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143 lines
3.9 KiB
143 lines
3.9 KiB
from flask import Flask, request
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from flask_restful import Resource, Api, abort
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import json
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import io
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import base64
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from PIL import Image
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import sys,getpass
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import datetime
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import cv2
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import ssl
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from urllib.parse import urlencode
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from urllib.request import Request, urlopen
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if getpass.getuser() == "tedankara":
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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 object_detection.utils import label_map_util
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from object_detection.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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context = ssl._create_unverified_context()
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score_dict = {
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1: 1,
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2: 1,
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3: 1,
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4: 1,
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5: 1,
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6: 1,
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7: 1,
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8: 1
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}
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with open("modules/databases/complaints.json","r") as f:
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complaints = json.load(f)
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if getpass.getuser() == "tedankara":
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# Path to frozen detection graph. This is the actual model that is used for the object detection.
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# List of the strings that is used to add correct label for each box.
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PATH_TO_LABELS = 'modules/trainedModels/crack_label_map.pbtxt'
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NUM_CLASSES = 8
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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 load_image_into_numpy_array(image):
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(im_width, im_height) = image.size
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return np.array(image.getdata()).reshape(
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(im_height, im_width, 3)).astype(np.uint8)
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def process_img(img_base64):
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if getpass.getuser() == "tedankara":
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url = 'https://127.0.0.1:5001/ai' # Set destination URL here
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post_fields = {'img': img_base64,"type":"damage"} # Set POST fields here
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request = Request(url, urlencode(post_fields).encode())
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img = load_image_into_numpy_array(Image.open(io.BytesIO(base64.b64decode(img_base64))))
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output_dict = json.loads(urlopen(request, context=context).read())
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print(output_dict)
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vis_util.visualize_boxes_and_labels_on_image_array(
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img,
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np.array(output_dict['detection_boxes']),
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output_dict['detection_classes'],
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output_dict['detection_scores'],
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category_index,
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instance_masks=output_dict.get('detection_masks'),
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use_normalized_coordinates=True,
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line_thickness=8,
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min_score_thresh=0.3
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)
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defects = []
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for index, i in enumerate(output_dict['detection_classes']):
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score = output_dict['detection_scores'][index]
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if score > 0.3:
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defects.append(i)
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priority = 0
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for i in defects:
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priority += score_dict[i]
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if priority > 10:
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priority = 10
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buffered = io.BytesIO()
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img = Image.fromarray(img, 'RGB')
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img.save(buffered, format="JPEG")
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img_str = base64.b64encode(buffered.getvalue())
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return img_str.decode("ascii"),priority,defects
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return img_base64, 7,["unprocessed"]
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class Complaint(Resource):
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def post(self):
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args = request.form.to_dict()
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complaint = args
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complaint["response"] = {"status":False}
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img_process,priority,tags = process_img(complaint["img"])
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complaint["img"] = img_process
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complaint["response"]["priority"] = str(priority)
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complaint["tags"] = list(map(str, tags))
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complaint["datetime"] = datetime.datetime.now().strftime('%b-%d-%I:%M %p-%G')
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try:
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complaints[complaint["id"]].append(complaint)
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except KeyError:
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complaints[complaint["id"]] = [complaint]
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del complaints[complaint["id"]][-1]["id"]
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with open('modules/databases/complaints.json', 'w') as complaints_file:
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json.dump(complaints, complaints_file, indent=2)
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class Complaints(Resource):
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def post(self):
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id = request.form["id"]
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return complaints[id]
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class ComplaintsUpdate(Resource):
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def get(self):
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args = request.args
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complaints[args.get("id")][int(args.get("index"))]["response"]["message"] = args.get("message")
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complaints[args["id"]][int(args["index"])]["response"]["status"] = True
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with open('modules/databases/complaints.json', 'w') as complaints_file:
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json.dump(complaints, complaints_file, indent=2)
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return
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