webstreaming.py.py

from packages.centroidtracker import CentroidTracker
from packages.trackableobject import TrackableObject
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import dlib
import cv2

from flask import Flask, render_template, Response

app = Flask(__name__)

# initialize the list of class labels MobileNet SSD was trained to
# detect
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
    "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
    "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
    "sofa", "train", "tvmonitor"]

# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(r"modelfiles/MobileNetSSD_deploy.prototxt", r"modelfiles/MobileNetSSD_deploy.caffemodel")

# if a video path was not supplied, grab a reference to the webcam
if not (r"videos/example_01.mp4", False):
    print("[INFO] starting video stream...")
    vs = VideoStream(src=0).start()
    time.sleep(2.0)

# otherwise, grab a reference to the video file
else:
    print("[INFO] opening video file...")
    vs = cv2.VideoCapture("videos/example_01.mp4")

# initialize the video writer (we'll instantiate later if need be)


@app.route('/')
def index():
    return render_template('base.html')

def gen():
    writer = None

    # initialize the frame dimensions (we'll set them as soon as we read
    # the first frame from the video)
    W = None
    H = None

    # instantiate our centroid tracker, then initialize a list to store
    # each of our dlib correlation trackers, followed by a dictionary to
    # map each unique object ID to a TrackableObject
    ct = CentroidTracker(maxDisappeared=40, maxDistance=50)
    trackers = []
    trackableObjects = {}

    # initialize the total number of frames processed thus far, along
    # with the total number of objects that have moved either up or down
    totalFrames = 0
    totalDown = 0
    totalUp = 0
    skip_frames = 30

    # start the frames per second throughput estimator
    fps = FPS().start()
    while True:
        # grab the next frame and handle if we are reading from either
        # VideoCapture or VideoStream
        sucess ,frame = vs.read()

    	# if we are viewing a video and we did not grab a frame then we
        #ave reached the end of the video
        if "videos/example_01.mp4" is not None and frame is None:
            print("noframe")
            break
        # resize the frame to have a maximum width of 500 pixels (the
        # less data we have, the faster we can process it), then convert
        # the frame from BGR to RGB for dlib
        frame = imutils.resize(frame, width=500)
        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        # if the frame dimensions are empty, set them
        if W is None or H is None:
            (H, W) = frame.shape[:2]
            # if we are supposed to be writing a video to disk, initialize
            # the writer
        status = "Waiting"
        rects = []
        # check to see if we should run a more computationally expensive
        # object detection method to aid our tracker
        if totalFrames % skip_frames == 0:
            print("detection")
            # set the status and initialize our new set of object trackers
            status = "Detecting"
            trackers = []
            # convert the frame to a blob and pass the blob through the
            # network and obtain the detections
            blob = cv2.dnn.blobFromImage(frame, 0.007843, (W, H), 127.5)
            net.setInput(blob)
            detections = net.forward()
            # loop over the detections
            for i in np.arange(0, detections.shape[2]):
                print("first forloop")
                # extract the confidence (i.e., probability) associated
                # with the prediction
                confidence = detections[0, 0, i, 2]
                # filter out weak detections by requiring a minimum
                # confidence
                if confidence > 0.4:
                    print("yes greater")
                    # extract the index of the class label from the
                    # detections list
                    idx = int(detections[0, 0, i, 1])
                    # if the class label is not a person, ignore it
                    if CLASSES[idx] != "person":
                        print("yes person")
                        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")
                    # construct a dlib rectangle object from the bounding
                    # box coordinates and then start the dlib correlation
                    # tracker
                    tracker = dlib.correlation_tracker()
                    rect = dlib.rectangle(startX, startY, endX, endY)
                    tracker.start_track(rgb, rect)
                    # add the tracker to our list of trackers so we can
                    # utilize it during skip frames
                    trackers.append(tracker)
            # otherwise, we should utilize our object *trackers* rather than
            # object *detectors* to obtain a higher frame processing throughput
        else:
            # loop over the trackers
            for tracker in trackers:
                # set the status of our system to be 'tracking' rather
                # than 'waiting' or 'detecting'
                status = "Tracking"
                # update the tracker and grab the updated position
                tracker.update(rgb)
                pos = tracker.get_position()
                # unpack the position object
                startX = int(pos.left())
                startY = int(pos.top())
                endX = int(pos.right())
                endY = int(pos.bottom())
                # add the bounding box coordinates to the rectangles list
                rects.append((startX, startY, endX, endY))
                # draw a horizontal line in the center of the frame -- once an
                # object crosses this line we will determine whether they were
                # moving 'up' or 'down'
        cv2.line(frame, (0, H // 2), (W, H // 2), (0, 255, 255), 2)
        # use the centroid tracker to associate the (1) old object
        # centroids with (2) the newly computed object centroids
        objects = ct.update(rects)
        # loop over the tracked objects
        for (objectID, centroid) in objects.items():
            # check to see if a trackable object exists for the current
            # object ID
            to = trackableObjects.get(objectID, None)
            # if there is no existing trackable object, create one
            if to is None:
                to = TrackableObject(objectID, centroid)
                # otherwise, there is a trackable object so we can utilize it
                # to determine direction
            else:
                # the difference between the y-coordinate of the *current*
                # centroid and the mean of *previous* centroids will tell
                # us in which direction the object is moving (negative for
                # 'up' and positive for 'down')
                y = [c[1] for c in to.centroids]
                direction = centroid[1] - np.mean(y)
                to.centroids.append(centroid)
                # check to see if the object has been counted or not
                if not to.counted:
                    # if the direction is negative (indicating the object
                    # is moving up) AND the centroid is above the center
                    # line, count the object
                    if direction < 0 and centroid[1] < H // 2:
                        totalUp += 1
                        to.counted = True
                    # if the direction is positive (indicating the object
                    # is moving down) AND the centroid is below the
                    # center line, count the object
                    elif direction > 0 and centroid[1] > H // 2:
                        totalDown += 1
                        to.counted = True
            # store the trackable object in our dictionary
            trackableObjects[objectID] = to
            # draw both the ID of the object and the centroid of the
            # object on the output frame
            text = "ID {}".format(objectID)
            cv2.putText(frame, text, (centroid[0] - 10, centroid[1] - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
            cv2.circle(frame, (centroid[0], centroid[1]), 4, (0, 255, 0), -1)
            cv2.imwrite("3.jpg",frame)
        # construct a tuple of information we will be displaying on the
        # frame
        info = [
            ("Up", totalUp),
            ("Down", totalDown),
            ("Status", status),
            ]
        # loop over the info tuples and draw them on our frame
        for (i, (k, v)) in enumerate(info):
            text = "{}: {}".format(k, v)
            cv2.putText(frame, text, (10, H - ((i * 20) + 20)),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
            # check to see if we should write the frame to disk
        (flag, encodedImage) = cv2.imencode(".jpg", frame)
        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 Response(gen(),
                    mimetype='multipart/x-mixed-replace; boundary=frame')

if __name__ == '__main__':
    app.run(host='0.0.0.0', debug=True)
    vs.release()
    cv2.destroyAllWindows()