import numpy as np from flask import Flask, request from flask_restx import Api, Resource, fields import os from datetime import datetime from yoloseg.inference_ import Inference, overlay_mask import cv2 import time import base64 import requests from requests_toolbelt import MultipartEncoder # from config_files import API_ENDPOINT_MAIN app = Flask(__name__) api = Api(app, version='1.0', title='CCTV Image Upload API', description='A simple API for receiving CCTV images') # Namespace definition ns = api.namespace('cctv', description='CCTV operations') model_path = 'yoloseg/weight/best.onnx' classes_txt_file = 'config_files/yolo_config.txt' image_path = 'yoloseg/img3.jpg' model_input_shape = (640, 640) inference_engine = Inference( onnx_model_path=model_path, model_input_shape=model_input_shape, classes_txt_file=classes_txt_file, run_with_cuda=True ) # Define the expected model for incoming data image_upload_model = api.model('ImageUpload', { 'image': fields.String(required=True, description='Image file', dt='File'), 'x-cctv-info': fields.String(required=False, description='CCTV identifier'), 'x-time-sent': fields.String(required=False, description='Time image was sent'), 'x-cctv-latitude': fields.String(required=False, description='Latitude of CCTV'), 'x-cctv-longitude': fields.String(required=False, description='Longitude of CCTV') }) # Define the directory where images will be saved IMAGE_DIR = "network_test" if not os.path.exists(IMAGE_DIR): os.makedirs(IMAGE_DIR) @ns.route('/infer', ) class ImageUpload(Resource): # @ns.expect(image_upload_model, validate=True) def __init__(self, *args, **kargs): super().__init__(*args, **kargs) self.time_sent = None self.cctv_latitude = None self.cctv_longitude = None self.cctv_info = None self.mask = None self.mask_blob = None self.image = None self.image_type = None self.area_percent = 0 @ns.response(200, 'Success') @ns.response(400, 'Validation Error') def post(self): if 'file' not in request.files: ns.abort(400, 'No image part in the request') self.image = request.files['file'] self.image_type = request.headers.get('Content-Type') self.cctv_info = base64.b64decode(request.headers.get('x-cctv-name', '')).decode('UTF-8') self.time_sent = request.headers.get('x-time-sent', '') self.cctv_latitude = request.headers.get('x-cctv-latitude', 'Not provided') self.cctv_longitude = request.headers.get('x-cctv-longitude', 'Not provided') # timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") image = self.image.read() image = np.frombuffer(image, np.uint8) image = cv2.imdecode(image, cv2.IMREAD_COLOR) # filename = f"{timestamp}_{self.cctv_info}.png" t1 = time.time() detections, self.mask = inference_engine.run_inference(cv2.resize(image, model_input_shape)) t2 = time.time() if len(self.mask) > 0: self.mask_blob = cv2.imencode('.png', self.mask) self.mask_blob = self.mask.tobytes() self.mask = cv2.resize(self.mask, (image.shape[0], image.shape[1])) print(t2 - t1) if len(self.mask) != 0: seg_image = overlay_mask(image, self.mask[0], color=(0, 255, 0), alpha=0.3) self.area_percent = 0 else : self.area_percent = np.sum(self.mask) / image.shape[0] * image.shape[1] # self.send_result() # write another post request for pushing a detection result return {"message": f"Image {self.mask} uploaded successfully!"} def send_result(self): time_sent = datetime.now(self.time_zone).strftime("yyyy-MM-dd'T'HH:mm:ss'Z'") header = { 'Content-Type': f'{self.image_type}', 'x-time-sent': time_sent, 'x-cctv-name': base64.b64encode(str(self.cctv_info).encode('utf-8')).decode('ascii'), 'x-cctv-latitude': str(self.cctv_latitude), 'x-cctv-longitude': str(self.cctv_longitude), 'x-area-percentage' : str(self.area_percent), } session = requests.Session() try: multipart_data = MultipartEncoder( fields={ 'image': ( f'frame_{self.cctv_info}.{self.image_type}', self.image, f'image/{self.image_type}' ), 'mask' : ( f'frame_mask_{self.cctv_info}.{self.image_type}', self.mask_blob, f'image/{self.image_type}' ) } ) header["Content-Type"] = multipart_data.content_type response = session.post(self.endpoint, headers=header, data=multipart_data) except Exception as e: print(e) print("Can not connect to the analyzer server. Check the endpoint address or connection.\n" f"Can not connect to : {self.endpoint}") if __name__ == '__main__': app.run(debug=True, port=12345)