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Sped up face alignment by using cv2 for rotation rather than PIL
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Josh
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@ -12,7 +12,6 @@ from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
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from deepface.commons import image_utils
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from deepface.commons.logger import Logger
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import time
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logger = Logger()
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@ -247,62 +246,62 @@ def detect_faces(
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# discard rest of the items
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facial_areas = facial_areas[0:max_faces]
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start_time = time.time()
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results = []
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for facial_area in facial_areas:
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x = facial_area.x
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y = facial_area.y
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w = facial_area.w
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h = facial_area.h
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left_eye = facial_area.left_eye
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right_eye = facial_area.right_eye
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confidence = facial_area.confidence
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if expand_percentage > 0:
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# Expand the facial region height and width by the provided percentage
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# ensuring that the expanded region stays within img.shape limits
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expanded_w = w + int(w * expand_percentage / 100)
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expanded_h = h + int(h * expand_percentage / 100)
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x = max(0, x - int((expanded_w - w) / 2))
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y = max(0, y - int((expanded_h - h) / 2))
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w = min(img.shape[1] - x, expanded_w)
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h = min(img.shape[0] - y, expanded_h)
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# extract detected face unaligned
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detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
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# align original image, then find projection of detected face area after alignment
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if align is True: # and left_eye is not None and right_eye is not None:
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aligned_img, angle = align_img_wrt_eyes(img=img, left_eye=left_eye, right_eye=right_eye)
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rotated_x1, rotated_y1, rotated_x2, rotated_y2 = project_facial_area(
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facial_area=(x, y, x + w, y + h), angle=angle, size=(img.shape[0], img.shape[1])
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)
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detected_face = aligned_img[
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int(rotated_y1) : int(rotated_y2), int(rotated_x1) : int(rotated_x2)
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]
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# restore x, y, le and re before border added
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x = x - width_border
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y = y - height_border
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# w and h will not change
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if left_eye is not None:
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left_eye = (left_eye[0] - width_border, left_eye[1] - height_border)
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if right_eye is not None:
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right_eye = (right_eye[0] - width_border, right_eye[1] - height_border)
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result = DetectedFace(
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img=detected_face,
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facial_area=FacialAreaRegion(
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x=x, y=y, h=h, w=w, confidence=confidence, left_eye=left_eye, right_eye=right_eye
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),
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confidence=confidence,
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)
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results.append(result)
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results.append(expand_and_align_face(facial_area=facial_area, img=img, align=align, expand_percentage=expand_percentage, width_border=width_border, height_border=height_border))
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return results
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def expand_and_align_face(facial_area: FacialAreaRegion, img: np.ndarray, align: bool, expand_percentage: int, width_border: int, height_border: int) -> DetectedFace:
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x = facial_area.x
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y = facial_area.y
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w = facial_area.w
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h = facial_area.h
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left_eye = facial_area.left_eye
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right_eye = facial_area.right_eye
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confidence = facial_area.confidence
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if expand_percentage > 0:
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# Expand the facial region height and width by the provided percentage
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# ensuring that the expanded region stays within img.shape limits
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expanded_w = w + int(w * expand_percentage / 100)
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expanded_h = h + int(h * expand_percentage / 100)
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x = max(0, x - int((expanded_w - w) / 2))
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y = max(0, y - int((expanded_h - h) / 2))
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w = min(img.shape[1] - x, expanded_w)
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h = min(img.shape[0] - y, expanded_h)
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# extract detected face unaligned
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detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
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# align original image, then find projection of detected face area after alignment
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if align is True: # and left_eye is not None and right_eye is not None:
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aligned_img, angle = align_img_wrt_eyes(img=img, left_eye=left_eye, right_eye=right_eye)
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rotated_x1, rotated_y1, rotated_x2, rotated_y2 = project_facial_area(
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facial_area=(x, y, x + w, y + h), angle=angle, size=(img.shape[0], img.shape[1])
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)
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detected_face = aligned_img[
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int(rotated_y1) : int(rotated_y2), int(rotated_x1) : int(rotated_x2)
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]
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# restore x, y, le and re before border added
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x = x - width_border
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y = y - height_border
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# w and h will not change
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if left_eye is not None:
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left_eye = (left_eye[0] - width_border, left_eye[1] - height_border)
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if right_eye is not None:
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right_eye = (right_eye[0] - width_border, right_eye[1] - height_border)
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return DetectedFace(
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img=detected_face,
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facial_area=FacialAreaRegion(
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x=x, y=y, h=h, w=w, confidence=confidence, left_eye=left_eye, right_eye=right_eye
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),
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confidence=confidence,
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)
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def align_img_wrt_eyes(
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img: np.ndarray,
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@ -327,7 +326,12 @@ def align_img_wrt_eyes(
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return img, 0
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angle = float(np.degrees(np.arctan2(left_eye[1] - right_eye[1], left_eye[0] - right_eye[0])))
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img = np.array(Image.fromarray(img).rotate(angle, resample=Image.BICUBIC))
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(h, w) = img.shape[:2]
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center = (w // 2, h // 2)
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M = cv2.getRotationMatrix2D(center, -angle, 1.0)
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img = cv2.warpAffine(img, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT, borderValue=(0,0,0))
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return img, angle
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