Click here to Skip to main content
14,977,676 members
Please Sign up or sign in to vote.
0.00/5 (No votes)
I am trying to run my tensorflow script so that the result image will be exported and will be use to my site. At first, I'm using flask but I decided to shift to django because the script is too slow read and sometimes it won't run. That's why, I use django. But then, the script still wont run. Please, need help...

Files that the tensorflow script needed were placed at the same location where, are in.

What I have tried:

# 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.

# 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

  # Number of classes the object detector can identify

  # 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 =
          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')


  # 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) =
      [detection_boxes, detection_scores, detection_classes, num_detections],
      feed_dict={image_tensor: image_expanded})

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


    # 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')

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

urlpatterns = [
	path("map/mandaluyong", views.mandaluyong, name='map/mandaluyong')
Updated 3-Nov-19 8:56am

1 solution


This content, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

CodeProject, 20 Bay Street, 11th Floor Toronto, Ontario, Canada M5J 2N8 +1 (416) 849-8900