOX_THRESH = 0.5
# Iterations between snapshots
__C.TRAIN.SNAPSHOT_ITERS = 4000
# solver.prototxt specifies the snapshot path prefix, this adds an optional
# infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel
__C.TRAIN.SNAPSHOT_PREFIX = 'res101_faster_rcnn'
# __C.TRAIN.SNAPSHOT_INFIX = ''
# Use a prefetch thread in roi_data_layer.layer
# So far I haven't found this useful; likely more engineering work is required
# __C.TRAIN.USE_PREFETCH = False
# Normalize the targets (subtract empirical mean, divide by empirical stddev)
__C.TRAIN.BBOX_NORMALIZE_TARGETS = True
# Deprecated (inside weights)
__C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
# Normalize the targets using "precomputed" (or made up) means and stdevs
# (BBOX_NORMALIZE_TARGETS must also be True)
__C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = True
__C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0)
__C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2)
# Train using these proposals
__C.TRAIN.PROPOSAL_METHOD = 'gt'
# Make minibatches from images that have similar aspect ratios (i.e. both
# tall and thin or both short and wide) in order to avoid wasting computation
# on zero-padding.
# Use RPN to detect objects
__C.TRAIN.HAS_RPN = True
# IOU >= thresh: positive example
__C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7
# IOU < thresh: negative example
__C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3
# If an anchor statisfied by positive and negative conditions set to negative
__C.TRAIN.RPN_CLOBBER_POSITIVES = False
# Max number of foreground examples
__C.TRAIN.RPN_FG_FRACTION = 0.5
# Total number of examples
__C.TRAIN.RPN_BATCHSIZE = 256
# NMS threshold used on RPN proposals
__C.TRAIN.RPN_NMS_THRESH = 0.7
# Number of top scoring boxes to keep before apply NMS to RPN proposals
__C.TRAIN.RPN_PRE_NMS_TOP_N = 12000
# Number of top scoring boxes to keep after applying NMS to RPN proposals
__C.TRAIN.RPN_POST_NMS_TOP_N = 2000
# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
# __C.TRAIN.RPN_MIN_SIZE = 16
# Deprecated (outside weights)
__C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
# Give the positive RPN examples weight of p * 1 / {num positives}
# and give negatives a weight of (1 - p)
# Set to -1.0 to use uniform example weighting
__C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0
# Whether to use all ground truth bounding boxes for training,
# For COCO, setting USE_ALL_GT to False will exclude boxes that are flagged as ''iscrowd''
__C.TRAIN.USE_ALL_GT = True
__C.TRAIN.FUSE = True
__C.TRAIN.USE_NOISE = False
__C.TRAIN.USE_NOISE_AUG = False
__C.TRAIN.USE_JPG_AUG = False
__C.TRAIN.HNM = False
#
# Testing options
#
__C.TEST = edict()
# Scale to use during testing (can NOT list multiple scales)
# The scale is the pixel size of an image's shortest side
__C.TEST.SCALES = (600,)
# Max pixel size of the longest side of a scaled input image
__C.TEST.MAX_SIZE = 1000
# Overlap threshold used for non-maximum suppression (suppress boxes with
# IoU >= this threshold)
__C.TEST.NMS = 0.2
# Experimental: treat the (K+1) units in the cls_score layer as linear
# predictors (trained, eg, with one-vs-rest SVMs).
__C.TEST.SVM = False
# Test using bounding-box regressors
__C.TEST.BBOX_REG = True
# Propose boxes
__C.TEST.HAS_RPN = False
# Test using these proposals
__C.TEST.PROPOSAL_METHOD = 'gt'
## NMS threshold used on RPN proposals
__C.TEST.RPN_NMS_THRESH = 0.7
## Number of top scoring boxes to keep before apply NMS to RPN proposals
__C.TEST.RPN_PRE_NMS_TOP_N = 6000
## Number of top scoring boxes to keep after applying NMS to RPN proposals
__C.TEST.RPN_POST_NMS_TOP_N = 100
# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
