MyCity/traffic_analyzer/ambulance_detect.py

#!/usr/bin/python3

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import cv2
from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO

from utils import label_map_util

from utils import visualization_utils as vis_util
from PIL import Image
switch = 1
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
import time
from object_detection.utils import ops as utils_ops

if StrictVersion(tf.__version__) < StrictVersion('1.12.0'):
  raise ImportError('Please upgrade your TensorFlow installation to v1.12.*.')

# What model to download.

#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17' #not even worth trying
MODEL_NAME="ssd_inception_v2_coco_11_06_2017" # not bad and fast
MODEL_NAME="rfcn_resnet101_coco_11_06_2017" # WORKS BEST BUT takes 4 times longer per image
#MODEL_NAME = "faster_rcnn_resnet101_coco_11_06_2017" # too slow
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_FROZEN_GRAPH = 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('object_detection/data', 'mscoco_label_map.pbtxt')

detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')
    
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)

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)
      
# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'object_detection/test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(3, 6) ]

# Size, in inches, of the output images.
sess = 0
switch = 1
def run_inference_for_single_image(image, graph):
  global switch
  global sess
  with graph.as_default():
    if(switch):
        sess = tf.Session()
        switch = 0
      # 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[1], image.shape[2])
      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: image})
    # 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.int64)
    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
cut=[-175,-1,-175,-1]
a = 1
cam = cv2.VideoCapture(0)
with detection_graph.as_default():
    sess = tf.Session()
    switch = 0
while 1:
    if(True):

        ret,image = cam.read()
        image_np = image[cut[0]:cut[1],cut[2]:cut[3]]
        #image_np = image_np[int(r[1]):int(r[1]+r[3]),int(r[0]):int(r[0]+r[2])]
        # the array based representation of the image will be used later in order to prepare the
        # result image with boxes and labels on it.

        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image_np, axis=0)
        t1 = time.time()
        # Actual detection.
        output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)
        # Visualization of the results of a detection.
        vis_util.visualize_boxes_and_labels_on_image_array(
            image_np,
            output_dict['detection_boxes'],
            output_dict['detection_classes'],
            output_dict['detection_scores'],
            category_index,
            instance_masks=output_dict.get('detection_masks'),
            use_normalized_coordinates=True,
            line_thickness=8)
        image[cut[0]:cut[1],cut[2]:cut[3]] = image_np
        cv2.imshow("Cam",np.concatenate((image,image_np),axis=0))
        t2 = time.time()
        print("time taken for {}".format(t2-t1))

        ex_c = [27, ord("q"), ord("Q")]
        if cv2.waitKey(1) & 0xFF in ex_c:
            break
cv2.destroyAllWindows()
cam.release()