Spaces:
Running
Running
File size: 4,882 Bytes
1f77b91 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 |
from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import cv2
from utils import *
import argparse
import os
import os.path as osp
from darknet import Darknet
# from preprocess import prep_image, inp_to_image
import pandas as pd
import random
import pickle as pkl
import itertools
import os
if __name__ == '__main__':
images = os.path.join('victoria.jpg')
batch_size = int(1)
confidence = float(0.5)
nms_thesh = float(0.4)
reso = 416
start = 0
CUDA = torch.cuda.is_available()
num_classes = 80
classes = load_classes('data/coco.names')
#Set up the neural network
model = Darknet("yolov3.cfg")
model.load_weights("yolov3.weights")
print(' [*] Model Loaded Successfuly')
# set model resolution
model.net_info["height"] = reso
inp_dim = int(model.net_info["height"])
assert inp_dim % 32 == 0
assert inp_dim > 32
# If there's a GPU availible, put the model on GPU
if CUDA:
model.cuda()
# Set the model in evaluation mode
model.eval()
imlist = []
imlist.append( osp.join(osp.realpath('.') , images) )
batches = list( map( prep_image , imlist , [ inp_dim for x in range( len(imlist) ) ] ) )
im_batches = [x[0] for x in batches]
orig_ims = [x[1] for x in batches]
im_dim_list = [x[2] for x in batches]
print( 'im_dim_list : ' , im_dim_list )
im_dim_list = torch.FloatTensor(im_dim_list).repeat(1,2)
print( 'im_dim_list : after' , im_dim_list )
if CUDA:
im_dim_list = im_dim_list.cuda()
print('im_batches' , len(im_batches))
batch = im_batches[0]
if CUDA:
batch = batch.cuda()
#Apply offsets to the result predictions
#Tranform the predictions as described in the YOLO paper
#flatten the prediction vector
# B x (bbox cord x no. of anchors) x grid_w x grid_h --> B x bbox x (all the boxes)
# Put every proposed box as a row.
with torch.no_grad():
prediction = model(Variable(batch), CUDA)
# prediction = prediction[:,scale_indices]
#get the boxes with object confidence > threshold
#Convert the cordinates to absolute coordinates
#perform NMS on these boxes, and save the results
#I could have done NMS and saving seperately to have a better abstraction
#But both these operations require looping, hence
#clubbing these ops in one loop instead of two.
#loops are slower than vectorised operations.
prediction = write_results(prediction, confidence, num_classes, nms = True, nms_conf = nms_thesh)
# if type(prediction) == int:
# continue
end = time.time()
# print(end - start)
# prediction[:,0] += i*batch_size
output = prediction
# 1, 1, 1
# print( 'enumerate : ' , batch_size , len(imlist) , min( batch_size , len(imlist) ) )
for im_num, image in enumerate( imlist ):
im_id = im_num
objs = [classes[int(x[-1])] for x in output if int(x[0]) == im_id]
print("{0:20s} predicted in {1:6.3f} seconds".format(image.split("/")[-1], (end - start)/batch_size))
print("{0:20s} {1:s}".format("Objects Detected:", " ".join(objs)))
print("----------------------------------------------------------")
im_dim_list = torch.index_select(im_dim_list, 0, output[:,0].long())
scaling_factor = torch.min(inp_dim/im_dim_list,1)[0].view(-1,1)
output[:,[1,3]] -= (inp_dim - scaling_factor*im_dim_list[:,0].view(-1,1))/2
output[:,[2,4]] -= (inp_dim - scaling_factor*im_dim_list[:,1].view(-1,1))/2
output[:,1:5] /= scaling_factor
for i in range(output.shape[0]):
output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim_list[i,0])
output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim_list[i,1])
colors = pkl.load(open("pallete", "rb"))
def write(x, batches, results):
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
img = results[int(x[0])]
print( 'img' , int( x[0] ) )
print( 'cls' , int( x[-1] ) )
cls = int(x[-1])
label = "{0}".format(classes[cls])
color = random.choice(colors)
cv2.rectangle(img, c1, c2,color, 1)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
cv2.rectangle(img, c1, c2,color, -1)
cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1)
return img
list(map(lambda x: write(x, im_batches, orig_ims), output))
det_names = pd.Series(imlist).apply(lambda x: "{}/det_{}".format('det',x.split("/")[-1]))
print('det_names ',det_names)
print('orig_ims ',orig_ims[0].shape)
print('output : ',output)
list(map(cv2.imwrite, det_names, orig_ims))
|