# __C.TEST.RPN_MIN_SIZE = 16
# Testing mode, default to be 'nms', 'top' is slower but better
# See report for details
__C.TEST.MODE = 'nms'
# Only useful when TEST.MODE is 'top', specifies the number of top proposals to select
__C.TEST.RPN_TOP_N = 5000
#
# ResNet options
#
__C.RESNET = edict()
# Option to set if max-pooling is appended after crop_and_resize.
# if true, the region will be resized to a squre of 2xPOOLING_SIZE,
# then 2x2 max-pooling is applied; otherwise the region will be directly
# resized to a square of POOLING_SIZE
__C.RESNET.MAX_POOL = False
# Number of fixed blocks during finetuning, by default the first of all 4 blocks is fixed
# Range: 0 (none) to 3 (all)
__C.RESNET.FIXED_BLOCKS = 1
# Whether to tune the batch nomalization parameters during training
__C.RESNET.BN_TRAIN = False
#
# MISC
#
# The mapping from image coordinates to feature map coordinates might cause
# some boxes that are distinct in image space to become identical in feature
# coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor
# for identifying duplicate boxes.
# 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16
__C.DEDUP_BOXES = 1. / 16.
# Pixel mean values (BGR order) as a (1, 1, 3) array
# We use the same pixel mean for all networks even though it's not exactly what
# they were trained with
__C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])
# For reproducibility
__C.RNG_SEED = 3
# A small number that's used many times
__C.EPS = 1e-14
# Root directory of project
__C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..'))
# Data directory
__C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data'))
# Name (or path to) the matlab executable
__C.MATLAB = 'matlab'
# Place outputs under an experiments directory
__C.EXP_DIR = 'default'
# Use GPU implementation of non-maximum suppression
__C.USE_GPU_NMS = True
# Default GPU device id
__C.GPU_ID = 0
# Default pooling mode, only 'crop' is available
__C.POOLING_MODE = 'crop'
# Size of the pooled region after RoI pooling
__C.POOLING_SIZE = 7
# Anchor scales for RPN
__C.ANCHOR_SCALES = [8,16,32]
# Anchor ratios for RPN
__C.ANCHOR_RATIOS = [0.5,1,2]
def get_output_dir(imdb, weights_filename):
"""Return the directory where experimental artifacts are placed.
If the directory does not exist, it is created.
A canonical path is built using the name from an imdb and a network
(if not None).
"""
outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name))
if weights_filename is None:
weights_filename = 'default'
outdir = osp.join(outdir, weights_filename)
if not os.path.exists(outdir):
os.makedirs(outdir)
return outdir
def get_output_tb_dir(imdb, weights_filename):
"""Return the directory where tensorflow summaries are placed.
If the directory does not exist, it is created.
A canonical path is built using the name from an imdb and a network
(if not None).
"""
outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'tensorboard', __C.EXP_DIR, imdb.name))
if weights_filename is None:
weights_filename = 'default'
outdir = osp.join(outdir, weights_filename)
if not os.path.exists(outdir):
os.makedirs(outdir)
return outdir
def _merge_a_into_b(a, b):
"""Merge config dictionary a into config dictionary b, clobbering the
options in b whenever they are also specified in a.
"""
if type(a) is not edict:
return
for k, v in a.items():
# a must specify keys that are in b
if k not in b:
raise KeyError('{} is not a valid config key'.format(k))
# the types must match, too
old_type = type(b[k])
if old_type is not type(v):
if isinstance(b[k], np.ndarray):
v = np.array(v, dtype=b[k].dtype)
else:
raise ValueError(('Type mismatch ({} vs. {}) '
'for config key: {}').format(type(b[k]),
type(v), k))
# recursively merge dicts
if type(v) is edict:
try:
_merge_a_into_b(a[k], b[k])
except:
print(('Error under config key: {}'.format(k)))
raise
else:
b[k] = v
def cfg_from_file(filename):
"""Load a config file and merge it into the default options."""
import yaml
with open(filename, 'r') as f:
yaml_cfg = edict(yaml.load(f))
_merge_a_into_b(yaml_cfg, __C)
def cfg_from_list(cfg_list):
"""Set config keys via list (e.g., from command line)."""
from ast import literal_eval
assert len(cfg_list) % 2 == 0
for k, v in zip(cfg_list[0::2], cfg_list[1::2]):
key_list = k.split('.')
d = __C
for subkey in key_list[:-1]:
assert subkey in d
d = d[subkey]
subkey = key_list[-1]
assert subkey in d
try:
value = literal_eval(v)
except:
# handle the case when v is a string literal
value = v
assert type(value) == type(d[subkey]), \
'type {} does not match original type {}'.format(
type(value), type(d[subkey]))
d[subkey] = value
| 11,267 | 29.209115 | 91 | py |
RGB-N | RGB-N-master/lib/model/__init__.py | from . import config
| 21 | 10 | 20 | py |
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