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 views.py, urls.py are in.
What I have tried:
# Import packages
import numpy as np
import tensorflow as tf
# 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
# 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()
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
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],
# 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')