def segmentation(self,
images=None,
paths=None,
data=None,
batch_size=1,
use_gpu=False,
output_dir='ace2p_output',
visualization=False):
"""
API for human parsing.
Args:
images (list[numpy.ndarray]): images data, shape of each is [H, W, C], color space is BGR.
paths (list[str]): The paths of images.
batch_size (int): batch size.
use_gpu (bool): Whether to use gpu.
output_dir (str): The path to store output images.
visualization (bool): Whether to save output images or not.
Returns:
res (list[dict]): The result of human parsing and original path of images.
"""
if use_gpu:
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
except:
raise RuntimeError(
"Attempt to use GPU for prediction, but environment variable CUDA_VISIBLE_DEVICES was not set correctly."
)
# compatibility with older versions
if data and 'image' in data:
if paths is None:
paths = []
paths += data['image']
# get all data
all_data = []
scale = (473, 473) # size of preprocessed image.
rotation = 0 # rotation angle, used for obtaining affine matrix in preprocess.
for yield_data in reader(images, paths, scale, rotation):
all_data.append(yield_data)
total_num = len(all_data)
loop_num = int(np.ceil(total_num / batch_size))
res = []
for iter_id in range(loop_num):
batch_data = list()
handle_id = iter_id * batch_size
for image_id in range(batch_size):
try:
batch_data.append(all_data[handle_id + image_id])
except:
pass
# feed batch image
batch_image = np.array([data['image'] for data in batch_data])
batch_image = PaddleTensor(batch_image.astype('float32'))
data_out = self.gpu_predictor.run([
batch_image
]) if use_gpu else self.cpu_predictor.run([batch_image])
# postprocess one by one
for i in range(len(batch_data)):
out = postprocess(
data_out=data_out[0].as_ndarray()[i],
org_im=batch_data[i]['org_im'],
org_im_path=batch_data[i]['org_im_path'],
image_info=batch_data[i]['image_info'],
output_dir=output_dir,
visualization=visualization,
palette=self.palette)
res.append(out)
return res
def face_detection(self,
images=None,
paths=None,
data=None,
batch_size=1,
use_gpu=False,
output_dir='face_detector_320_predict_output',
visualization=False,
confs_threshold=0.5,
iou_threshold=0.5):
"""
API for face detection.
Args:
images (list(numpy.ndarray)): images data, shape of each is [H, W, C], color space is BGR.
paths (list[str]): The paths of images.
batch_size (int): batch size.
use_gpu (bool): Whether to use gpu.
output_dir (str): The path to store output images.
visualization (bool): Whether to save image or not.
confs_threshold (float): threshold for confidence coefficient.
iou_threshold (float): threshold for iou.
Returns:
res (list[dict()]): The result of face detection and save path of images.
"""
if use_gpu:
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
except:
raise RuntimeError(
"Attempt to use GPU for prediction, but environment variable CUDA_VISIBLE_DEVICES was not set correctly."
)
# compatibility with older versions
if data and 'image' in data:
if paths is None:
paths = []
paths += data['image']
# get all data
all_data = []
for yield_data in reader(images, paths):
all_data.append(yield_data)
total_num = len(all_data)
loop_num = int(np.ceil(total_num / batch_size))
res = []
for iter_id in range(loop_num):
batch_data = list()
handle_id = iter_id * batch_size
for image_id in range(batch_size):
try:
batch_data.append(all_data[handle_id + image_id])
except:
pass
# feed batch image
batch_image = np.array([data['image'] for data in batch_data])
batch_image = PaddleTensor(batch_image.astype('float32'))
data_out = self.gpu_predictor.run([
batch_image
]) if use_gpu else self.cpu_predictor.run([batch_image])
confidences = data_out[0].as_ndarray()
boxes = data_out[1].as_ndarray()
# postprocess one by one
for i in range(len(batch_data)):
out = postprocess(confidences=confidences[i],
boxes=boxes[i],
orig_im=batch_data[i]['orig_im'],
orig_im_shape=batch_data[i]['orig_im_shape'],
orig_im_path=batch_data[i]['orig_im_path'],
output_dir=output_dir,
visualization=visualization,
confs_threshold=confs_threshold,
iou_threshold=iou_threshold)
res.append(out)
return res
