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Author: yashk2000

detect_faces_video.py

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+from imutils.video import VideoStream
+import numpy as np
+import argparse
+import imutils
+import time
+import cv2
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-p", "--prototxt", required=True,
+	help="path to Caffe 'deploy' prototxt file")
+ap.add_argument("-m", "--model", required=True,
+	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())
+
+print("[INFO] loading model...")
+net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
+
+print("[INFO] starting video stream...")
+vs = VideoStream(src=0).start()
+time.sleep(2.0)
+
+while True:
+	frame = vs.read()
+	frame = imutils.resize(frame, width=400)
+ 
+	(h, w) = frame.shape[:2]
+	blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,
+		(300, 300), (104.0, 177.0, 123.0))
+ 
+	net.setInput(blob)
+	detections = net.forward()
+
+	for i in range(0, detections.shape[2]):
+		confidence = detections[0, 0, i, 2]
+
+		if confidence < args["confidence"]:
+			continue
+
+		box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+		(startX, startY, endX, endY) = box.astype("int")
+ 
+		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)
+
+	cv2.imshow("Frame", frame)
+	key = cv2.waitKey(1) & 0xFF
+ 
+	if key == ord("q"):
+		break
+
+cv2.destroyAllWindows()
+vs.stop()