def crop_from_dets(img, boxes, inps, pt1, pt2):
'''
Crop human from origin image according to Dectecion Results
'''
imght = img.size(1)
imgwidth = img.size(2)
tmp_img = img
tmp_img[0].add_(-0.406)
tmp_img[1].add_(-0.457)
tmp_img[2].add_(-0.480)
for i, box in enumerate(boxes):
upLeft = torch.Tensor((float(box[0]), float(box[1])))
bottomRight = torch.Tensor((float(box[2]), float(box[3])))
ht = bottomRight[1] - upLeft[1]
width = bottomRight[0] - upLeft[0]
if width > 100:
scaleRate = 0.2
else:
scaleRate = 0.3
upLeft[0] = max(0, upLeft[0] - width * scaleRate / 2)
upLeft[1] = max(0, upLeft[1] - ht * scaleRate / 2)
bottomRight[0] = max(
min(imgwidth - 1, bottomRight[0] + width * scaleRate / 2),
upLeft[0] + 5)
bottomRight[1] = max(
min(imght - 1, bottomRight[1] + ht * scaleRate / 2), upLeft[1] + 5)
inps[i] = cropBox(tmp_img.clone(), upLeft, bottomRight, opt.inputResH,
opt.inputResW)
pt1[i] = upLeft
pt2[i] = bottomRight
return inps, pt1, pt2
def crop_from_dets(img, boxes, inps, pt1, pt2, im_name):
'''
Crop human from origin image according to Dectecion Results
'''
imght = img.size(1)
imgwidth = img.size(2)
# cv2.imwrite(os.path.join(opt.outputpath, 'before', '1.jpg'), img)
# cv2.imwrite('examples/det/before', img)
tmp_img = img
tmp_img[0].add_(-0.406)
tmp_img[1].add_(-0.457)
tmp_img[2].add_(-0.480)
# cv2.imwrite(os.path.join(opt.outputpath, 'after', '2.jpg'), tmp_img)
# cv2.imwrite('examples/det/after', tmp_img)
for i, box in enumerate(boxes):
upLeft = torch.Tensor((float(box[0]), float(box[1])))
bottomRight = torch.Tensor((float(box[2]), float(box[3])))
ht = bottomRight[1] - upLeft[1]
width = bottomRight[0] - upLeft[0]
scaleRate = 0.25
upLeft[0] = max(0, upLeft[0] - width * scaleRate / 2)
upLeft[1] = max(0, upLeft[1] - ht * scaleRate / 2)
bottomRight[0] = max(
min(imgwidth - 1, bottomRight[0] + width * scaleRate / 2),
upLeft[0] + 5)
bottomRight[1] = max(
min(imght - 1, bottomRight[1] + ht * scaleRate / 2), upLeft[1] + 5)
try:
inps[i] = cropBox(tmp_img.clone(), upLeft, bottomRight,
opt.inputResH, opt.inputResW)
except IndexError:
print(tmp_img.shape)
print(upLeft)
print(bottomRight)
print('===')
pt1[i] = upLeft
pt2[i] = bottomRight
img = np.transpose(inps[i].numpy() * 255 * 1.5, (1, 2, 0))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_name = './human/' + im_name[5:17] + str(i) + '.jpg'
cv2.imwrite(img_name, img)
# 返回经过映射的输入图像tensor类型以及经过尺度变换的左上角和右下角坐标
return inps, pt1, pt2
def crop_from_dets(img, boxes):
'''
Crop human from origin image according to Dectecion Results
'''
imght = img.size(1)
imgwidth = img.size(2)
inp = []
pt1 = []
pt2 = []
for box in boxes:
tmp_img = img.clone()
tmp_img[0].add_(-0.406)
tmp_img[1].add_(-0.457)
tmp_img[2].add_(-0.480)
upLeft = torch.Tensor((float(box[0]), float(box[1])))
bottomRight = torch.Tensor((float(box[2]), float(box[3])))
