Выполнение скрипта Python на веб-сайте django

---

Я пытаюсь запустить свой скрипт tensorflow так, чтобы результирующее изображение было экспортировано и использовалось на моем сайте. Сначала я использую flask, но решил перейти на django, потому что скрипт слишком медленно читается и иногда не запускается. Вот почему я использую django. Но тогда сценарий все равно не запустится. Пожалуйста, нужна помощь...

Файлы, необходимые сценарию tensorflow, были помещены в то же место, где они находились. views.py, urls.py входите.

Что я уже пробовал:

views.py

# Import packages
import os
import cv2
import numpy as np
import tensorflow as tf
import sys
import easygui

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util
from api import object_counting_api
from django.shortcuts import render

def mandaluyong(request):
  # Name of the directory containing the object detection module we're using

  path = easygui.fileopenbox()

  MODEL_NAME = 'inference_graph'
  IMAGE_NAME = path

  # Grab path to current working directory
  CWD_PATH = os.getcwd()


  # Path to frozen detection graph .pb file, which contains the model that is used
  # for object detection.
  PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')

  # Path to label map file
  PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')

  # Path to image
  PATH_TO_IMAGE = os.path.join(CWD_PATH,IMAGE_NAME)

  # Number of classes the object detector can identify
  NUM_CLASSES = 2

  # Load the label map.
  # Label maps map indices to category names, so that when our convolution
  # Here we use internal utility functions, but anything that returns a
  # dictionary mapping integers to appropriate string labels would be fine
  label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
  categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
  category_index = label_map_util.create_category_index(categories)

  # Load the Tensorflow model into memory.
  detection_graph = tf.Graph()
  with detection_graph.as_default():
      od_graph_def = tf.GraphDef()
      with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
          serialized_graph = fid.read()
          od_graph_def.ParseFromString(serialized_graph)
          tf.import_graph_def(od_graph_def, name='')

      sess = tf.Session(graph=detection_graph)

  # Define input and output tensors (i.e. data) for the object detection classifier

  # Input tensor is the image
  image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

  # Output tensors are the detection boxes, scores, and classes
  # Each box represents a part of the image where a particular object was detected
  detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

  # Each score represents level of confidence for each of the objects.
  # The score is shown on the result image, together with the class label.
  detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
  detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')

  # Number of objects detected
  num_detections = detection_graph.get_tensor_by_name('num_detections:0')

  font = cv2.FONT_HERSHEY_SIMPLEX

  # Load image using OpenCV and
  # expand image dimensions to have shape: [1, None, None, 3]
  # i.e. a single-column array, where each item in the column has the pixel RGB value
  image = cv2.imread(PATH_TO_IMAGE)
  image_expanded = np.expand_dims(image, axis=0)

  # Perform the actual detection by running the model with the image as input
  (boxes, scores, classes, num) = sess.run(
      [detection_boxes, detection_scores, detection_classes, num_detections],
      feed_dict={image_tensor: image_expanded})

  # Draw the results of the detection (aka 'visulaize the results')

  vis_util.visualize_boxes_and_labels_on_image_array(
      image,
      np.squeeze(boxes),
      np.squeeze(classes).astype(np.int32),
      np.squeeze(scores),
      category_index,
      use_normalized_coordinates=True,
      line_thickness=1,
      min_score_thresh=0.50)

    # All the results have been drawn on image. Now display the image.

  path = 'static/result'
  cv2.imwrite(os.path.join(path , 'mandaluyong.png'), image)  
  
  return render(request, 'map/mandaluyong.php')

urls.py

from django.urls import include, path
from . import views

urlpatterns = [
	path("map/mandaluyong", views.mandaluyong, name='map/mandaluyong')
]

---