| import cv2 | |
| def discover_square_crop_points(points, image, step=3, maximum_step=30): | |
| x = points["x"] | |
| y = points["y"] | |
| w = points["w"] | |
| h = points["h"] | |
| if step >= maximum_step: | |
| return {"initial_x": x, "initial_y": y, "final_x": x + w, "final_y": y + h} | |
| img_height = image.shape[0] | |
| img_width = image.shape[1] | |
| crop_offset = ((w + h) / 2) / step | |
| initial_x = x - crop_offset | |
| initial_y = y - crop_offset | |
| final_x = x + w + crop_offset | |
| final_y = y + h + crop_offset | |
| if initial_x < 0 or initial_y < 0 or final_x > img_width or final_y > img_height: | |
| return discover_square_crop_points(points, image, step+1, maximum_step) | |
| initial_x = int(initial_x) | |
| initial_y = int(initial_y) | |
| final_x = int(final_x) | |
| final_y = int(final_y) | |
| print(f"step: {step}") | |
| return {"initial_x": initial_x, "initial_y": initial_y, "final_x": final_x, "final_y": final_y} | |
| def crop_faces(cv2_image): | |
| gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY) | |
| face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_alt2.xml') | |
| multiScaleValues = [1.01] | |
| for value in multiScaleValues: | |
| faces = face_cascade.detectMultiScale(image=gray, scaleFactor=value, minNeighbors=4, minSize=(512, 512)) | |
| if len(faces) > 0: | |
| break | |
| cropped_faces = [] | |
| for (x, y, w, h) in faces: | |
| points = {"x": x, "y": y, "w": w, "h": h} | |
| points = discover_square_crop_points(points, cv2_image) | |
| initial_x = points["initial_x"] | |
| initial_y = points["initial_y"] | |
| final_x = points["final_x"] | |
| final_y = points["final_y"] | |
| face = cv2_image[initial_y:final_y, initial_x:final_x] | |
| cropped_faces.append(face) | |
| return cropped_faces |