ht = bottomRight[1] - upLeft[1]
width = bottomRight[0] - upLeft[0]
if width > 100:
scaleRate = 0.2
else:
scaleRate = 0.3
upLeft[0] = max(0, upLeft[0] - width * scaleRate / 2)
upLeft[1] = max(0, upLeft[1] - ht * scaleRate / 2)
bottomRight[0] = max(
min(imgwidth - 1, bottomRight[0] + width * scaleRate / 2),
upLeft[0] + 5)
bottomRight[1] = max(
min(imght - 1, bottomRight[1] + ht * scaleRate / 2), upLeft[1] + 5)
tmp_inp = cropBox(tmp_img, upLeft, bottomRight, opt.inputResH,
opt.inputResW)
inp.append(tmp_inp.unsqueeze(0))
pt1.append(upLeft.unsqueeze(0))
pt2.append(bottomRight.unsqueeze(0))
inp = torch.cat(inp, 0)
pt1 = torch.cat(pt1, 0)
pt2 = torch.cat(pt2, 0)
return inp, pt1, pt2
def crop_from_dets(img, boxes, inps, pt1, pt2):
'''
根据检测结果从原始图像中裁剪人
:param img:
:param boxes:
:param inps:
:param pt1:
:param pt2:
:return:
'''
imght = img.size(1)
imgwidth = img.size(2)
tmp_img = img
tmp_img[0].add_(-0.406)
tmp_img[1].add_(-0.457)
tmp_img[2].add_(-0.480)
for i, box in enumerate(boxes):
upLeft = torch.Tensor((float(box[0]), float(box[1])))
bottomRight = torch.Tensor((float(box[2]), float(box[3])))
ht = bottomRight[1] - upLeft[1]
width = bottomRight[0] - upLeft[0]
scaleRate = 0.3
upLeft[0] = max(0, upLeft[0] - width * scaleRate / 2)
upLeft[1] = max(0, upLeft[1] - ht * scaleRate / 2)
bottomRight[0] = max(
min(imgwidth - 1, bottomRight[0] + width * scaleRate / 2),
upLeft[0] + 5)
bottomRight[1] = max(
min(imght - 1, bottomRight[1] + ht * scaleRate / 2), upLeft[1] + 5)
try:
inps[i] = cropBox(tmp_img.clone(), upLeft, bottomRight,
opt.inputResH, opt.inputResW)
except IndexError:
print(tmp_img.shape)
print(upLeft)
print(bottomRight)
print('===')
pt1[i] = upLeft
pt2[i] = bottomRight
return inps, pt1, pt2
def crop_from_dets(img, boxes, inps, pt1, pt2):
'''
Crop human from origin image according to Dectecion Results
'''
imght = img.size(1)
imgwidth = img.size(2)
tmp_img = img
tmp_img[0].add_(-0.406)
tmp_img[1].add_(-0.457)
tmp_img[2].add_(-0.480)
for i, box in enumerate(boxes):
upLeft = torch.Tensor(
(float(box[0]), float(box[1])))
bottomRight = torch.Tensor(
(float(box[2]), float(box[3])))
ht = bottomRight[1] - upLeft[1]
width = bottomRight[0] - upLeft[0]
scaleRate = 0.3
upLeft[0] = max(0, upLeft[0] - width * scaleRate / 2)
upLeft[1] = max(0, upLeft[1] - ht * scaleRate / 2)
bottomRight[0] = max(
min(imgwidth - 1, bottomRight[0] + width * scaleRate / 2), upLeft[0] + 5)
bottomRight[1] = max(
min(imght - 1, bottomRight[1] + ht * scaleRate / 2), upLeft[1] + 5)
try:
inps[i] = cropBox(tmp_img.clone(), upLeft, bottomRight, opt.inputResH, opt.inputResW)
# print('#########################input information inps= ',inps[i].shape)
except IndexError:
print(tmp_img.shape)
print(upLeft)
print(bottomRight)
print('===')
pt1[i] = upLeft
pt2[i] = bottomRight
return inps, pt1, pt2
