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https://github.com/serengil/deepface.git
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batched detection
This commit is contained in:
galthran-wq
parent
72e82f0605
commit
f4d18a70c0
@ -19,7 +19,7 @@ logger = Logger()
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def extract_faces(
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img_path: Union[str, np.ndarray, IO[bytes]],
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img_path: Union[List[Union[str, np.ndarray, IO[bytes]]], str, np.ndarray, IO[bytes]],
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detector_backend: str = "opencv",
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enforce_detection: bool = True,
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align: bool = True,
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@ -31,10 +31,10 @@ def extract_faces(
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max_faces: Optional[int] = None,
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) -> List[Dict[str, Any]]:
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"""
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Extract faces from a given image
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Extract faces from a given image or list of images
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Args:
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img_path (str or np.ndarray or IO[bytes]): Path to the first image. Accepts exact image path
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img_paths (List[str or np.ndarray or IO[bytes]] or str or np.ndarray or IO[bytes]): Path(s) to the image(s). Accepts exact image path
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as a string, numpy array (BGR), a file object that supports at least `.read` and is
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opened in binary mode, or base64 encoded images.
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@ -80,135 +80,140 @@ def extract_faces(
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just available in the result only if anti_spoofing is set to True in input arguments.
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"""
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resp_objs = []
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if not isinstance(img_path, list):
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img_path = [img_path]
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# img might be path, base64 or numpy array. Convert it to numpy whatever it is.
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img, img_name = image_utils.load_image(img_path)
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all_images = []
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img_names = []
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if img is None:
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raise ValueError(f"Exception while loading {img_name}")
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for single_img_path in img_path:
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# img might be path, base64 or numpy array. Convert it to numpy whatever it is.
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img, img_name = image_utils.load_image(single_img_path)
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height, width, _ = img.shape
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if img is None:
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raise ValueError(f"Exception while loading {img_name}")
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base_region = FacialAreaRegion(x=0, y=0, w=width, h=height, confidence=0)
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all_images.append(img)
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img_names.append(img_name)
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if detector_backend == "skip":
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face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
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else:
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face_objs = detect_faces(
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detector_backend=detector_backend,
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img=img,
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align=align,
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expand_percentage=expand_percentage,
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max_faces=max_faces,
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)
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# Run detect_faces for all images at once
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all_face_objs = detect_faces(
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detector_backend=detector_backend,
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img=all_images,
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align=align,
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expand_percentage=expand_percentage,
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max_faces=max_faces,
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)
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# in case of no face found
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if len(face_objs) == 0 and enforce_detection is True:
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if img_name is not None:
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raise ValueError(
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f"Face could not be detected in {img_name}."
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"Please confirm that the picture is a face photo "
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"or consider to set enforce_detection param to False."
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)
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else:
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raise ValueError(
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"Face could not be detected. Please confirm that the picture is a face photo "
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"or consider to set enforce_detection param to False."
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)
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if len(all_images) == 1:
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all_face_objs = [all_face_objs]
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if len(face_objs) == 0 and enforce_detection is False:
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face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
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all_resp_objs = []
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for face_obj in face_objs:
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current_img = face_obj.img
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current_region = face_obj.facial_area
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for img, img_name, face_objs in zip(all_images, img_names, all_face_objs):
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height, width, _ = img.shape
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if current_img.shape[0] == 0 or current_img.shape[1] == 0:
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continue
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if len(face_objs) == 0 and enforce_detection is True:
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if img_name is not None:
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raise ValueError(
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f"Face could not be detected in {img_name}."
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"Please confirm that the picture is a face photo "
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"or consider to set enforce_detection param to False."
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)
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else:
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raise ValueError(
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"Face could not be detected. Please confirm that the picture is a face photo "
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"or consider to set enforce_detection param to False."
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)
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if grayscale is True:
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logger.warn("Parameter grayscale is deprecated. Use color_face instead.")
