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65 lines
1.8 KiB
65 lines
1.8 KiB
from modules import utils
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from flask import Flask, request, Response
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from flask_restful import Resource, Api
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from PIL import Image
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import base64
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import json
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import sys
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import os
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import io
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if sys.platform == "win32":
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import tensorflow as tf
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import numpy as np
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import pickle
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sys.path.insert(0, r'C:\Users\Tednokent01\Downloads\MyCity\traffic_analyzer')
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from utils import label_map_util
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from utils import visualization_utils as vis_util
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app = Flask(__name__)
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api = Api(app)
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db_path = os.path.join(app.root_path, 'databases', 'crashes.json')
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with open(db_path, 'r') as f:
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crashes = json.load(f)
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users_path = os.path.join(app.root_path, 'databases', 'users.json')
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with open(users_path, 'r') as f:
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users = json.load(f)
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if sys.platform == "win32":
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PATH_TO_LABELS = '../../traffic_analyzer/object_detection/data/mscoco_label_map.pbtxt'
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PATH_TO_CKPT = 'modules/faster_rcnn_resnet101_kitti_2018_01_28/frozen_inference_graph.pb'
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NUM_CLASSES = 8
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detection_graph = tf.Graph()
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with detection_graph.as_default():
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od_graph_def = tf.GraphDef()
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with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
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serialized_graph = fid.read()
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od_graph_def.ParseFromString(serialized_graph)
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tf.import_graph_def(od_graph_def, name='')
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label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
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categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
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category_index = label_map_util.create_category_index(categories)
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def process_img(img):
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pass
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class Crash(Resource):
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def post(self):
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message = request.form['message']
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base64_img = request.form['img']
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id = request.form['id']
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process_img(Image.open(io.BytesIO(base64.b64decode(base64_img))))
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return id
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