Code for Face Detection using OpenCV in Python Tutorial

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face_detection.py

import cv2 # loading the test image image = cv2.imread("kids.jpg") # converting to grayscale image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # initialize the face recognizer (default face haar cascade) face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") # detect all the faces in the image faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) # print the number of faces detected print(f"{len(faces)} faces detected in the image.") # for every face, draw a blue rectangle for x, y, width, height in faces: cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) # save the image with rectangles cv2.imwrite("kids_detected.jpg", image)

live_face_detection.py

import cv2 # create a new cam object cap = cv2.VideoCapture(0) # initialize the face recognizer (default face haar cascade) face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") while True: # read the image from the cam _, image = cap.read() # converting to grayscale image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # detect all the faces in the image faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) # for every face, draw a blue rectangle for x, y, width, height in faces: cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) cv2.imshow("image", image) if cv2.waitKey(1) == ord("q"): break cap.release() cv2.destroyAllWindows()

face_detection_dnn.py

import cv2 import numpy as np # https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt prototxt_path = "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 = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" # load Caffe model model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path) # read the desired image image = cv2.imread("kids.jpg") # get width and height of the image 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 model.setInput(blob) # perform inference and get the result output = np.squeeze(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 cv2.rectangle(image, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2) # draw text as well cv2.putText(image, f"{confidence*100:.2f}%", (start_x, start_y-5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 0, 0), 2) # show the image cv2.imshow("image", image) cv2.waitKey(0) # save the image with rectangles cv2.imwrite("kids_detected_dnn.jpg", image)

live_face_detection_dnn.py

import cv2 import numpy as np # https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt prototxt_path = "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 = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" # load Caffe model model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path) cap = cv2.VideoCapture(0) while True: # read the desired image _, image = cap.read() # get width and height of the image 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 model.setInput(blob) # perform inference and get the result output = np.squeeze(model.forward()) for i in range(0, output.shape[0]): # get the confidence confidence = output[i, 2] # if confidence is above 45%, then draw the surrounding box if confidence > 0.45: # 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 cv2.rectangle(image, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2) # draw text as well cv2.putText(image, f"{confidence*100:.2f}%", (start_x, start_y-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2) # show the image cv2.imshow("image", image) if cv2.waitKey(1) == ord("q"): break cv2.destroyAllWindows() cap.release()