#!/usr/bin/python3 import numpy as np import os import sys import tensorflow as tf import cv2 from distutils.version import StrictVersion import socket from utils import label_map_util from utils import visualization_utils as vis_util import psutil import json import base64 from PIL import Image from io import BytesIO import psutil 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 data = {"gpu_temp":"10C","gpu_load":"15%","cpu_temp":"47C","cpu_load":"15%","mem_temp":"NaN","mem_load":"17%","fan_speed":"10000RPM"} def get_temps(): global data temps = psutil.sensors_temperatures() data["cpu_temp"] = str(int(temps["dell_smm"][0][1]))+"°C" data["cpu_load"] = str(psutil.cpu_percent())+"%" data["mem_load"] = str(dict(psutil.virtual_memory()._asdict())["percent"])+"%" data["fan_speed"] = str(psutil.sensors_fans()["dell_smm"][0][1])+"RPM" 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] cut_send = [0,0,0,0] a = 1 img_counter = 0 socket_switch = True cam = cv2.VideoCapture(0) with detection_graph.as_default(): sess = tf.Session() switch = 0 get_temps() while 1: if(True): try: 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 send_image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB) cv2.imshow("Cam",image) cv2.imshow("Cut",image_np) if socket_switch: try: client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) client_socket.connect(('127.0.0.1', 8485)) connection = client_socket.makefile('wb') socket_switch = False except: socket_switch=True continue try: crop_img = send_image.copy(order='C') crop_img = Image.fromarray(crop_img,"RGB") buffered = BytesIO() crop_img.save(buffered, format="JPEG") img = base64.b64encode(buffered.getvalue()).decode("ascii") client_socket.sendall(json.dumps({"image_full":img,"image_sizes":{"x":cut_send[2],"y":cut_send[0],"width":cut_send[3],"height":cut_send[1]},"load":data}).encode('gbk')+b"\n") img_counter += 1 except: socket_switch=True if img_counter % 10 ==0: get_temps() 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 except KeyboardInterrupt: if not socket_switch: client_socket.sendall(b"Bye\n") cam.release() exit(0) cv2.destroyAllWindows() cam.release()