Sep-09-2019, 09:51 PM
Hello,
I'm running the following script to stream Raspberry Pi camera video to a web browser. The script runs properly, but I don't know how to add code to flip the image vertically, since it is displayed upside down and I can't flip the camera itself. I tried adding a camera.vflip = True line after the "imports" section, but then the script fails. I can't figure out how to flip the output image since the script is not calling the PiCamera module. Any help is appreciated.
I'm running the following script to stream Raspberry Pi camera video to a web browser. The script runs properly, but I don't know how to add code to flip the image vertically, since it is displayed upside down and I can't flip the camera itself. I tried adding a camera.vflip = True line after the "imports" section, but then the script fails. I can't figure out how to flip the output image since the script is not calling the PiCamera module. Any help is appreciated.
# USAGE # python webstreaming.py --ip 0.0.0.0 --port 8000 # import the necessary packages from pyimagesearch.motion_detection 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 for 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) # cehck 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__': # construct the argument parser and parse command line arguments ap = argparse.ArgumentParser() ap.add_argument("-i", "--ip", type=str, required=True, help="ip address of the device") ap.add_argument("-o", "--port", type=int, required=True, help="ephemeral port number of the server (1024 to 65535)") ap.add_argument("-f", "--frame-count", type=int, default=32, help="# of frames used to construct the background model") args = vars(ap.parse_args()) # start a thread that will perform motion detection t = threading.Thread(target=detect_motion, args=( args["frame_count"],)) t.daemon = True t.start() # start the flask app app.run(host=args["ip"], port=args["port"], debug=True, threaded=True, use_reloader=False) # release the video stream pointer vs.stop()delta1071