def debug(self):
base_dir = os.path.join(self.configer.get('project_dir'),
'vis/results/pose',
self.configer.get('dataset'), 'debug')
if not os.path.exists(base_dir):
os.makedirs(base_dir)
val_data_loader = self.pose_data_loader.get_valloader()
for i, (inputs, heatmap) in enumerate(val_data_loader):
for j in range(inputs.size(0)):
ori_img = DeNormalize(
mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params', 'std'))(inputs[j])
image_raw = ori_img.numpy().transpose(1, 2, 0)
image_raw = cv2.cvtColor(image_raw, cv2.COLOR_RGB2BGR)
heatmap_avg = heatmap[j].numpy().transpose(1, 2, 0)
heatmap_avg = cv2.resize(
heatmap_avg, (0, 0),
fx=self.configer.get('network', 'stride'),
fy=self.configer.get('network', 'stride'),
interpolation=cv2.INTER_CUBIC)
all_peaks = self.__extract_heatmap_info(heatmap_avg)
image_save = self.__draw_key_point(all_peaks, image_raw)
cv2.imwrite(
os.path.join(base_dir, '{}_{}_result.jpg'.format(i, j)),
image_save)
def debug(self):
base_dir = os.path.join(self.configer.get('project_dir'),
'vis/results/seg',
self.configer.get('dataset'), 'debug')
if not os.path.exists(base_dir):
os.makedirs(base_dir)
val_data_loader = self.seg_data_loader.get_valloader()
count = 0
for i, (inputs, targets) in enumerate(val_data_loader):
for j in range(inputs.size(0)):
count = count + 1
if count > 20:
exit(1)
ori_img = DeNormalize(
mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params', 'std'))(inputs[j])
ori_img = ori_img.numpy().transpose(1, 2, 0).astype(np.uint8)
image_bgr = cv2.cvtColor(ori_img, cv2.COLOR_RGB2BGR)
label_map = targets[j].numpy()
image_canvas = self.seg_parser.colorize(label_map,
image_canvas=image_bgr)
cv2.imwrite(
os.path.join(base_dir, '{}_{}_vis.png'.format(i, j)),
image_canvas)
cv2.imshow('main', image_canvas)
cv2.waitKey()
def debug(self):
base_dir = os.path.join(self.configer.get('project_dir'),
'vis/results/det', self.configer.get('dataset'), 'debug')
if not os.path.exists(base_dir):
os.makedirs(base_dir)
val_data_loader = self.det_data_loader.get_valloader()
count = 0
for i, (inputs, bboxes, labels) in enumerate(val_data_loader):
for j in range(inputs.size(0)):
count = count + 1
if count > 20:
exit(1)
ori_img_rgb = DeNormalize(mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params', 'std'))(inputs[j])
ori_img_rgb = ori_img_rgb.numpy().transpose(1, 2, 0).astype(np.uint8)
ori_img_bgr = cv2.cvtColor(ori_img_rgb, cv2.COLOR_RGB2BGR)
eye_matrix = torch.eye(self.configer.get('data', 'num_classes'))
labels_target = eye_matrix[labels.view(-1)].view(inputs.size(0), -1,
self.configer.get('data', 'num_classes'))
boxes, lbls, scores = self.__decode(bboxes[j], labels_target[j])
json_dict = self.__get_info_tree(boxes, lbls, scores, ori_img_rgb)
image_canvas = self.det_parser.draw_bboxes(ori_img_bgr.copy(),
json_dict,
conf_threshold=self.configer.get('vis', 'conf_threshold'))
cv2.imwrite(os.path.join(base_dir, '{}_{}_vis.png'.format(i, j)), image_canvas)
cv2.imshow('main', image_canvas)
cv2.waitKey()
def tensor2bgr(self, tensor):
assert len(tensor.size()) == 3
ori_img = DeNormalize(**self.configer.get('data', 'normalize'))(tensor.cpu())
ori_img = ori_img.numpy().transpose(1, 2, 0).astype(np.uint8)
if self.configer.get('data', 'input_mode') == 'BGR':
return ori_img
else:
image_bgr = cv2.cvtColor(ori_img, cv2.COLOR_RGB2BGR)
return image_bgr
def debug(self):
base_dir = os.path.join(self.configer.get('project_dir'),
'vis/results/pose',
self.configer.get('dataset'), 'debug')
if not os.path.exists(base_dir):
os.makedirs(base_dir)
val_data_loader = self.pose_data_loader.get_valloader()
count = 0
for i, (inputs, heatmap, maskmap,
vecmap) in enumerate(val_data_loader):
for j in range(inputs.size(0)):
count = count + 1
if count > 10:
exit(1)
Log.info(heatmap.size())
ori_img = DeNormalize(
mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params', 'std'))(inputs[j])
ori_img = ori_img.numpy().transpose(1, 2, 0).astype(np.uint8)
image_bgr = cv2.cvtColor(ori_img, cv2.COLOR_RGB2BGR)
heatmap_avg = heatmap[j].numpy().transpose(1, 2, 0)
heatmap_avg = cv2.resize(
heatmap_avg, (0, 0),
fx=self.configer.get('network', 'stride'),
fy=self.configer.get('network', 'stride'),
interpolation=cv2.INTER_CUBIC)
paf_avg = vecmap[j].numpy().transpose(1, 2, 0)
paf_avg = cv2.resize(paf_avg, (0, 0),
fx=self.configer.get('network', 'stride'),
fy=self.configer.get('network', 'stride'),
interpolation=cv2.INTER_CUBIC)
