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| import cv2 | |
| import numpy as np | |
| class CaesarFaceDetection: | |
| def __init__(self) -> None: | |
| # https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt | |
| prototxt_path = "CaesarFaceDetection/weights/deploy.prototxt.txt" | |
| # https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel | |
| model_path = "CaesarFaceDetection/weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" | |
| # load Caffe model | |
| self.model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path) | |
| def detect_face(self,image, showtext=False,snapcropface=False): | |
| h, w = image.shape[:2] | |
| # preprocess the image: resize and performs mean subtraction | |
| blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0)) | |
| # set the image into the input of the neural network | |
| self.model.setInput(blob) | |
| # perform inference and get the result | |
| output = np.squeeze(self.model.forward()) | |
| font_scale = 1.0 | |
| for i in range(0, output.shape[0]): | |
| # get the confidence | |
| confidence = output[i, 2] | |
| # if confidence is above 50%, then draw the surrounding box | |
| if confidence > 0.5: | |
| # get the surrounding box cordinates and upscale them to original image | |
| box = output[i, 3:7] * np.array([w, h, w, h]) | |
| # convert to integers | |
| start_x, start_y, end_x, end_y = box.astype(np.int) | |
| # draw the rectangle surrounding the face | |
| start_point = (start_x, start_y) | |
| end_point = (end_x, end_y) | |
| if snapcropface == True: | |
| factor_add = 20 | |
| crop_img = image[start_y- factor_add:end_y+ factor_add, start_x- factor_add:end_x + factor_add] | |
| return crop_img | |
| #cv2.imshow("cropped", crop_img) | |
| #cv2.waitKey(0) | |
| cv2.rectangle(image,start_point,end_point, color=(255, 0, 0), thickness=2) | |
| # draw text as well | |
| if showtext == True: | |
| cv2.putText(image, f"{confidence*100:.2f}%", (start_x, start_y-5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 0, 0), 2) | |
| if snapcropface != True: | |
| return image | |