def __getitem__(self, index):
img = ImageHelper.read_image(
self.item_list[index][0],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
ori_img_size = ImageHelper.get_size(img)
if self.aug_transform is not None:
img = self.aug_transform(img)
border_size = ImageHelper.get_size(img)
if self.img_transform is not None:
img = self.img_transform(img)
meta = dict(ori_img_size=ori_img_size,
border_size=border_size,
img_path=self.item_list[index][0],
filename=self.item_list[index][1])
return dict(img=DataContainer(img,
stack=True,
return_dc=True,
samples_per_gpu=True),
meta=DataContainer(meta,
stack=False,
cpu_only=True,
return_dc=True,
samples_per_gpu=True))
def __test_img(self, image_path, save_path):
Log.info('Image Path: {}'.format(image_path))
ori_image = ImageHelper.read_image(image_path,
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
ori_width, ori_height = ImageHelper.get_size(ori_image)
ori_img_bgr = ImageHelper.get_cv2_bgr(ori_image, mode=self.configer.get('data', 'input_mode'))
heatmap_avg = np.zeros((ori_height, ori_width, self.configer.get('network', 'heatmap_out')))
for i, scale in enumerate(self.configer.get('test', 'scale_search')):
image = self.blob_helper.make_input(ori_image,
input_size=self.configer.get('test', 'input_size'),
scale=scale)
with torch.no_grad():
heatmap_out_list = self.pose_net(image)
heatmap_out = heatmap_out_list[-1]
# extract outputs, resize, and remove padding
heatmap = heatmap_out.squeeze(0).cpu().numpy().transpose(1, 2, 0)
heatmap = cv2.resize(heatmap, (ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
heatmap_avg = heatmap_avg + heatmap / len(self.configer.get('test', 'scale_search'))
all_peaks = self.__extract_heatmap_info(heatmap_avg)
image_canvas = self.__draw_key_point(all_peaks, ori_img_bgr)
ImageHelper.save(image_canvas, save_path)
def __getitem__(self, index):
img = ImageHelper.read_image(
self.img_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
img_size = ImageHelper.get_size(img)
bboxes, labels = self.__read_json_file(self.json_list[index])
ori_bboxes, ori_labels = bboxes.copy(), labels.copy()
if self.aug_transform is not None:
img, bboxes, labels = self.aug_transform(img,
bboxes=bboxes,
labels=labels)
img_scale = ImageHelper.get_size(img)[0] / img_size[0]
labels = torch.from_numpy(labels).long()
bboxes = torch.from_numpy(bboxes).float()
meta = dict(ori_img_size=img_size,
border_size=ImageHelper.get_size(img),
img_scale=img_scale,
ori_bboxes=torch.from_numpy(ori_bboxes).float(),
ori_labels=torch.from_numpy(ori_labels).long())
if self.img_transform is not None:
img = self.img_transform(img)
return dict(img=DataContainer(img,
stack=True,
return_dc=True,
samples_per_gpu=True),
bboxes=DataContainer(bboxes,
stack=False,
return_dc=True,
samples_per_gpu=True),
labels=DataContainer(labels,
stack=False,
return_dc=True,
samples_per_gpu=True),
meta=DataContainer(meta,
stack=False,
cpu_only=True,
return_dc=True,
samples_per_gpu=True))
def __getitem__(self, index):
img = ImageHelper.read_image(
self.img_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
img_size = ImageHelper.get_size(img)
labelmap = ImageHelper.read_image(self.label_list[index],
tool=self.configer.get(
'data', 'image_tool'),
mode='P')
if self.configer.get('data.label_list', default=None):
labelmap = self._encode_label(labelmap)
if self.configer.get('data.reduce_zero_label', default=None):
labelmap = self._reduce_zero_label(labelmap)
ori_target = ImageHelper.to_np(labelmap)
if self.aug_transform is not None:
img, labelmap = self.aug_transform(img, labelmap=labelmap)
border_size = ImageHelper.get_size(img)
if self.img_transform is not None:
img = self.img_transform(img)
if self.label_transform is not None:
labelmap = self.label_transform(labelmap)
meta = dict(ori_img_wh=img_size,
border_wh=border_size,
ori_target=ori_target)
return dict(
img=DataContainer(img, stack=True),
labelmap=DataContainer(labelmap, stack=True),
meta=DataContainer(meta, stack=False, cpu_only=True),
)
def __getitem__(self, index):
img = ImageHelper.read_image(
self.img_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
if os.path.exists(self.mask_list[index]):
