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 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 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 vis_default_bboxes(self,
ori_img_in,
default_bboxes,
labels,
name='default',
sub_dir='encode'):
base_dir = os.path.join(self.configer.get('project_dir'), DET_DIR,
sub_dir)
if not os.path.exists(base_dir):
log.error('Dir:{} not exists!'.format(base_dir))
os.makedirs(base_dir)
if not isinstance(ori_img_in, np.ndarray):
ori_img = DeNormalize(
div_value=self.configer.get('normalize', 'div_value'),
mean=self.configer.get('normalize', 'mean'),
std=self.configer.get('normalize', 'std'))(ori_img_in.clone())
ori_img = ori_img.data.cpu().squeeze().numpy().transpose(
1, 2, 0).astype(np.uint8)
ori_img = cv2.cvtColor(ori_img, cv2.COLOR_RGB2BGR)
else:
ori_img = ori_img_in.copy()
assert labels.size(0) == default_bboxes.size(0)
bboxes = torch.cat([
default_bboxes[:, :2] - default_bboxes[:, 2:] / 2,
default_bboxes[:, :2] + default_bboxes[:, 2:] / 2
], 1)
height, width, _ = ori_img.shape
for i in range(labels.size(0)):
if labels[i] == 0:
continue
class_name = self.configer.get('details',
'name_seq')[labels[i] - 1]
color_num = len(self.configer.get('details', 'color_list'))
cv2.rectangle(
ori_img,
(int(bboxes[i][0] * width), int(bboxes[i][1] * height)),
(int(bboxes[i][2] * width), int(bboxes[i][3] * height)),
color=self.configer.get(
'details', 'color_list')[(labels[i] - 1) % color_num],
thickness=3)
cv2.putText(ori_img,
class_name, (int(bboxes[i][0] * width) + 5,
int(bboxes[i][3] * height) - 5),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.5,
color=self.configer.get('details',
'color_list')[(labels[i] - 1) %
color_num],
thickness=2)
img_path = os.path.join(base_dir, '{}.jpg'.format(name))
cv2.imwrite(img_path, ori_img)
def vis_rois(self, inputs, indices_and_rois, rois_labels=None, name='default', sub_dir='rois'):
base_dir = os.path.join(self.configer.get('project_dir'), DET_DIR, sub_dir)
if not os.path.exists(base_dir):
log.error('Dir:{} not exists!'.format(base_dir))
os.makedirs(base_dir)
for i in range(inputs.size(0)):
rois = indices_and_rois[indices_and_rois[:, 0] == i][:, 1:]
ori_img = DeNormalize(div_value=self.configer.get('normalize', 'div_value'),
mean=self.configer.get('normalize', 'mean'),
std=self.configer.get('normalize', 'std'))(inputs[i])
ori_img = ori_img.data.cpu().squeeze().numpy().transpose(1, 2, 0).astype(np.uint8)
ori_img = cv2.cvtColor(ori_img, cv2.COLOR_RGB2BGR)
color_num = len(self.configer.get('details', 'color_list'))
for j in range(len(rois)):
label = 1 if rois_labels is None else rois_labels[j]
if label == 0:
continue
class_name = self.configer.get('details', 'name_seq')[label - 1]
cv2.rectangle(ori_img,
(int(rois[j][0]), int(rois[j][1])),
(int(rois[j][2]), int(rois[j][3])),
color=self.configer.get('details', 'color_list')[(label - 1) % color_num], thickness=3)
cv2.putText(ori_img, class_name,
(int(rois[j][0]) + 5, int(rois[j][3]) - 5),
cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.5,
color=self.configer.get('details', 'color_list')[(label - 1) % color_num], thickness=2)
img_path = os.path.join(base_dir, '{}_{}_{}.jpg'.format(name, i, time.time()))
cv2.imwrite(img_path, ori_img)
def vis_peaks(self,
heatmap,
ori_img,
name='default',
vis_dir=PEAK_DIR,
scale_factor=1,
img_size=(368, 368)):
vis_dir = os.path.join(self.configer.get('project_dir'), vis_dir)
if not os.path.exists(vis_dir):
Log.error('Dir:{} not exists!'.format(vis_dir))
os.makedirs(vis_dir)
if not isinstance(heatmap, np.ndarray):
if len(heatmap.size()) != 3:
Log.error('Heatmap size is not valid.')
