Face Detection

The more accurate OpenCV face detector is deep learning based, and in particular, utilizes the Single Shot Detector (SSD) framework with ResNet as the base network.

#!/usr/bin/env python
# encoding: utf-8
from imutils.video import VideoStream
import numpy as np
import argparse
import cv2
import imutils
import time
def detected_draw(args, img, detections):
    (h, w) = img.shape[:2]
    # loop over the detections
    for i in range(0, detections.shape[2]):
        # extract the confidence (i.e., probability) associated with the prediction
        confidence = detections[0, 0, i, 2]
        # filter out weak detections by ensuring the `confidence` is greater than the minimum confidence
        if confidence > args["confidence"]:
            # compute the (x, y)-coordinates of the bounding box for the object
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")
            # draw the bounding box of the face along with the associated probability
            text = "{:.2f}%".format(confidence * 100)
            y = startY - 10 if startY - 10 > 10 else startY + 10
            cv2.rectangle(img, (startX, startY), (endX, endY), (255, 255, 255), 2)
            cv2.putText(img, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (255, 255, 255), 2)
        pass
    pass
def img_detect(args):
    # load our serialized model from disk
    print("[INFO] loading model...")
    net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
    # load the input image and construct an input blob for the image
    # by resizing to a fixed 300x300 pixels and then normalizing it
    image = cv2.imread(args["image"])
    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
    # pass the blob through the network and obtain the detections and predictions
    print("[INFO] computing object detections...")
    net.setInput(blob)
    detections = net.forward()
    detected_draw(args, image, detections)
    # show the output image
    cv2.imshow("Output", image)
    cv2.waitKey(0)
    pass
def cam_detect(args):
    # load our serialized model from disk
    print("[INFO] loading model...")
    net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
    # initialize the video stream and allow the camera sensor to warm up
    print("[INFO] starting video stream...")
    vs = VideoStream(src=0).start()
    #for Raspberry Pi camera
    #vs = VideoStream(usePiCamera=True).start()
    time.sleep(2.0)
    # loop over the frames from the video stream
    while True:
        # grab the frame from the threaded video stream and resize it
        # to have a maximum width of 400 pixels
        frame = vs.read()
        frame = imutils.resize(frame, width=800)
        # grab the frame dimensions and convert it to a blob
        blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
        # pass the blob through the network and obtain the detections and predictions
        net.setInput(blob)
        detections = net.forward()
        detected_draw(args, frame, detections)
        # show the output frame
        cv2.imshow("Frame", frame)
        key = cv2.waitKey(1) & 0xFF
        # if the `q` key was pressed, break from the loop
        if key == ord("q"):
            break
        pass
    # do a bit of cleanup
    cv2.destroyAllWindows()
    vs.stop()
    pass
if __name__ == '__main__':
    # construct the argument parse and parse the arguments
    ap = argparse.ArgumentParser()
    ap.add_argument("-i", "--image", required=False, default='', help="path to input image")
    ap.add_argument("-p", "--prototxt", required=False, default='deploy.prototxt', help="path to Caffe 'deploy' prototxt file")
    ap.add_argument("-m", "--model", required=False, default='res10_300x300_ssd_iter_140000_fp16.caffemodel', help="path to Caffe pre-trained model")
    ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections")
    args = vars(ap.parse_args())
    if len(args['image']) == 0:
        cam_detect(args)
    else:
        img_detect(args)
    pass

refs:
the Caffe prototxt file: deploy.prototxt
the Caffe weight file: res10_300x300_ssd_iter_140000_fp16.caffemodel