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# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import cv2
import torch
class BRG2Tensor_transform(object):
def __call__(self, pic):
img = torch.from_numpy(pic.transpose((2, 0, 1)))
if isinstance(img, torch.ByteTensor):
return img.float()
else:
return img
class BGR2RGB_transform(object):
def __call__(self, tensor):
return tensor[[2,1,0],:,:]
def flip_back(output_flipped, matched_parts):
'''
ouput_flipped: numpy.ndarray(batch_size, num_joints, height, width)
'''
assert output_flipped.ndim == 4,\
'output_flipped should be [batch_size, num_joints, height, width]'
output_flipped = output_flipped[:, :, :, ::-1]
for pair in matched_parts:
tmp = output_flipped[:, pair[0], :, :].copy()
output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]
output_flipped[:, pair[1], :, :] = tmp
return output_flipped
def fliplr_joints(joints, joints_vis, width, matched_parts):
"""
flip coords
"""
# Flip horizontal
joints[:, 0] = width - joints[:, 0] - 1
# Change left-right parts
for pair in matched_parts:
joints[pair[0], :], joints[pair[1], :] = \
joints[pair[1], :], joints[pair[0], :].copy()
joints_vis[pair[0], :], joints_vis[pair[1], :] = \
joints_vis[pair[1], :], joints_vis[pair[0], :].copy()
return joints*joints_vis, joints_vis
def transform_preds(coords, center, scale, input_size):
target_coords = np.zeros(coords.shape)
trans = get_affine_transform(center, scale, 0, input_size, inv=1)
for p in range(coords.shape[0]):
target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)
return target_coords
def transform_parsing(pred, center, scale, width, height, input_size):
trans = get_affine_transform(center, scale, 0, input_size, inv=1)
target_pred = cv2.warpAffine(
pred,
trans,
(int(width), int(height)), #(int(width), int(height)),
flags=cv2.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0))
return target_pred
def transform_logits(logits, center, scale, width, height, input_size):
trans = get_affine_transform(center, scale, 0, input_size, inv=1)
channel = logits.shape[2]
target_logits = []
for i in range(channel):
target_logit = cv2.warpAffine(
logits[:,:,i],
trans,
(int(width), int(height)), #(int(width), int(height)),
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0))
target_logits.append(target_logit)
target_logits = np.stack(target_logits,axis=2)
return target_logits
def get_affine_transform(center,
scale,
rot,
output_size,
shift=np.array([0, 0], dtype=np.float32),
inv=0):
if not isinstance(scale, np.ndarray) and not isinstance(scale, list):
print(scale)
scale = np.array([scale, scale])
scale_tmp = scale
src_w = scale_tmp[0]
dst_w = output_size[1]
dst_h = output_size[0]
rot_rad = np.pi * rot / 180
src_dir = get_dir([0, src_w * -0.5], rot_rad)
dst_dir = np.array([0, (dst_w-1) * -0.5], np.float32)
src = np.zeros((3, 2), dtype=np.float32)
dst = np.zeros((3, 2), dtype=np.float32)
src[0, :] = center + scale_tmp * shift
src[1, :] = center + src_dir + scale_tmp * shift
dst[0, :] = [(dst_w-1) * 0.5, (dst_h-1) * 0.5]
dst[1, :] = np.array([(dst_w-1) * 0.5, (dst_h-1) * 0.5]) + dst_dir
src[2:, :] = get_3rd_point(src[0, :], src[1, :])
dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])
if inv:
trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
else:
trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
return trans
def affine_transform(pt, t):
new_pt = np.array([pt[0], pt[1], 1.]).T
new_pt = np.dot(t, new_pt)
return new_pt[:2]
def get_3rd_point(a, b):
direct = a - b
return b + np.array([-direct[1], direct[0]], dtype=np.float32)
def get_dir(src_point, rot_rad):
sn, cs = np.sin(rot_rad), np.cos(rot_rad)
src_result = [0, 0]
src_result[0] = src_point[0] * cs - src_point[1] * sn
src_result[1] = src_point[0] * sn + src_point[1] * cs
return src_result
def crop(img, center, scale, output_size, rot=0):
trans = get_affine_transform(center, scale, rot, output_size)
dst_img = cv2.warpAffine(img,
trans,
(int(output_size[1]), int(output_size[0])),
flags=cv2.INTER_LINEAR)
return dst_img
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