exit(1)
heatmap = heatmap.data.squeeze().cpu().numpy().transpose(1, 2, 0)
if not isinstance(ori_img, np.ndarray):
ori_img = DeNormalize(mean=[128.0, 128.0, 128.0],
std=[256.0, 256.0, 256.0])(ori_img)
ori_img = ori_img.data.cpu().squeeze().numpy().transpose(1, 2, 0)
for j in range(self.configer.get('num_keypoints')):
peaks = self.__get_peaks(heatmap[:, :, j].data.cpu().numpy())
image_path = os.path.join(vis_dir, '{}_{}.jpg'.format(name, j))
for peak in peaks:
image = cv2.circle(ori_img, (peak[0], peak[1]),
self.configer.get('vis', 'circle_radius'),
(0, 255, 0),
thickness=-1)
image = self.scale_image(image, scale_factor, img_size)
cv2.imwrite(image_path, image)
def vis_paf(self, inputs, ori_img, name='default', vis_dir=PAF_DIR):
vis_dir = os.path.join(self.configer.get('project_dir'), vis_dir)
if not os.path.exists(vis_dir):
Log.error('Dir:{} not exists!'.format(vis_dir))
os.makedirs(vis_dir)
if not isinstance(inputs, np.ndarray):
if len(inputs.size()) != 3:
Log.error('Heatmap size is not valid.')
exit(1)
inputs = inputs.data.squeeze().cpu().numpy().transpose(1, 2, 0)
if not isinstance(ori_img, np.ndarray):
if len(ori_img.size()) != 3:
Log.error('Heatmap size is not valid.')
exit(1)
ori_img = DeNormalize(mean=[128.0, 128.0, 128.0],
std=[256.0, 256.0, 256.0])(ori_img)
ori_img = ori_img.data.cpu().squeeze().numpy().transpose(
1, 2, 0).astype(np.uint8)
if inputs.shape[0] != ori_img.shape[0]:
resize_width = (inputs.shape[1] + ori_img.shape[1]) // 2
resize_height = (inputs.shape[0] + ori_img.shape[0]) // 2
inputs = cv2.resize(inputs, (resize_width, resize_height),
interpolation=cv2.INTER_CUBIC)
ori_img = cv2.resize(ori_img, (resize_width, resize_height),
interpolation=cv2.INTER_CUBIC)
for i in range(1):
U = inputs[:, :, 2 * i] * -1
V = inputs[:, :, 2 * i + 1]
X, Y = np.meshgrid(np.arange(U.shape[1]), np.arange(U.shape[0]))
M = np.zeros(U.shape, dtype='bool')
M[U**2 + V**2 < 0.5 * 0.5] = True
U = ma.masked_array(U, mask=M)
V = ma.masked_array(V, mask=M)
# 1
plt.figure()
plt.imshow(ori_img + 0.5, alpha=.5)
s = 5
Q = plt.quiver(X[::s, ::s],
Y[::s, ::s],
U[::s, ::s],
V[::s, ::s],
scale=50,
headaxislength=4,
alpha=.5,
width=0.001,
color='r')
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(10, 10)
plt.savefig(os.path.join(vis_dir, '{}_{}.jpg'.format(name, i)))
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 vis_peaks(self,
heatmap_in,
ori_img_in,
name='default',
sub_dir='peaks'):
base_dir = os.path.join(self.configer.get('project_dir'), POSE_DIR,
sub_dir)
if not os.path.exists(base_dir):
Log.error('Dir:{} not exists!'.format(base_dir))
os.makedirs(base_dir)
if not isinstance(heatmap_in, np.ndarray):
if len(heatmap_in.size()) != 3:
Log.error('Heatmap size is not valid.')
exit(1)
heatmap = heatmap_in.clone().data.cpu().numpy().transpose(1, 2, 0)
else:
heatmap = heatmap_in.copy()
if not isinstance(ori_img_in, np.ndarray):
ori_img = DeNormalize(
mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params',
'std'))(ori_img_in.clone())
ori_img = ori_img.data.cpu().squeeze().numpy().transpose(
1, 2, 0).astype(np.uint8)
ori_img = cv2.cvtColor(ori_img, cv2.COLOR_RGB2BGR)
else:
ori_img = ori_img_in.copy()
for j in range(self.configer.get('data', 'num_keypoints')):
peaks = self.__get_peaks(heatmap[:, :, j])
for peak in peaks:
ori_img = cv2.circle(ori_img, (peak[0], peak[1]),
self.configer.get('vis', 'circle_radius'),
self.configer.get('details',
'color_list')[j],
thickness=-1)
ori_img = cv2.resize(
ori_img, tuple(self.configer.get('data', 'input_size')))
cv2.imwrite(os.path.join(base_dir, '{}_{}.jpg'.format(name, j)),
ori_img)
def vis_img(self, image_in, name='default', sub_dir='images'):
base_dir = os.path.join(self.configer.get('project_dir'), TENSOR_DIR,
sub_dir)
if not isinstance(image_in, np.ndarray):
if len(image_in.size()) != 3:
Log.error('Image size is not valid.')
exit(1)
image = DeNormalize(mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params',
'std'))(image_in.clone())
image = image.data.cpu().numpy().transpose(1, 2, 0)
else:
image = image_in.copy()
if not os.path.exists(base_dir):
Log.error('Dir:{} not exists!'.format(base_dir))
os.makedirs(base_dir)
img = cv2.resize(image, tuple(self.configer.get('data', 'input_size')))
cv2.imwrite(img, os.path.join(base_dir, '{}.jpg'.format(name)))
