Merge pull request #1302 from serengil/feat-task-0808-refactor-detect…

…ion-module

detection module refactored

deepface/modules/detection.py

@@ -8,8 +8,7 @@
 
 # project dependencies
 from deepface.modules import modeling
-from deepface.models.Detector import DetectedFace, FacialAreaRegion
-from deepface.detectors import DetectorWrapper
+from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
 from deepface.commons import image_utils
 from deepface.commons.logger import Logger
 
@@ -91,7 +90,7 @@ def extract_faces(
  if detector_backend == "skip":
  face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
  else:
- face_objs = DetectorWrapper.detect_faces(
+ face_objs = detect_faces(
  detector_backend=detector_backend,
  img=img,
  align=align,
@@ -173,7 +172,120 @@ def extract_faces(
  return resp_objs
 
 
-def align_face(
+def detect_faces(
+ detector_backend: str, img: np.ndarray, align: bool = True, expand_percentage: int = 0
+) -> List[DetectedFace]:
+ """
+ Detect face(s) from a given image
+ Args:
+ detector_backend (str): detector name
+
+ img (np.ndarray): pre-loaded image
+
+ align (bool): enable or disable alignment after detection
+
+ expand_percentage (int): expand detected facial area with a percentage (default is 0).
+
+ Returns:
+ results (List[DetectedFace]): A list of DetectedFace objects
+ where each object contains:
+
+ - img (np.ndarray): The detected face as a NumPy array.
+
+ - facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h,
+ left_eye and right eye. left eye and right eye are eyes on the left and right
+ with respect to the person instead of observer.
+
+ - confidence (float): The confidence score associated with the detected face.
+ """
+ height, width, _ = img.shape
+
+ face_detector: Detector = modeling.build_model(
+ task="face_detector", model_name=detector_backend
+ )
+
+ # validate expand percentage score
+ if expand_percentage < 0:
+ 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
+ # 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:
+ img = cv2.copyMakeBorder(
+ img,
+ 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
+ facial_areas = face_detector.detect_faces(img)
+
+ results = []
+ for facial_area in facial_areas:
+ x = facial_area.x
+ y = facial_area.y
+ w = facial_area.w
+ h = facial_area.h
+ left_eye = facial_area.left_eye
+ right_eye = facial_area.right_eye
+ confidence = facial_area.confidence
+
+ if expand_percentage > 0:
+ # Expand the facial region height and width by the provided percentage
+ # ensuring that the expanded region stays within img.shape limits
+ expanded_w = w + int(w * expand_percentage / 100)
+ expanded_h = h + int(h * expand_percentage / 100)
+
+ x = max(0, x - int((expanded_w - w) / 2))
+ y = max(0, y - int((expanded_h - h) / 2))
+ w = min(img.shape[1] - x, expanded_w)
+ h = min(img.shape[0] - y, expanded_h)
+
+ # extract detected face unaligned
+ detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
+
+ # align original image, then find projection of detected face area after alignment
+ if align is True: # and left_eye is not None and right_eye is not None:
+ aligned_img, angle = align_img_wrt_eyes(img=img, left_eye=left_eye, right_eye=right_eye)
+
+ rotated_x1, rotated_y1, rotated_x2, rotated_y2 = project_facial_area(
+ facial_area=(x, y, x + w, y + h), angle=angle, size=(img.shape[0], img.shape[1])
+ )
+ detected_face = aligned_img[
+ int(rotated_y1) : int(rotated_y2), int(rotated_x1) : int(rotated_x2)
+ ]
+
+ # restore x, y, le and re before border added
+ x = x - width_border
+ y = y - height_border
+ # w and h will not change
+ if left_eye is not None:
+ left_eye = (left_eye[0] - width_border, left_eye[1] - height_border)
+ if right_eye is not None:
+ right_eye = (right_eye[0] - width_border, right_eye[1] - height_border)
+
+ result = DetectedFace(
+ img=detected_face,
+ facial_area=FacialAreaRegion(
+ x=x, y=y, h=h, w=w, confidence=confidence, left_eye=left_eye, right_eye=right_eye
+ ),
+ confidence=confidence,
+ )
+ results.append(result)
+ return results
+
+
+def align_img_wrt_eyes(
  img: np.ndarray,
  left_eye: Union[list, tuple],
  right_eye: Union[list, tuple],
@@ -198,3 +310,64 @@ def align_face(
  angle = float(np.degrees(np.arctan2(left_eye[1] - right_eye[1], left_eye[0] - right_eye[0])))
  img = np.array(Image.fromarray(img).rotate(angle, resample=Image.BICUBIC))
  return img, angle
+
+
+def project_facial_area(
+ facial_area: Tuple[int, int, int, int], angle: float, size: Tuple[int, int]
+) -> Tuple[int, int, int, int]:
+ """
+ Update pre-calculated facial area coordinates after image itself
+ rotated with respect to the eyes.
+ Inspried from the work of @UmutDeniz26 - github.com/serengil/retinaface/pull/80
+
+ Args:
+ facial_area (tuple of int): Representing the (x1, y1, x2, y2) of the facial area.
+ x2 is equal to x1 + w1, and y2 is equal to y1 + h1
+ angle (float): Angle of rotation in degrees. Its sign determines the direction of rotation.
+ Note that angles > 360 degrees are normalized to the range [0, 360).
+ size (tuple of int): Tuple representing the size of the image (width, height).
+
+ Returns:
+ rotated_coordinates (tuple of int): Representing the new coordinates
+ (x1, y1, x2, y2) or (x1, y1, x1+w1, y1+h1) of the rotated facial area.
+ """
+
+ # Normalize the witdh of the angle so we don't have to
+ # worry about rotations greater than 360 degrees.
+ # We workaround the quirky behavior of the modulo operator
+ # for negative angle values.
+ direction = 1 if angle >= 0 else -1
+ angle = abs(angle) % 360
+ if angle == 0:
+ return facial_area
+
+ # Angle in radians
+ angle = angle * np.pi / 180
+
+ height, weight = size
+
+ # Translate the facial area to the center of the image
+ x = (facial_area[0] + facial_area[2]) / 2 - weight / 2
+ y = (facial_area[1] + facial_area[3]) / 2 - height / 2
+
+ # Rotate the facial area
+ x_new = x * np.cos(angle) + y * direction * np.sin(angle)
+ y_new = -x * direction * np.sin(angle) + y * np.cos(angle)
+
+ # Translate the facial area back to the original position
+ x_new = x_new + weight / 2
+ y_new = y_new + height / 2
+
+ # Calculate projected coordinates after alignment
+ x1 = x_new - (facial_area[2] - facial_area[0]) / 2
+ y1 = y_new - (facial_area[3] - facial_area[1]) / 2
+ x2 = x_new + (facial_area[2] - facial_area[0]) / 2
+ y2 = y_new + (facial_area[3] - facial_area[1]) / 2
+
+ # validate projected coordinates are in image's boundaries
+ x1 = max(int(x1), 0)
+ y1 = max(int(y1), 0)
+ x2 = min(int(x2), weight)
+ y2 = min(int(y2), height)
+
+ return (x1, y1, x2, y2)