#!/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()