I applied mask to video feed using OpenCV and want to display the Live streaming on the website but the following code stops streaming once it starts. I've been wrapping my head around but couldn't figure out the solution. Any help would be greatly appreciated.
views.py
def gen(frame):
while True:
# frame = camera.get_frame()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' frame b'\r\n\r\n')
@api_view(['GET'])
def seasoncolor(request):
while True:
return StreamingHttpResponse(gen(color_detection.color_detection(0)),
content_type='multipart/x-mixed-replace; boundary=frame')
color_detection.py
import numpy as np
import cv2
import sys
'''
ML object detection algo(haarcascade)used to identify objects.
the XML file consists of trained Haar Cascade models.
'''
def color_detection(season):
face_cascade = cv2.CascadeClassifier(
'accounts/personal_color/self_detection/haarcascade_frontalface_default.xml')
# 'accounts/personal_color/self_detection/haarcascade_frontalface_default.xml'
# initialize video from the webcam
video = cv2.VideoCapture(1)
# Spring/summer/fall/winter
while True:
# ret tells if the camera works properly. Frame is an actual frame from the video feed
ret, frame = video.read()
# make sure port is working and read the image
if frame is not None and video.isOpened():
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
'''
Detect the faces within the subregions of the image in scales
scaleFactor indicates how much the image size is reduced at each image scale.
minNeighbors: Higher value results in higher quality of the detected face.
'''
faces = face_cascade.detectMultiScale(
gray, scaleFactor=1.1, minNeighbors=6)
# Draw the rectangle around each face
for (x, y, w, h) in faces:
# Use the stcoordinates to find the center of the face and from that point draw a rectangle of radius w/2 or h/2.
center_coordinates = x w // 2, y h // 2
radius = w // 2 # or can be h / 2 or can be anything based on your requirements
# background color(black)
mask = np.zeros(frame.shape[:2], dtype="uint8")
# Draw the desired region to crop out in white
cv2.circle(mask, center_coordinates, radius, (255, 255, 255), -1)
masked = cv2.bitwise_and(frame, frame, mask=mask)
if int(season) ==0: # Spring
# Replace all (0,0,0)channel with Coral pink
masked[np.where((masked == [0, 0, 0]).all(axis=2))] = [121, 131, 248]
elif int(season) ==1: # Summer
#Replace all (0,0,0)channel with Rose Red
masked[np.where((masked==[0,0,0]).all(axis=2))]=[86,30,194]
elif int(season) ==2: # Fall
#Replace all (0,0,0)channel with Red Brown /Cinnamon
masked[np.where((masked==[0,0,0]).all(axis=2))]=[30,105,210]
else: # Winter
#Replace all (0,0,0)channel with Burgundy Red
masked[np.where((masked==[0,0,0]).all(axis=2))]=[31,2,141]
# cv2.imshow('mask applied', masked)
ret, jpeg = cv2.imencode('.jpg', masked)
return jpeg.tobytes()
if cv2.waitKey(30) & 0xff == 27:
break
video.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
arg = sys.argv[1]
color_detection(arg)
Self_color_diagnosis.js
import React, { useState, useEffect } from 'react';
import ReactDOM from 'react-dom';
import CameraScreen from './CameraScreen';
import { StyleSheet, Text, View, Image } from 'react-native';
import { NavigationContainer } from '@react-navigation/native';
import { createStackNavigator } from '@react-navigation/stack';
import axios from 'axios';
function Self_color_diagnosis({navigation,route}) {
return (
<View style={styles.title_container}>
<Image style={styles.video} source={{
uri: 'http://localhost:8000/seasoncolor/',}}/>
</View>
);
}
const styles = StyleSheet.create({
video: {
width: 500,
height: 500
},
title_container: {
flex: 1,
justifyContent: 'center'
},
});
export default Self_color_diagnosis;
The above code results in the pic below. The streaming stops and does not change at all.
CodePudding user response：
gen() runs loop which all time uses the same frame().
You have to get frame inside this loop.
def gen():
while True:
frame = color_detection.color_detection(0)
if frame:
yield b'--frame\r\nContent-Type: image/jpeg\r\n\r\n' frame b'\r\n\r\n'
#else:
# print('no frame')
But color_detection should run without loop.
And you should create VideoCapture(1) only once.
And you should return frame even if you didn't detect any face.
path = os.path.join(cv2.data.haarcascades, 'haarcascade_frontalface_default.xml')
face_cascade = cv2.CascadeClassifier(path)
video = cv2.VideoCapture(1)
def color_detection(season):
# ret tells if the camera works properly. Frame is an actual frame from the video feed
ret, frame = video.read()
# make sure port is working and read the image
if frame is not None and video.isOpened():
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
'''
Detect the faces within the subregions of the image in scales
scaleFactor indicates how much the image size is reduced at each image scale.
minNeighbors: Higher value results in higher quality of the detected face.
