Vfrdtyky

I am trying to detect cars in a certain area of live video stream. For that I used Tensorflow's object detection API. Now, the detection is fair enough and almost all the cars in the live video stream are detected as "car" with bounding boxes around them and with some amount of detection confidence score in percentage.

My question is how do I check for availability of only the desired on required bounding boxes?

For example, since the desired area and the camera used for detection are both fixed in position, I used OpenCV's cv2.rectangle() function and passed the (x1,y1) and (x2,y2) co-ordinates of the desired area. So now, I have constant rectangular box around that area. My task is to somehow know that the car has arrived in this marked rectangle area by printing a logging message "detected" to the Ubuntu terminal.

I am having difficulties comparing bounding box co-ordinates with the rectangle co-ordinates. So the question arises how to

1. Grab only required bounding boxes(thereby required detected cars)?


2. detect whenever these bounding boxes are inside the rectangle/marked area?

Here is the code I used.

import numpy as np

import os

import six.moves.urllib as urllib

import sys

import tarfile

import tensorflow as tf

import zipfile



from collections import defaultdict

from io import StringIO

from PIL import Image



import cv2

cap = cv2.VideoCapture(0)

# This is needed since the notebook is stored in the object_detection 

folder.

sys.path.append("..")

from object_detection.utils import ops as utils_ops



if tf.__version__ != '1.10.1':

  raise ImportError('Please upgrade your tensorflow installation to 

v1.10.1* or later!')





# ## Env setup



# In[3]:



# ## Object detection imports

# Here are the imports from the object detection module.



# In[5]:



from utils import label_map_util

from utils import visualization_utils as vis_util



# # Model preparation 



# ## Variables



# Any model exported using the `export_inference_graph.py` tool can be 

loaded here simply by changing `PATH_TO_FROZEN_GRAPH` to point to a 

new .pb file.  

# 

# By default we use an "SSD with Mobilenet" model here. See the 

[detection model zoo] 



# In[6]:

# What model to download.

MODEL_NAME = 'car_inference_graph'



# Path to frozen detection graph. This is the actual model that is 

used for the object detection.

PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'



# List of the strings that is used to add correct label for each box.

PATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt')



NUM_CLASSES = 1



# ## Load a (frozen) Tensorflow model into memory.



# In[7]:

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='')



# ## Loading label map

# Label maps map indices to category names, so that when our 

convolution network predicts `5`, we know that this corresponds to 

`airplane`.  Here we use internal utility functions, but anything that 

returns a dictionary mapping integers to appropriate string labels 

would be fine



# In[8]:



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)



# ## Helper code



# In[9]:



def load_image_into_numpy_array(image):

  (im_width, im_height) = image.size

  return np.array(image.getdata()).reshape(

      (im_height, im_width, 3)).astype(np.uint8)



# # Detection



def run_inference_for_single_image(image, graph):

  with graph.as_default():

    with tf.Session() as sess:

      # Get handles to input and output tensors

      ops = tf.get_default_graph().get_operations()

      all_tensor_names = {output.name for op in ops for output in 

 op.outputs}

      tensor_dict = {}

      for key in [

          'num_detections', 'detection_boxes', 'detection_scores',

          'detection_classes', 'detection_masks'

      ]:

        tensor_name = key + ':0'

        if tensor_name in all_tensor_names:

          tensor_dict[key] = 

tf.get_default_graph().get_tensor_by_name(

              tensor_name)

      if 'detection_masks' in tensor_dict:

        # The following processing is only for single image

        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], 

[0])

        detection_masks = tf.squeeze(tensor_dict['detection_masks'], 

[0])

        # Reframe is required to translate mask from box coordinates 

to image coordinates and fit the image size.

        real_num_detection = tf.cast(tensor_dict['num_detections'][0], 

tf.int32)

        detection_boxes = tf.slice(detection_boxes, [0, 0], 

[real_num_detection, -1])

        detection_masks = tf.slice(detection_masks, [0, 0, 0], 

[real_num_detection, -1, -1])

        detection_masks_reframed = 

utils_ops.reframe_box_masks_to_image_masks(

            detection_masks, detection_boxes, image.shape[0], 

image.shape[1])

        detection_masks_reframed = tf.cast(

            tf.greater(detection_masks_reframed, 0.5), tf.uint8)

        # Follow the convention by adding back the batch dimension

        tensor_dict['detection_masks'] = tf.expand_dims(

            detection_masks_reframed, 0)

      image_tensor = 

tf.get_default_graph().get_tensor_by_name('image_tensor:0')



      # Run inference

      output_dict = sess.run(tensor_dict,

                             feed_dict={image_tensor: 

np.expand_dims(image, 0)})



      # all outputs are float32 numpy arrays, so convert types as 

appropriate

      output_dict['num_detections'] = 

int(output_dict['num_detections'][0])

      output_dict['detection_classes'] = output_dict[

          'detection_classes'][0].astype(np.uint8)

      output_dict['detection_boxes'] = output_dict['detection_boxes'] 

[0]

      output_dict['detection_scores'] = 

output_dict['detection_scores'][0]

      if 'detection_masks' in output_dict:

        output_dict['detection_masks'] = 

output_dict['detection_masks'][0]

  return output_dict



with detection_graph.as_default():

  with tf.Session(graph=detection_graph) as sess:

    while True:

      ret, image_np = cap.read()



     # Expand dimensions since the model expects images to have shape: 

[1, None, None, 3]

      image_np_expanded = np.expand_dims(image_np, axis=0)

      image_tensor = 

detection_graph.get_tensor_by_name('image_tensor:0')

      # Each box represents a part of the image where a particular 

object was detected.

      boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

      # Each score represent how level of confidence for each of the 

objects.

      # Score is shown on the result image, together with the class 

label.

      scores = 

detection_graph.get_tensor_by_name('detection_scores:0')

      classes = 

detection_graph.get_tensor_by_name('detection_classes:0')

      num_detections = 

detection_graph.get_tensor_by_name('num_detections:0')

      # Actual detection.

      (boxes, scores, classes, num_detections) = sess.run(

          [boxes, scores, classes, num_detections],

          feed_dict={image_tensor: image_np_expanded})

      # Visualization of the results of a detection.

      vis_util.visualize_boxes_and_labels_on_image_array(

          image_np,

          np.squeeze(boxes),

          np.squeeze(classes).astype(np.int32),

          np.squeeze(scores),

          category_index,

          use_normalized_coordinates=True,

          line_thickness=8)



      area1 = cv2.rectangle(image_np,(201,267),(355,476), 

   (0,255,0),2)

      area2 = cv2.rectangle(image_np,(354,271),(562,454), 

   (255,0,0),2)

      cv2.imshow("object detection", image_np)



      if 'detection_boxes:0' == 1 in area1[(201,267),(353,468)]:

        print("area1 occupied!")

      else:

        print("area1 free!")



      if 'detection_boxes:1' == 1 in area2[(354,271),(562,454)]:

        print("area2 occupied!")

      else:

        print("area2 free!")



      if cv2.waitKey(1) & 0xFF == ord('q'):

        cv2.destroyAllWindows()

        cap.release()

        break

I find it difficult to find a solution. Please help.

Technical information:

Tensorflow 1.10

OS - Ubuntu 18.04

Python 3.6

OpenCV 3.4.2

Thanks :)

You can use Intersection over Union for this. If the car is in your desired marked rectangle. IOU will have some value otherwise it will be zero.

When car rectangle is exactly in your marked rectangle, it will be close to 1 and that will be your solution