Hey I want to show the open cv frame output in window of browser. I am making flask app of human Activity Recognition.
libraries
import the necessary packages
import time
import onnx import numpy as np import argparse import imutils import sys import cv2 from imutils.video import VideoStream from flask import Flask, render_template, request
app = Flask(__name__)
app.secret_key = "my precious" c = open('action_recognition_kinetics.txt') @app.route('/') def home():
return render_template('harPro.html') # render a template
@app.route('/recognize', methods= ['POST','GET']) def Recognize(): if request.method == 'POST': i = request.form['myFile'] CLASSES = c.read().strip().split("\n") SAMPLE_DURATION = 16 SAMPLE_SIZE = 112
load the human activity recognition model
print("[INFO] loading human activity recognition model...")
net = cv2.dnn.readNet('resnet-34_kinetics.onnx')
# grab a pointer to the input video stream
print("[INFO] accessing video stream...")
vs = cv2.VideoCapture(i if i else 0)
# loop until we explicitly break from it
while True:
# initialize the batch of frames that will be passed through the
# model
frames = []
# loop over the number of required sample frames
for i in range(0, SAMPLE_DURATION):
# read a frame from the video stream
(grabbed, frame) = vs.read()
# if the frame was not grabbed then we've reached the end of
# the video stream so exit the script
if not grabbed:
print("[INFO] no frame read from stream - exiting")
sys.exit(0)
# otherwise, the frame was read so resize it and add it to
# our frames list
frame = imutils.resize(frame, width=400)
frames.append(frame)
# now that our frames array is filled we can construct our blob
blob = cv2.dnn.blobFromImages(frames, 1.0,
(SAMPLE_SIZE, SAMPLE_SIZE), (114.7748, 107.7354, 99.4750),
swapRB=True, crop=True)
blob = np.transpose(blob, (1, 0, 2, 3))
blob = np.expand_dims(blob, axis=0)
# pass the blob through the network to obtain our human activity
# recognition predictions
net.setInput(blob)
outputs = net.forward()
label = CLASSES[np.argmax(outputs)]
# loop over our frames
for frame in frames:
# draw the predicted activity on the frame
cv2.rectangle(frame, (0, 0), (300, 40), (0, 0, 0), -1)
cv2.putText(frame, label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX,
0.8, (255, 255, 255), 2)
# # display the frame to our screen
cv2.imshow("Activity Recognition", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
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