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Clean the new wrapper.
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@ -28,30 +28,18 @@ def detect_face(face_detector, img, align=False):
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for result in results:
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for result in results:
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x, y, w, h = result.boxes.xywh.tolist()[0]
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x, y, w, h = result.boxes.xywh.tolist()[0]
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confidence = result.boxes.conf.tolist()[0]
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confidence = result.boxes.conf.tolist()[0]
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# print(f"Confidence: {confidence}, x: {x}, y: {y}, w: {w}, h: {h}")
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# print landmarks
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# print(result.keypoints.tolist())
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# print(f"Left eye: {left_eye}, right eye: {right_eye}")
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# add eyes landmarks to img
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# import cv2
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# img = cv2.circle(img, (int(left_eye[0]), int(left_eye[1])), 2, (0, 0, 255), 2)
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# img = cv2.circle(img, (int(right_eye[0]), int(right_eye[1])), 2, (0, 255, 0), 2)
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# change to top left corner, width, height
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x, y, w, h = int(x - w / 2), int(y - h / 2), int(w), int(h)
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x, y, w, h = int(x - w / 2), int(y - h / 2), int(w), int(h)
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detected_face = img[y : y + h, x : x + w].copy()
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detected_face = img[y : y + h, x : x + w].copy()
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if align:
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if align:
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# Extract landmarks
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left_eye, right_eye, _, _, _ = result.keypoints.tolist()
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left_eye, right_eye, _, _, _ = result.keypoints.tolist()
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# Check the landmarks confidence before alignment
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# Check the landmarks confidence before alignment
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# print(f"Left eye: {left_eye[2]}, right eye: {right_eye[2]}")
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if left_eye[2] > 0.5 and right_eye[2] > 0.5:
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if left_eye[2] > 0.5 and right_eye[2] > 0.5:
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# print("Aligning face")
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detected_face = FaceDetector.alignment_procedure(
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# print(left_eye[:2], right_eye[:2])
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detected_face, left_eye[:2], right_eye[:2]
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detected_face = FaceDetector.alignment_procedure(detected_face, left_eye[:2], right_eye[:2])
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)
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resp.append((detected_face, [x, y, w, h], confidence))
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resp.append((detected_face, [x, y, w, h], confidence))
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