detect_faces_video.py

# import libraries
import cv2
import time
import imutils
import numpy as np
from imutils.video import VideoStream


# load serialized model from disk and initialize the video stream
net = cv2.dnn.readNetFromCaffe('deploy.prototxt.txt','res10_300x300_ssd_iter_140000.caffemodel') 
vs = VideoStream(src=0).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=400)
 
    # grab the frame dimensions and convert it to a blob
    (h, w) = frame.shape[:2]
    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()

    # 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
        # you can also change the 'confidence' (0.5 here) for better results
        if confidence < 0.5:
            continue

        # 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(frame, (startX, startY), (endX, endY), (0, 0, 255), 2)
        cv2.putText(frame, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)

    # show the output frame
    cv2.imshow("Frame", frame)
    # Close windows with Esc
    key = cv2.waitKey(1) & 0xFF
 
    # break the loop if ESC key is pressed
    if key == 27:
        break

# destroy all the windows
cv2.destroyAllWindows()
vs.stop()