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current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
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else:
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if color_face == "rgb":
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current_img = current_img[:, :, ::-1]
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elif color_face == "bgr":
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pass # image is in BGR
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elif color_face == "gray":
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if len(face_objs) == 0 and enforce_detection is False:
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base_region = FacialAreaRegion(x=0, y=0, w=width, h=height, confidence=0)
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face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
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for face_obj in face_objs:
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current_img = face_obj.img
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current_region = face_obj.facial_area
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if current_img.shape[0] == 0 or current_img.shape[1] == 0:
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continue
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if grayscale is True:
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logger.warn("Parameter grayscale is deprecated. Use color_face instead.")
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current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
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else:
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raise ValueError(f"The color_face can be rgb, bgr or gray, but it is {color_face}.")
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if color_face == "rgb":
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current_img = current_img[:, :, ::-1]
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elif color_face == "bgr":
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pass # image is in BGR
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elif color_face == "gray":
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current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
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else:
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raise ValueError(f"The color_face can be rgb, bgr or gray, but it is {color_face}.")
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if normalize_face:
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current_img = current_img / 255 # normalize input in [0, 1]
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if normalize_face:
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current_img = current_img / 255 # normalize input in [0, 1]
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# cast to int for flask, and do final checks for borders
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x = max(0, int(current_region.x))
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y = max(0, int(current_region.y))
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w = min(width - x - 1, int(current_region.w))
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h = min(height - y - 1, int(current_region.h))
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# cast to int for flask, and do final checks for borders
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x = max(0, int(current_region.x))
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y = max(0, int(current_region.y))
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w = min(width - x - 1, int(current_region.w))
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h = min(height - y - 1, int(current_region.h))
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facial_area = {
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"x": x,
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"y": y,
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"w": w,
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"h": h,
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"left_eye": current_region.left_eye,
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"right_eye": current_region.right_eye,
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}
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facial_area = {
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"x": x,
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"y": y,
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"w": w,
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"h": h,
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"left_eye": current_region.left_eye,
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"right_eye": current_region.right_eye,
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}
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# optional nose, mouth_left and mouth_right fields are coming just for retinaface
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if current_region.nose is not None:
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facial_area["nose"] = current_region.nose
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if current_region.mouth_left is not None:
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facial_area["mouth_left"] = current_region.mouth_left
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if current_region.mouth_right is not None:
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facial_area["mouth_right"] = current_region.mouth_right
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# optional nose, mouth_left and mouth_right fields are coming just for retinaface
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if current_region.nose is not None:
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facial_area["nose"] = current_region.nose
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if current_region.mouth_left is not None:
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facial_area["mouth_left"] = current_region.mouth_left
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if current_region.mouth_right is not None:
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facial_area["mouth_right"] = current_region.mouth_right
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resp_obj = {
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"face": current_img,
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"facial_area": facial_area,
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"confidence": round(float(current_region.confidence or 0), 2),
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}
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resp_obj = {
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"face": current_img,
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"facial_area": facial_area,
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"confidence": round(float(current_region.confidence or 0), 2),
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}
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if anti_spoofing is True:
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antispoof_model = modeling.build_model(task="spoofing", model_name="Fasnet")
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is_real, antispoof_score = antispoof_model.analyze(img=img, facial_area=(x, y, w, h))
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resp_obj["is_real"] = is_real
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resp_obj["antispoof_score"] = antispoof_score
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if anti_spoofing is True:
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antispoof_model = modeling.build_model(task="spoofing", model_name="Fasnet")
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is_real, antispoof_score = antispoof_model.analyze(img=img, facial_area=(x, y, w, h))
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resp_obj["is_real"] = is_real
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resp_obj["antispoof_score"] = antispoof_score
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resp_objs.append(resp_obj)
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all_resp_objs.append(resp_obj)
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if len(resp_objs) == 0 and enforce_detection == True:
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raise ValueError(
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f"Exception while extracting faces from {img_name}."
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"Consider to set enforce_detection arg to False."
