Viewing file:  api.py (3.13 KB) -rw-r--r--
Select action/file-type:
 (+) |  (+) |  (+) | Code (+) | Session (+) |  (+) | SDB (+) |  (+) |  (+) |  (+) |  (+) |  (+) |
from flask import Flask, jsonify, request
import os
import time
import cv2
import numpy as np
from tensorflow import keras
### Load model using ke
json_file = open('tensorflow_model/model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = keras.models.model_from_json(loaded_model_json)
loaded_model.load_weights("tensorflow_model/model.h5")
# RTSP_URL = 'rtsp://admin:admin123@192.168.31.190:554/cam/realmonitor?channel=8&subtype=0'
# creating a Flask app
app = Flask(__name__)
# on the terminal type: curl http://127.0.0.1:5000/
# returns hello world when we use GET.
# returns the data that we send when we use POST.
@app.route('/ai', methods = ['GET', 'POST'])
def with_parameters():
url = request.args.get('url')
if (request.args.get('subtype')):
url = url+"&subtype="+request.args.get('subtype')
RTSP_URL = url
os.environ['OPENCV_FFMPEG_CAPTURE_OPTIONS'] = 'rtsp_transport;udp'
if(request.method == 'GET'):
#Capture Video from CCTV Camera using OpenCV
cap = cv2.VideoCapture(RTSP_URL, cv2.CAP_FFMPEG)
if not cap.isOpened():
print('Cannot open RTSP stream') ## checking RTSP stream
exit(-1)
#Initializing Videowriter to save video
# width, height = 700, 600
# fps = 25
# # fourcc = cv2.VideoWriter_fourcc(*'DIVX')
# fourcc =cv2.VideoWriter_fourcc(*'MJPG')
# # Create VideoWriter object
# writer = cv2.VideoWriter(filename='video_output.avi',
# fourcc=fourcc,
# apiPreference=cv2.CAP_FFMPEG,
# fps=float(fps),
# frameSize=(width, height),
# isColor=True)
#Declaring Frame Rate to Control frame
frame_rate = 1
prev = 0
while True:
_, frame = cap.read() ##Read stream
# writer.write(frame) ##write Frame to save video
time_elapsed = time.time() - prev
if time_elapsed > 1./frame_rate: ##controlling Frame Rate
prev = time.time()
## Preparing input for prediction
image = cv2.resize(frame, (224, 224))
img = np.reshape(image, [1, 224, 224, 3])
## Give input value to model
output=np.argmax(loaded_model.predict(img))
if (output==0):
text ="Okey"
else:
text = "Problem"
return text
# #Resize to show Video with output prediction4
# image = cv2.resize(frame, (700, 550))
# im=cv2.putText(img=image, text=text, org=(0, 50), fontFace=cv2.FONT_HERSHEY_TRIPLEX, fontScale=1.1, color=(0, 255, 255),thickness=1)
# cv2.imshow("output",im)
# if cv2.waitKey(1) == 27:
# cap.release()
# writer.release()
# cv2.destroyAllWindows()
# break
# driver function
if __name__ == '__main__':
app.run(debug = True)
|