def __get_paf_and_heatmap(self, img_raw):
multiplier = [scale * self.configer.get('data', 'input_size')[0] / img_raw.shape[1]
for scale in self.configer.get('data', 'scale_search')]
heatmap_avg = np.zeros((img_raw.shape[0], img_raw.shape[1], self.configer.get('network', 'heatmap_out')))
paf_avg = np.zeros((img_raw.shape[0], img_raw.shape[1], self.configer.get('network', 'paf_out')))
for i, scale in enumerate(multiplier):
img_test = cv2.resize(img_raw, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
img_test_pad, pad = PadImage(self.configer.get('network', 'stride'))(img_test)
img_test_pad = ToTensor()(img_test_pad)
img_test_pad = Normalize(mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params', 'std'))(img_test_pad)
with torch.no_grad():
img_test_pad = img_test_pad.unsqueeze(0).to(self.device)
paf_out, heatmap_out = self.pose_net(img_test_pad)
# extract outputs, resize, and remove padding
heatmap = heatmap_out.data.squeeze().cpu().numpy().transpose(1, 2, 0)
heatmap = cv2.resize(heatmap, (0, 0), fx=self.configer.get('network', 'stride'),
fy=self.configer.get('network', 'stride'), interpolation=cv2.INTER_CUBIC)
heatmap = heatmap[:img_test_pad.size(2) - pad[3], :img_test_pad.size(3) - pad[2], :]
heatmap = cv2.resize(heatmap, (img_raw.shape[1], img_raw.shape[0]), interpolation=cv2.INTER_CUBIC)
paf = paf_out.data.squeeze().cpu().numpy().transpose(1, 2, 0)
paf = cv2.resize(paf, (0, 0), fx=self.configer.get('network', 'stride'),
fy=self.configer.get('network', 'stride'), interpolation=cv2.INTER_CUBIC)
paf = paf[:img_test_pad.size(2) - pad[3], :img_test_pad.size(3) - pad[2], :]
paf = cv2.resize(paf, (img_raw.shape[1], img_raw.shape[0]), interpolation=cv2.INTER_CUBIC)
heatmap_avg = heatmap_avg + heatmap / len(multiplier)
paf_avg = paf_avg + paf / len(multiplier)
return paf_avg, heatmap_avg
def __get_paf_and_heatmap(self, img_raw):
multiplier = [
scale * self.configer.get('data', 'input_size')[0] /
img_raw.shape[1]
for scale in self.configer.get('data', 'scale_search')
]
heatmap_avg = np.zeros((img_raw.shape[0], img_raw.shape[1],
self.configer.get('data', 'num_keypoints')))
paf_avg = np.zeros((img_raw.shape[0], img_raw.shape[1],
self.configer.get('network', 'paf_out')))
partmap_avg = np.zeros((img_raw.shape[0], img_raw.shape[1],
self.configer.get('network', 'heatmap_out')))
for i, scale in enumerate(multiplier):
img_test = cv2.resize(img_raw, (0, 0),
fx=scale,
fy=scale,
interpolation=cv2.INTER_CUBIC)
img_test_pad, pad = PadImage(self.configer.get(
'network', 'stride'))(img_test)
pad_right = pad[2]
pad_down = pad[3]
img_test_pad = ToTensor()(img_test_pad)
img_test_pad = Normalize(
mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params', 'std'))(img_test_pad)
with torch.no_grad():
img_test_pad = img_test_pad.unsqueeze(0).to(self.device)
paf_out_list, partmap_out_list = self.pose_net(img_test_pad)
paf_out = paf_out_list[-1]
partmap_out = partmap_out_list[-1]
partmap = partmap_out.data.squeeze().cpu().numpy().transpose(
1, 2, 0)
paf = paf_out.data.squeeze().cpu().numpy().transpose(1, 2, 0)
# self.pose_visualizer.vis_tensor(heatmap_out)
heatmap = np.zeros((partmap.shape[0], partmap.shape[1],
self.configer.get('data', 'num_keypoints')))
part_num = np.zeros((self.configer.get('data', 'num_keypoints'), ))
for index in range(len(self.configer.get('details', 'limb_seq'))):
a = self.configer.get('details', 'limb_seq')[index][0] - 1
b = self.configer.get('details', 'limb_seq')[index][1] - 1
heatmap_a = partmap[:, :, index * 4:index * 4 + 2]**2
heatmap_a = np.sqrt(np.sum(heatmap_a, axis=2).squeeze())
heatmap[:, :, a] = (heatmap[:, :, a] * part_num[a] +
heatmap_a) / (part_num[a] + 1)
part_num[a] += 1
heatmap_b = partmap[:, :, index * 4 + 2:index * 4 + 4]**2
heatmap_b = np.sqrt(np.sum(heatmap_b, axis=2).squeeze())
heatmap[:, :, b] = (heatmap[:, :, b] * part_num[b] +
heatmap_b) / (part_num[b] + 1)
part_num[b] += 1
heatmap = cv2.resize(heatmap, (0, 0),
fx=self.configer.get('network', 'stride'),
fy=self.configer.get('network', 'stride'),
interpolation=cv2.INTER_CUBIC)
heatmap = heatmap[:img_test_pad.size(2) -
pad_down, :img_test_pad.size(3) - pad_right, :]
heatmap = cv2.resize(heatmap, (img_raw.shape[1], img_raw.shape[0]),
interpolation=cv2.INTER_CUBIC)
partmap = cv2.resize(partmap, (0, 0),
fx=self.configer.get('network', 'stride'),
fy=self.configer.get('network', 'stride'),
interpolation=cv2.INTER_CUBIC)
partmap = partmap[:img_test_pad.size(2) -
pad_down, :img_test_pad.size(3) - pad_right, :]
partmap = cv2.resize(partmap, (img_raw.shape[1], img_raw.shape[0]),
interpolation=cv2.INTER_CUBIC)
paf = cv2.resize(paf, (0, 0),
fx=self.configer.get('network', 'stride'),
fy=self.configer.get('network', 'stride'),
interpolation=cv2.INTER_CUBIC)
paf = paf[:img_test_pad.size(2) - pad_down, :img_test_pad.size(3) -
pad_right, :]
paf = cv2.resize(paf, (img_raw.shape[1], img_raw.shape[0]),
interpolation=cv2.INTER_CUBIC)
partmap_avg = partmap_avg + partmap / len(multiplier)
heatmap_avg = heatmap_avg + heatmap / len(multiplier)
paf_avg = paf_avg + paf / len(multiplier)
return paf_avg, heatmap_avg, partmap_avg