'''
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=6)
# Draw circle around each face
for (x, y, w, h) in faces:
# Use the stcoordinates to find the center of the face and from that point draw a rectangle of radius w/2 or h/2.
center_coordinates = x w // 2, y h // 2
radius = w // 2 # or can be h / 2 or can be anything based on your requirements
# background color(black)
mask = np.zeros(frame.shape[:2], dtype="uint8")
# Draw the desired region to crop out in white
cv2.circle(mask, center_coordinates, radius, (255, 255, 255), -1)
masked = cv2.bitwise_and(frame, frame, mask=mask)
if season == 0: # Spring
# Replace all (0,0,0)channel with Coral pink
masked[np.where((masked == [0, 0, 0]).all(axis=2))] = [121, 131, 248]
elif season == 1: # Summer
#Replace all (0,0,0)channel with Rose Red
masked[np.where((masked==[0,0,0]).all(axis=2))] = [86,30,194]
elif season == 2: # Fall
#Replace all (0,0,0)channel with Red Brown /Cinnamon
masked[np.where((masked==[0,0,0]).all(axis=2))] = [30,105,210]
else: # Winter
#Replace all (0,0,0)channel with Burgundy Red
masked[np.where((masked==[0,0,0]).all(axis=2))] = [31,2,141]
ret, jpeg = cv2.imencode('.jpg', masked)
else: # it is `for/else` construction, not `if/else`
ret, jpeg = cv2.imencode('.jpg', frame)
return jpeg.tobytes()
#return None
BTW:
I see other problem. When it detects many faces then it creates new mask for every face and assigns every mask to original image - so every mask skip previous mask - so it should show only last face, and hide other faces. You should first create one mask with all circles and next use it on image.
EDIT:
I don't know what web framework you use so I used Flask to create minimal working example.
import os
from flask import Flask, Response
import cv2
import numpy as np
app = Flask(__name__)
print('\n'.join(sorted(os.listdir(cv2.data.haarcascades))))
path = os.path.join(cv2.data.haarcascades, 'haarcascade_frontalface_default.xml')
face_cascade = cv2.CascadeClassifier(path)
#video = cv2.VideoCapture(0) # my webcam
video = cv2.VideoCapture(0) # your webcam
def color_detection(season):
ret, frame = video.read()
if frame is not None and video.isOpened():
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=6)
# test two circles on image 640x480
#faces = [[100, 100, 250, 250], [640-100-250, 480-100-250, 250, 250]]
if len(faces) > 0: # it can be `if faces:` because `faces` is `numpy array` which need different method to check if not empty
# background color(black)
mask = np.zeros(frame.shape[:2], dtype="uint8")
# draw all circles on mask
for (x, y, w, h) in faces:
#print(x, y, w, h)
# use the coordinates to find the center of the face and from that point draw a rectangle of radius w/2 or h/2.
center_coordinates = x w // 2, y h // 2
radius = max(w, h) // 2 # or can be h / 2 or can be anything based on your requirements
# draw the desired region to crop out in white
cv2.circle(mask, center_coordinates, radius, (255, 255, 255), -1)
# use mask with all circles
masked = cv2.bitwise_and(frame, frame, mask=mask)
if season == 0: # Spring - Coral pink
color = [121, 131, 248]
elif season == 1: # Summer - Rose Red
color = [86,30,194]
elif season == 2: # Fall - Red Brown /Cinnamon
color = [30,105,210]
else: # Winter - Burgundy Red
color = [31,2,141]
masked[np.where((masked == [0,0,0]).all(axis=2))] = color
else: # no faces
masked = frame
ret, jpeg = cv2.imencode('.jpg', masked)
return jpeg.tobytes()
def gen():
while True:
frame = color_detection(0)
if frame:
yield (b'--frame\r\nContent-Type: image/jpeg\r\n\r\n' frame b'\r\n\r\n')
#else:
# print('no frame')
@app.route('/')
def index():
return '<image src="/seasoncolor">'
@app.route('/seasoncolor')
def seasoncolor():
return Response(gen(), mimetype='multipart/x-mixed-replace; boundary=frame')
if __name__ == '__main__':
#app.debug = True
app.run()
BTW:
To make sure: VideoCapture can work only with local camera where you run web server. It can't work with remote camera on user computer. Only user's browser has access to its camera. To work with remote camera on user computer you would have to use JavaScript to access camera in user browser and send frames to server - like in my examples in my GitHub python-examples: web camera in browser - canvas - take image and upload to server