maskmap = ImageHelper.read_image(self.mask_list[index],
tool=self.configer.get(
'data', 'image_tool'),
mode='P')
else:
maskmap = np.ones((img.size[1], img.size[0]), dtype=np.uint8)
if self.configer.get('data', 'image_tool') == 'pil':
maskmap = ImageHelper.to_img(maskmap)
kpts, bboxes = self.__read_json_file(self.json_list[index])
if self.aug_transform is not None and len(bboxes) > 0:
img, maskmap, kpts, bboxes = self.aug_transform(img,
maskmap=maskmap,
kpts=kpts,
bboxes=bboxes)
elif self.aug_transform is not None:
img, maskmap, kpts = self.aug_transform(img,
maskmap=maskmap,
kpts=kpts)
width, height = ImageHelper.get_size(maskmap)
maskmap = ImageHelper.resize(
maskmap, (width // self.configer.get('network', 'stride'),
height // self.configer.get('network', 'stride')),
interpolation='nearest')
maskmap = torch.from_numpy(np.array(maskmap, dtype=np.float32))
maskmap = maskmap.unsqueeze(0)
heatmap = self.heatmap_generator(kpts, [width, height], maskmap)
vecmap = self.paf_generator(kpts, [width, height], maskmap)
if self.img_transform is not None:
img = self.img_transform(img)
meta = dict(kpts=kpts, )
return dict(
img=DataContainer(img, stack=True),
heatmap=DataContainer(heatmap, stack=True),
maskmap=DataContainer(maskmap, stack=True),
vecmap=DataContainer(vecmap, stack=True),
meta=DataContainer(meta, stack=False, cpu_only=True),
)
def __test_img(self, image_path, json_path, raw_path, vis_path):
Log.info('Image Path: {}'.format(image_path))
ori_image = ImageHelper.read_image(image_path,
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
ori_width, ori_height = ImageHelper.get_size(ori_image)
ori_img_bgr = ImageHelper.get_cv2_bgr(ori_image, mode=self.configer.get('data', 'input_mode'))
heatmap_avg = np.zeros((ori_height, ori_width, self.configer.get('network', 'heatmap_out')))
paf_avg = np.zeros((ori_height, ori_width, self.configer.get('network', 'paf_out')))
multiplier = [scale * self.configer.get('test', 'input_size')[1] / ori_height
for scale in self.configer.get('test', 'scale_search')]
stride = self.configer.get('network', 'stride')
for i, scale in enumerate(multiplier):
image, border_hw = self._get_blob(ori_image, scale=scale)
with torch.no_grad():
paf_out_list, heatmap_out_list = self.pose_net(image)
paf_out = paf_out_list[-1]
heatmap_out = heatmap_out_list[-1]
# extract outputs, resize, and remove padding
heatmap = heatmap_out.squeeze(0).cpu().numpy().transpose(1, 2, 0)
heatmap = cv2.resize(heatmap, None, fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
heatmap = cv2.resize(heatmap[:border_hw[0], :border_hw[1]],
(ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
paf = paf_out.squeeze(0).cpu().numpy().transpose(1, 2, 0)
paf = cv2.resize(paf, None, fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
paf = cv2.resize(paf[:border_hw[0], :border_hw[1]],
(ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
heatmap_avg = heatmap_avg + heatmap / len(multiplier)
paf_avg = paf_avg + paf / len(multiplier)
all_peaks = self.__extract_heatmap_info(heatmap_avg)
special_k, connection_all = self.__extract_paf_info(ori_img_bgr, paf_avg, all_peaks)
subset, candidate = self.__get_subsets(connection_all, special_k, all_peaks)
json_dict = self.__get_info_tree(ori_img_bgr, subset, candidate)
image_canvas = self.pose_parser.draw_points(ori_img_bgr.copy(), json_dict)
image_canvas = self.pose_parser.link_points(image_canvas, json_dict)
ImageHelper.save(image_canvas, vis_path)
ImageHelper.save(ori_img_bgr, raw_path)
Log.info('Json Save Path: {}'.format(json_path))
JsonHelper.save_file(json_dict, json_path)
def __getitem__(self, index):
img = ImageHelper.read_image(self.img_list[index],
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
kpts, bboxes = self.__read_json_file(self.json_list[index])
if self.aug_transform is not None:
img, kpts, bboxes = self.aug_transform(img, kpts=kpts, bboxes=bboxes)
heatmap = self.heatmap_generator(kpts, ImageHelper.get_size(img))
if self.img_transform is not None:
img = self.img_transform(img)
return dict(
img=DataContainer(img, stack=True),
heatmap=DataContainer(heatmap, stack=True),
)
