def process_3d(self, data_dir):
new_data_dir = '{}_new'.format(data_dir.rstrip('/'))
if os.path.exists(new_data_dir):
shutil.rmtree(new_data_dir)
os.makedirs(new_data_dir)
for filename in FileHelper.list_dir(data_dir):
if not ImageHelper.is_img(filename) or 'depth' in filename:
Log.info('Image Path: {}'.format(
os.path.join(data_dir, filename)))
continue
file_path = os.path.join(data_dir, filename)
img = io.imread(file_path)
kpts = self.detect_face(img)
if kpts is None:
Log.info('Invliad face detected in {}'.format(file_path))
continue
depth = np.array(
io.imread(
os.path.join(data_dir, filename.replace('rgb', 'depth'))))
face_depth, kpts = self.align_face(
[np.array(img), np.array(depth)], kpts)
if face_depth is None:
Log.info('Invliad face detected in {}'.format(file_path))
continue
ImageHelper.save(ImageHelper.rgb2bgr(face_depth[0]),
os.path.join(new_data_dir, filename))
ImageHelper.save(
ImageHelper.rgb2bgr(face_depth[1]),
os.path.join(new_data_dir, filename.replace('rgb', 'depth')))
def vis_bboxes(self,
image_in,
bboxes_list,
name='default',
sub_dir='bbox'):
"""
Show the diff bbox of individuals.
"""
base_dir = os.path.join(self.configer.get('project_dir'), DET_DIR,
sub_dir)
if isinstance(image_in, Image.Image):
image = ImageHelper.rgb2bgr(ImageHelper.to_np(image_in))
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_path = os.path.join(
base_dir,
name if ImageHelper.is_img(name) else '{}.jpg'.format(name))
for bbox in bboxes_list:
image = cv2.rectangle(image, (bbox[0], bbox[1]),
(bbox[2], bbox[3]), (0, 255, 0), 2)
cv2.imwrite(img_path, image)
def __test_img(self, image_path, json_path, raw_path, vis_path):
Log.info('Image Path: {}'.format(image_path))
ori_img_rgb = ImageHelper.img2np(ImageHelper.pil_open_rgb(image_path))
ori_img_bgr = ImageHelper.rgb2bgr(ori_img_rgb)
inputs = ImageHelper.resize(ori_img_rgb, tuple(self.configer.get('data', 'input_size')), Image.CUBIC)
inputs = ToTensor()(inputs)
inputs = Normalize(mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params', 'std'))(inputs)
with torch.no_grad():
inputs = inputs.unsqueeze(0).to(self.device)
bbox, cls = self.det_net(inputs)
bbox = bbox.cpu().data.squeeze(0)
cls = F.softmax(cls.cpu().squeeze(0), dim=-1).data
boxes, lbls, scores = self.__decode(bbox, cls)
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(vis_path, image_canvas)
cv2.imwrite(raw_path, ori_img_bgr)
Log.info('Json Path: {}'.format(json_path))
JsonHelper.save_file(json_dict, json_path)
return json_dict
def inference(self, image_rgb):
image_bgr = ImageHelper.rgb2bgr(image_rgb)
paf_avg, heatmap_avg = self.__get_paf_and_heatmap(image_rgb)
all_peaks = self.__extract_heatmap_info(heatmap_avg)
special_k, connection_all = self.__extract_paf_info(image_rgb, 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)
return json_dict
def process(self, data_dir):
new_data_dir = '{}_new'.format(data_dir.rstrip('/'))
if os.path.exists(new_data_dir):
shutil.rmtree(new_data_dir)
os.makedirs(new_data_dir)
for filename in FileHelper.list_dir(data_dir):
if not ImageHelper.is_img(filename):
Log.info('Image Path: {}'.format(
os.path.join(data_dir, filename)))
continue
file_path = os.path.join(data_dir, filename)
img = io.imread(file_path)
kpts = self.detect_face(img)
if kpts is None:
Log.info('Invliad face detected in {}'.format(file_path))
continue
face, kpts = self.align_face(img, kpts)
cv2.imwrite(os.path.join(new_data_dir, filename),
ImageHelper.rgb2bgr(face))