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)
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return resp_objs
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return all_resp_objs
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def detect_faces(
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detector_backend: str,
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img: np.ndarray,
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img: Union[np.ndarray, List[np.ndarray]],
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align: bool = True,
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expand_percentage: int = 0,
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max_faces: Optional[int] = None,
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) -> List[DetectedFace]:
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) -> Union[List[List[DetectedFace]], List[DetectedFace]]:
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"""
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Detect face(s) from a given image
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Detect face(s) from a given image or list of images
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Args:
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detector_backend (str): detector name
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img (np.ndarray): pre-loaded image
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img (np.ndarray or List[np.ndarray]): pre-loaded image or list of images
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align (bool): enable or disable alignment after detection
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expand_percentage (int): expand detected facial area with a percentage (default is 0).
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Returns:
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results (List[DetectedFace]): A list of DetectedFace objects
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results (Union[List[List[DetectedFace]], List[DetectedFace]]):
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A list of lists of DetectedFace objects or a list of DetectedFace objects
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where each object contains:
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- img (np.ndarray): The detected face as a NumPy array.
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@ -219,53 +224,65 @@ def detect_faces(
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- confidence (float): The confidence score associated with the detected face.
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"""
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height, width, _ = img.shape
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if not isinstance(img, list):
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img = [img]
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face_detector: Detector = modeling.build_model(
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task="face_detector", model_name=detector_backend
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)
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all_detected_faces = []
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# validate expand percentage score
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if expand_percentage < 0:
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logger.warn(
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f"Expand percentage cannot be negative but you set it to {expand_percentage}."
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"Overwritten it to 0."
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)
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expand_percentage = 0
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for single_img in img:
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height, width, _ = single_img.shape
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# If faces are close to the upper boundary, alignment move them outside
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# Add a black border around an image to avoid this.
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height_border = int(0.5 * height)
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width_border = int(0.5 * width)
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if align is True:
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img = cv2.copyMakeBorder(
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img,
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height_border,
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height_border,
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width_border,
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width_border,
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cv2.BORDER_CONSTANT,
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value=[0, 0, 0], # Color of the border (black)
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)
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# validate expand percentage score
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if expand_percentage < 0:
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logger.warn(
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f"Expand percentage cannot be negative but you set it to {expand_percentage}."
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"Overwritten it to 0."
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)
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expand_percentage = 0
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# find facial areas of given image
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facial_areas = face_detector.detect_faces(img)
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# If faces are close to the upper boundary, alignment move them outside
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# Add a black border around an image to avoid this.
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height_border = int(0.5 * height)
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width_border = int(0.5 * width)
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if align is True:
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single_img = cv2.copyMakeBorder(
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single_img,
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height_border,
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height_border,
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width_border,
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width_border,
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cv2.BORDER_CONSTANT,
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value=[0, 0, 0], # Color of the border (black)
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)
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if max_faces is not None and max_faces < len(facial_areas):
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facial_areas = nlargest(
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max_faces, facial_areas, key=lambda facial_area: facial_area.w * facial_area.h
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)
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# find facial areas of given image
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facial_areas = face_detector.detect_faces(single_img)
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return [
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extract_face(
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facial_area=facial_area,
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img=img,
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align=align,
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expand_percentage=expand_percentage,
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width_border=width_border,
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height_border=height_border,
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)
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for facial_area in facial_areas
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]
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if max_faces is not None and max_faces < len(facial_areas):
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facial_areas = nlargest(
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max_faces, facial_areas, key=lambda facial_area: facial_area.w * facial_area.h
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)
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detected_faces = [
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extract_face(
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facial_area=facial_area,
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img=single_img,
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align=align,
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expand_percentage=expand_percentage,
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width_border=width_border,
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height_border=height_border,
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)
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for facial_area in facial_areas
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]
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all_detected_faces.append(detected_faces)
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if len(all_detected_faces) == 1:
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return all_detected_faces[0]
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return all_detected_faces
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def extract_face(
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