First Commit

webstreaming.py

@@ -0,0 +1,127 @@
+# import the necessary packages
+from pyimagesearch.motion_detection.singlemotiondetector import SingleMotionDetector
+from imutils.video import VideoStream
+from flask import Response
+from flask import Flask
+from flask import render_template
+import threading
+import argparse
+import datetime
+import imutils
+import time
+import cv2
+
+# initialize the output frame and a lock used to ensure thread-safe
+# exchanges of the output frames (useful when multiple browsers/tabs
+# are viewing the stream)
+outputFrame = None
+lock = threading.Lock()
+
+# initialize a flask object
+app = Flask(__name__)
+
+# initialize the video stream and allow the camera sensor to
+# warmup
+#vs = VideoStream(usePiCamera=1).start()
+vs = VideoStream(src=0).start()
+time.sleep(2.0)
+
+@app.route("/")
+def index():
+	# return the rendered template
+	return render_template("index.html")
+
+def detect_motion(frameCount):
+	# grab global references to the video stream, output frame, and
+	# lock variables
+	global vs, outputFrame, lock
+
+	# initialize the motion detector and the total number of frames
+	# read thus far
+	md = SingleMotionDetector(accumWeight=0.1)
+	total = 0
+
+  # loop over frames from the video stream
+	while True:
+		# read the next frame from the video stream, resize it,
+		# convert the frame to grayscale, and blur it
+		frame = vs.read()
+		frame = imutils.resize(frame, width=400)
+		gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+		gray = cv2.GaussianBlur(gray, (7, 7), 0)
+
+		# grab the current timestamp and draw it on the frame
+		timestamp = datetime.datetime.now()
+		cv2.putText(frame, timestamp.strftime(
+			"%A %d %B %Y %I:%M:%S%p"), (10, frame.shape[0] - 10),
+			cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
+
+    # if the total number of frames has reached a sufficient
+		# number to construct a reasonable background model, then
+		# continue to process the frame
+		if total > frameCount:
+			# detect motion in the image
+			motion = md.detect(gray)
+
+			# check to see if motion was found in the frame
+			if motion is not None:
+				# unpack the tuple and draw the box surrounding the
+				# "motion area" on the output frame
+				(thresh, (minX, minY, maxX, maxY)) = motion
+				cv2.rectangle(frame, (minX, minY), (maxX, maxY),
+					(0, 0, 255), 2)
+		
+		# update the background model and increment the total number
+		# of frames read thus far
+		md.update(gray)
+		total += 1
+
+		# acquire the lock, set the output frame, and release the
+		# lock
+		with lock:
+			outputFrame = frame.copy()
+
+def generate():
+	# grab global references to the output frame and lock variables
+	global outputFrame, lock
+
+	# loop over frames from the output stream
+	while True:
+		# wait until the lock is acquired
+		with lock:
+			# check if the output frame is available, otherwise skip
+			# the iteration of the loop
+			if outputFrame is None:
+				continue
+			
+      # encode the frame in JPEG format
+			(flag, encodedImage) = cv2.imencode(".jpg", outputFrame)
+			
+      # ensure the frame was successfully encoded
+			if not flag:
+				continue
+		
+    # yield the output frame in the byte format
+		yield(b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + 
+			bytearray(encodedImage) + b'\r\n')
+
+@app.route("/video_feed")
+def video_feed():
+	# return the response generated along with the specific media
+	# type (mime type)
+	return Response(generate(),
+		mimetype = "multipart/x-mixed-replace; boundary=frame")
+
+# check to see if this is the main thread of execution
+if __name__ == '__main__':
+	# start a thread that will perform motion detection
+	t = threading.Thread(target=detect_motion, args=(32,))
+	t.daemon = True
+	t.start()
+
+	# start the flask app
+	app.run(host="0.0.0.0", port=8080, debug=False,
+		threaded=True, use_reloader=False)
+
+# release the video stream pointer
+vs.stop()