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batched detection

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galthran-wq 2025-02-12 09:43:18 +00:00
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1 changed files with 151 additions and 134 deletions
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deepface/modules/detection.py
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@ -19,7 +19,7 @@ logger = Logger()
def extract_faces( def extract_faces(
img_path: Union[str, np.ndarray, IO[bytes]], img_path: Union[List[Union[str, np.ndarray, IO[bytes]]], str, np.ndarray, IO[bytes]],
detector_backend: str = "opencv", detector_backend: str = "opencv",
enforce_detection: bool = True, enforce_detection: bool = True,
align: bool = True, align: bool = True,
@ -31,10 +31,10 @@ def extract_faces(
max_faces: Optional[int] = None, max_faces: Optional[int] = None,
) -> List[Dict[str, Any]]: ) -> List[Dict[str, Any]]:
""" """
Extract faces from a given image Extract faces from a given image or list of images
Args: Args:
img_path (str or np.ndarray or IO[bytes]): Path to the first image. Accepts exact image path 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
as a string, numpy array (BGR), a file object that supports at least `.read` and is as a string, numpy array (BGR), a file object that supports at least `.read` and is
opened in binary mode, or base64 encoded images. opened in binary mode, or base64 encoded images.
@ -80,135 +80,140 @@ def extract_faces(
just available in the result only if anti_spoofing is set to True in input arguments. just available in the result only if anti_spoofing is set to True in input arguments.
""" """
resp_objs = [] if not isinstance(img_path, list):
img_path = [img_path]
# img might be path, base64 or numpy array. Convert it to numpy whatever it is. all_images = []
img, img_name = image_utils.load_image(img_path) img_names = []
if img is None: for single_img_path in img_path:
raise ValueError(f"Exception while loading {img_name}") # img might be path, base64 or numpy array. Convert it to numpy whatever it is.
img, img_name = image_utils.load_image(single_img_path)
height, width, _ = img.shape if img is None:
raise ValueError(f"Exception while loading {img_name}")
base_region = FacialAreaRegion(x=0, y=0, w=width, h=height, confidence=0) all_images.append(img)
img_names.append(img_name)
if detector_backend == "skip": # Run detect_faces for all images at once
face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)] all_face_objs = detect_faces(
else: detector_backend=detector_backend,
face_objs = detect_faces( img=all_images,
detector_backend=detector_backend, align=align,
img=img, expand_percentage=expand_percentage,
align=align, max_faces=max_faces,
expand_percentage=expand_percentage, )
max_faces=max_faces,
)
# in case of no face found if len(all_images) == 1:
if len(face_objs) == 0 and enforce_detection is True: all_face_objs = [all_face_objs]
if img_name is not None:
raise ValueError(
f"Face could not be detected in {img_name}."
"Please confirm that the picture is a face photo "
"or consider to set enforce_detection param to False."
)
else:
raise ValueError(
"Face could not be detected. Please confirm that the picture is a face photo "
"or consider to set enforce_detection param to False."
)
if len(face_objs) == 0 and enforce_detection is False: all_resp_objs = []
face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
for face_obj in face_objs: for img, img_name, face_objs in zip(all_images, img_names, all_face_objs):
current_img = face_obj.img height, width, _ = img.shape
current_region = face_obj.facial_area
if current_img.shape[0] == 0 or current_img.shape[1] == 0: if len(face_objs) == 0 and enforce_detection is True:
continue if img_name is not None:
raise ValueError(
f"Face could not be detected in {img_name}."
"Please confirm that the picture is a face photo "
"or consider to set enforce_detection param to False."
)
else:
raise ValueError(
"Face could not be detected. Please confirm that the picture is a face photo "
"or consider to set enforce_detection param to False."
)
if grayscale is True: if len(face_objs) == 0 and enforce_detection is False:
logger.warn("Parameter grayscale is deprecated. Use color_face instead.") base_region = FacialAreaRegion(x=0, y=0, w=width, h=height, confidence=0)
current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY) face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
else:
if color_face == "rgb": for face_obj in face_objs:
current_img = current_img[:, :, ::-1] current_img = face_obj.img
elif color_face == "bgr": current_region = face_obj.facial_area
pass # image is in BGR
elif color_face == "gray": if current_img.shape[0] == 0 or current_img.shape[1] == 0:
continue
if grayscale is True:
logger.warn("Parameter grayscale is deprecated. Use color_face instead.")
current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY) current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
else: else:
raise ValueError(f"The color_face can be rgb, bgr or gray, but it is {color_face}.") if color_face == "rgb":
current_img = current_img[:, :, ::-1]
elif color_face == "bgr":
pass # image is in BGR
elif color_face == "gray":
current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
else:
raise ValueError(f"The color_face can be rgb, bgr or gray, but it is {color_face}.")