# self.pose_visualizer.vis_paf(paf_avg, image_rgb.astype(np.uint8), name='314{}_{}'.format(i,j))
self.pose_visualizer.vis_peaks(heatmap_avg, image_bgr)
self.pose_visualizer.vis_paf(paf_avg, image_bgr)
all_peaks = self.__extract_heatmap_info(heatmap_avg)
special_k, connection_all = self.__extract_paf_info(
image_bgr, paf_avg, all_peaks)
subset, candidate = self.__get_subsets(connection_all,
special_k, all_peaks)
json_dict = self.__get_info_tree(image_bgr, subset, candidate)
image_canvas = self.pose_parser.draw_points(
image_bgr, json_dict)
image_canvas = self.pose_parser.link_points(
image_canvas, json_dict)
cv2.imwrite(
os.path.join(base_dir, '{}_{}_vis.png'.format(i, j)),
image_canvas)
cv2.imshow('main', image_canvas)
cv2.waitKey()
def debug(self):
base_dir = os.path.join(self.configer.get('project_dir'),
'vis/results/det', self.configer.get('dataset'), 'debug')
if not os.path.exists(base_dir):
os.makedirs(base_dir)
val_data_loader = self.det_data_loader.get_valloader(SSDDataLoader)
count = 0
for i, (inputs, bboxes, labels) in enumerate(val_data_loader):
for j in range(inputs.size(0)):
count = count + 1
if count > 20:
exit(1)
ori_img = DeNormalize(mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params', 'std'))(inputs[j])
ori_img = ori_img.numpy().transpose(1, 2, 0)
image_bgr = cv2.cvtColor(ori_img, cv2.COLOR_RGB2BGR)
eye_matrix = torch.eye(self.configer.get('data', 'num_classes'))
labels_target = eye_matrix[labels.view(-1)].view(inputs.size(0), -1,
self.configer.get('data', 'num_classes'))
boxes, lbls, scores, has_obj = self.__decode(bboxes[j], labels_target[j])
if has_obj:
boxes = boxes.cpu().numpy()
boxes = np.clip(boxes, 0, 1)
lbls = lbls.cpu().numpy()
scores = scores.cpu().numpy()
img_canvas = self.__draw_box(image_bgr, boxes, lbls, scores)
else:
# print('None obj detected!')
img_canvas = image_bgr
# self.det_visualizer.vis_bboxes(paf_avg, image_rgb.astype(np.uint8), name='314{}_{}'.format(i,j))
cv2.imwrite(os.path.join(base_dir, '{}_{}_result.jpg'.format(i, j)), img_canvas)
def debug(self):
base_dir = os.path.join(self.configer.get('project_dir'),
'vis/results/pose',
self.configer.get('dataset'), 'debug')
if not os.path.exists(base_dir):
os.makedirs(base_dir)
val_data_loader = self.pose_data_loader.get_valloader()
count = 0
for i, (inputs, partmap, maskmap,
vecmap) in enumerate(val_data_loader):
for j in range(inputs.size(0)):
count = count + 1
if count > 2:
exit(1)
Log.info(partmap.size())
ori_img = DeNormalize(
mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params', 'std'))(inputs[j])
ori_img = ori_img.numpy().transpose(1, 2, 0).astype(np.uint8)
image_bgr = cv2.cvtColor(ori_img, cv2.COLOR_RGB2BGR)
partmap_avg = partmap[j].numpy().transpose(1, 2, 0)
heatmap_avg = np.zeros(
(partmap_avg.shape[0], partmap_avg.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_avg[:, :, index * 4:index * 4 + 2]**2
heatmap_a = np.sqrt(np.sum(heatmap_a, axis=2).squeeze())
heatmap_avg[:, :,
a] = (heatmap_avg[:, :, a] * part_num[a] +
heatmap_a) / (part_num[a] + 1)
part_num[a] += 1
heatmap_b = partmap_avg[:, :,
index * 4 + 2:index * 4 + 4]**2
heatmap_b = np.sqrt(np.sum(heatmap_b, axis=2).squeeze())
heatmap_avg[:, :,
b] = (heatmap_avg[:, :, b] * part_num[b] +
heatmap_b) / (part_num[b] + 1)
part_num[b] += 1
partmap_avg = cv2.resize(
partmap_avg, (0, 0),
fx=self.configer.get('network', 'stride'),
fy=self.configer.get('network', 'stride'),
interpolation=cv2.INTER_CUBIC)
heatmap_avg = cv2.resize(
heatmap_avg, (0, 0),
fx=self.configer.get('network', 'stride'),
fy=self.configer.get('network', 'stride'),
interpolation=cv2.INTER_CUBIC)
paf_avg = vecmap[j].numpy().transpose(1, 2, 0)
paf_avg = cv2.resize(paf_avg, (0, 0),
fx=self.configer.get('network', 'stride'),
fy=self.configer.get('network', 'stride'),
interpolation=cv2.INTER_CUBIC)
self.pose_visualizer.vis_peaks(heatmap_avg, image_bgr)
self.pose_visualizer.vis_paf(paf_avg, image_bgr)
all_peaks = self.__extract_heatmap_info(heatmap_avg)
special_k, connection_all = self.__extract_paf_info(
image_bgr, paf_avg, partmap_avg, all_peaks)
subset, candidate = self.__get_subsets(connection_all,
special_k, all_peaks)
json_dict = self.__get_info_tree(image_bgr, subset, candidate)
image_canvas = self.pose_parser.draw_points(
image_bgr, json_dict)
image_canvas = self.pose_parser.link_points(
image_canvas, json_dict)
cv2.imwrite(
os.path.join(base_dir, '{}_{}_vis.png'.format(i, j)),
image_canvas)
cv2.imshow('main', image_canvas)
cv2.waitKey()
