Spaces:
Sleeping
Sleeping
File size: 5,162 Bytes
5a486d6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 |
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao ([email protected])
# ------------------------------------------------------------------------------
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
|