if normalize_face: if normalize_face:
current_img = current_img / 255 # normalize input in [0, 1] current_img = current_img / 255 # normalize input in [0, 1]
# cast to int for flask, and do final checks for borders # cast to int for flask, and do final checks for borders
x = max(0, int(current_region.x)) x = max(0, int(current_region.x))
y = max(0, int(current_region.y)) y = max(0, int(current_region.y))
w = min(width - x - 1, int(current_region.w)) w = min(width - x - 1, int(current_region.w))
h = min(height - y - 1, int(current_region.h)) h = min(height - y - 1, int(current_region.h))
facial_area = { facial_area = {
"x": x, "x": x,
"y": y, "y": y,
"w": w, "w": w,
"h": h, "h": h,
"left_eye": current_region.left_eye, "left_eye": current_region.left_eye,
"right_eye": current_region.right_eye, "right_eye": current_region.right_eye,
} }
# optional nose, mouth_left and mouth_right fields are coming just for retinaface # optional nose, mouth_left and mouth_right fields are coming just for retinaface
if current_region.nose is not None: if current_region.nose is not None:
facial_area["nose"] = current_region.nose facial_area["nose"] = current_region.nose
if current_region.mouth_left is not None: if current_region.mouth_left is not None:
facial_area["mouth_left"] = current_region.mouth_left facial_area["mouth_left"] = current_region.mouth_left
if current_region.mouth_right is not None: if current_region.mouth_right is not None:
facial_area["mouth_right"] = current_region.mouth_right facial_area["mouth_right"] = current_region.mouth_right
resp_obj = { resp_obj = {
"face": current_img, "face": current_img,
"facial_area": facial_area, "facial_area": facial_area,
"confidence": round(float(current_region.confidence or 0), 2), "confidence": round(float(current_region.confidence or 0), 2),
} }
if anti_spoofing is True: if anti_spoofing is True:
antispoof_model = modeling.build_model(task="spoofing", model_name="Fasnet") antispoof_model = modeling.build_model(task="spoofing", model_name="Fasnet")
is_real, antispoof_score = antispoof_model.analyze(img=img, facial_area=(x, y, w, h)) is_real, antispoof_score = antispoof_model.analyze(img=img, facial_area=(x, y, w, h))
resp_obj["is_real"] = is_real resp_obj["is_real"] = is_real
resp_obj["antispoof_score"] = antispoof_score resp_obj["antispoof_score"] = antispoof_score
resp_objs.append(resp_obj) all_resp_objs.append(resp_obj)
if len(resp_objs) == 0 and enforce_detection == True: return all_resp_objs
raise ValueError(
f"Exception while extracting faces from {img_name}."
"Consider to set enforce_detection arg to False."
)
return resp_objs
def detect_faces( def detect_faces(
detector_backend: str, detector_backend: str,
img: np.ndarray, img: Union[np.ndarray, List[np.ndarray]],
align: bool = True, align: bool = True,
expand_percentage: int = 0, expand_percentage: int = 0,
max_faces: Optional[int] = None, max_faces: Optional[int] = None,
) -> List[DetectedFace]: ) -> Union[List[List[DetectedFace]], List[DetectedFace]]:
""" """
Detect face(s) from a given image Detect face(s) from a given image or list of images
Args: Args:
detector_backend (str): detector name detector_backend (str): detector name
img (np.ndarray): pre-loaded image img (np.ndarray or List[np.ndarray]): pre-loaded image or list of images
align (bool): enable or disable alignment after detection align (bool): enable or disable alignment after detection
expand_percentage (int): expand detected facial area with a percentage (default is 0). expand_percentage (int): expand detected facial area with a percentage (default is 0).
Returns: Returns:
results (List[DetectedFace]): A list of DetectedFace objects results (Union[List[List[DetectedFace]], List[DetectedFace]]):
A list of lists of DetectedFace objects or a list of DetectedFace objects
where each object contains: where each object contains:
- img (np.ndarray): The detected face as a NumPy array. - img (np.ndarray): The detected face as a NumPy array.
@ -219,53 +224,65 @@ def detect_faces(
- confidence (float): The confidence score associated with the detected face. - confidence (float): The confidence score associated with the detected face.
""" """
height, width, _ = img.shape if not isinstance(img, list):
img = [img]
face_detector: Detector = modeling.build_model( face_detector: Detector = modeling.build_model(
task="face_detector", model_name=detector_backend task="face_detector", model_name=detector_backend
) )
all_detected_faces = []
# validate expand percentage score for single_img in img:
if expand_percentage < 0: height, width, _ = single_img.shape
logger.warn(
f"Expand percentage cannot be negative but you set it to {expand_percentage}."
"Overwritten it to 0."
)
expand_percentage = 0
# If faces are close to the upper boundary, alignment move them outside # validate expand percentage score
# Add a black border around an image to avoid this. if expand_percentage < 0:
height_border = int(0.5 * height) logger.warn(
width_border = int(0.5 * width) f"Expand percentage cannot be negative but you set it to {expand_percentage}."
if align is True: "Overwritten it to 0."
img = cv2.copyMakeBorder( )
img, expand_percentage = 0
height_border,
height_border,
width_border,
width_border,
cv2.BORDER_CONSTANT,
value=[0, 0, 0], # Color of the border (black)
)
# find facial areas of given image # If faces are close to the upper boundary, alignment move them outside
facial_areas = face_detector.detect_faces(img) # Add a black border around an image to avoid this.
height_border = int(0.5 * height)
width_border = int(0.5 * width)
if align is True:
single_img = cv2.copyMakeBorder(
single_img,
height_border,
height_border,
width_border,
width_border,
cv2.BORDER_CONSTANT,
value=[0, 0, 0], # Color of the border (black)
)
if max_faces is not None and max_faces < len(facial_areas): # find facial areas of given image
facial_areas = nlargest( facial_areas = face_detector.detect_faces(single_img)
max_faces, facial_areas, key=lambda facial_area: facial_area.w * facial_area.h
)
return [ if max_faces is not None and max_faces < len(facial_areas):
extract_face( facial_areas = nlargest(
facial_area=facial_area, max_faces, facial_areas, key=lambda facial_area: facial_area.w * facial_area.h
img=img, )
align=align,
expand_percentage=expand_percentage, detected_faces = [
width_border=width_border, extract_face(
height_border=height_border, facial_area=facial_area,
) img=single_img,
for facial_area in facial_areas align=align,
] expand_percentage=expand_percentage,
width_border=width_border,
height_border=height_border,
)
for facial_area in facial_areas
]
all_detected_faces.append(detected_faces)
if len(all_detected_faces) == 1:
return all_detected_faces[0]
return all_detected_faces
def extract_face( def extract_face(