def detect_image(model, file):
image = common.imread(file)
objs = detect(model, image)
for obj in objs:
common.drawbbox(image, obj)
common.imwrite("detect_result/" + common.file_name_no_suffix(file) + ".draw.jpg", image)
def detect_image_list(image_list: typing.List,
output_dir,
align_face: bool = False):
if output_dir != "" and not os.path.exists(output_dir):
raise ValueError(f"output folder ({output_dir}) is not existed")
ret = {}
for image_file_name in image_list:
print(f"detect for {image_file_name}")
output_file = ""
if output_dir != "":
base_name = common.file_name_no_suffix(image_file_name)
output_file = f"{output_dir}/{base_name}.draw.jpg"
face_info = do_detect_image(image_file_name, output_file, align_face)
ret[image_file_name] = face_info
return ret
def _do_align_face(raw_image: np.array, face_infos: List[common.BBox],
output_filename: str):
for inx, face_info in enumerate(face_infos):
if not face_info:
continue
info = face_info.json
landmark = info["landmark"]
bbox = info["bbox"]
aligned_face_image = align_face(raw_image, landmark, bbox)
if output_filename == "":
out_face_file = f"/tmp/aligned_face_{inx}.jpg"
else:
no_suffix_name = common.file_name_no_suffix(output_filename)
base_dir = os.path.dirname(output_filename)
out_face_file = f"{base_dir}/{no_suffix_name}_aligned_face_{inx}.jpg"
cv2.imwrite(out_face_file, aligned_face_image)
with open(out_face_file, "rb") as image_file:
encoded_image = base64.b64encode(image_file.read())
face_encode_str = encoded_image.decode('utf-8')
face_info.aligned_face = face_encode_str
return face_infos
mean = [0.408, 0.447, 0.47]
std = [0.289, 0.274, 0.278]
files, anns = zip(
*common.load_webface("webface/val/label.txt", "webface/WIDER_val/images"))
# forward and summary
prefix = "webface/WIDER_val/images/"
all_result_dict = {}
total_file = len(files)
for i in range(total_file):
# preper key and file_name
file = files[i]
key = file[len(prefix):file.rfind("/")]
file_name = common.file_name_no_suffix(file)
# load image and forward
image = common.imread(file)
objs = eval_tool.detect_image(model, image, mean, std, 0.01)
# summary to all_result_dict
image_pred = []
for obj in objs:
image_pred.append(obj.xywh + [obj.score])
# build all_result_dict
if key not in all_result_dict:
all_result_dict[key] = {}
all_result_dict[key][file_name] = np.array(image_pred)
