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203 lines
8.1 KiB
203 lines
8.1 KiB
#!/usr/bin/python3
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import numpy as np
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import os
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import sys
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import tensorflow as tf
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import cv2
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from distutils.version import StrictVersion
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import socket
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from utils import label_map_util
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from utils import visualization_utils as vis_util
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import psutil
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import json
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import base64
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from PIL import Image
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from io import BytesIO
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import psutil
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switch = 1
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# This is needed since the notebook is stored in the object_detection folder.
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sys.path.append("..")
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import time
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from object_detection.utils import ops as utils_ops
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if StrictVersion(tf.__version__) < StrictVersion('1.12.0'):
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raise ImportError('Please upgrade your TensorFlow installation to v1.12.*.')
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# What model to download.
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#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17' #not even worth trying
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MODEL_NAME="ssd_inception_v2_coco_11_06_2017" # not bad and fast
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MODEL_NAME="rfcn_resnet101_coco_11_06_2017" # WORKS BEST BUT takes 4 times longer per image
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#MODEL_NAME = "faster_rcnn_resnet101_coco_11_06_2017" # too slow
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MODEL_FILE = MODEL_NAME + '.tar.gz'
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DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
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# Path to frozen detection graph. This is the actual model that is used for the object detection.
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PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'
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# List of the strings that is used to add correct label for each box.
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PATH_TO_LABELS = os.path.join('object_detection/data', 'mscoco_label_map.pbtxt')
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detection_graph = tf.Graph()
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with detection_graph.as_default():
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od_graph_def = tf.GraphDef()
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with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
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serialized_graph = fid.read()
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od_graph_def.ParseFromString(serialized_graph)
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tf.import_graph_def(od_graph_def, name='')
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category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
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def load_image_into_numpy_array(image):
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(im_width, im_height) = image.size
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return np.array(image.getdata()).reshape(
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(im_height, im_width, 3)).astype(np.uint8)
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# For the sake of simplicity we will use only 2 images:
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# image1.jpg
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# image2.jpg
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# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
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PATH_TO_TEST_IMAGES_DIR = 'object_detection/test_images'
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TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(3, 6) ]
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# Size, in inches, of the output images.
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sess = 0
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switch = 1
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data = {"gpu_temp":"10C","gpu_load":"15%","cpu_temp":"47C","cpu_load":"15%","mem_temp":"NaN","mem_load":"17%","fan_speed":"10000RPM"}
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def get_temps():
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global data
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temps = psutil.sensors_temperatures()
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data["cpu_temp"] = str(int(temps["dell_smm"][0][1]))+"°C"
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data["cpu_load"] = str(psutil.cpu_percent())+"%"
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data["mem_load"] = str(dict(psutil.virtual_memory()._asdict())["percent"])+"%"
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data["fan_speed"] = str(psutil.sensors_fans()["dell_smm"][0][1])+"RPM"
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def run_inference_for_single_image(image, graph):
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global switch
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global sess
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with graph.as_default():
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if(switch):
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sess = tf.Session()
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switch = 0
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# Get handles to input and output tensors
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ops = tf.get_default_graph().get_operations()
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all_tensor_names = {output.name for op in ops for output in op.outputs}
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tensor_dict = {}
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for key in [
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'num_detections', 'detection_boxes', 'detection_scores',
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'detection_classes', 'detection_masks'
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]:
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tensor_name = key + ':0'
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if tensor_name in all_tensor_names:
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tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
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tensor_name)
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if 'detection_masks' in tensor_dict:
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# The following processing is only for single image
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detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
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detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
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# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
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real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
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detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
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detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
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detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
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detection_masks, detection_boxes, image.shape[1], image.shape[2])
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detection_masks_reframed = tf.cast(
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tf.greater(detection_masks_reframed, 0.5), tf.uint8)
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# Follow the convention by adding back the batch dimension
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tensor_dict['detection_masks'] = tf.expand_dims(
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detection_masks_reframed, 0)
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image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
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# Run inference
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output_dict = sess.run(tensor_dict,
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feed_dict={image_tensor: image})
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# all outputs are float32 numpy arrays, so convert types as appropriate
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output_dict['num_detections'] = int(output_dict['num_detections'][0])
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output_dict['detection_classes'] = output_dict[
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'detection_classes'][0].astype(np.int64)
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output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
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output_dict['detection_scores'] = output_dict['detection_scores'][0]
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if 'detection_masks' in output_dict:
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output_dict['detection_masks'] = output_dict['detection_masks'][0]
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return output_dict
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cut=[-175,-1,-175,-1]
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cut_send = [0,0,0,0]
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a = 1
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img_counter = 0
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socket_switch = True
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cam = cv2.VideoCapture(0)
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with detection_graph.as_default():
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sess = tf.Session()
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switch = 0
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get_temps()
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while 1:
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if(True):
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try:
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ret,image = cam.read()
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image_np = image[cut[0]:cut[1],cut[2]:cut[3]]
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#image_np = image_np[int(r[1]):int(r[1]+r[3]),int(r[0]):int(r[0]+r[2])]
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# the array based representation of the image will be used later in order to prepare the
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# result image with boxes and labels on it.
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# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
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image_np_expanded = np.expand_dims(image_np, axis=0)
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t1 = time.time()
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# Actual detection.
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output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)
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# Visualization of the results of a detection.
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vis_util.visualize_boxes_and_labels_on_image_array(
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image_np,
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output_dict['detection_boxes'],
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output_dict['detection_classes'],
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output_dict['detection_scores'],
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category_index,
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instance_masks=output_dict.get('detection_masks'),
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use_normalized_coordinates=True,
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line_thickness=8)
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image[cut[0]:cut[1],cut[2]:cut[3]] = image_np
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send_image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
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cv2.imshow("Cam",image)
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cv2.imshow("Cut",image_np)
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if socket_switch:
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try:
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client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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client_socket.connect(('127.0.0.1', 8485))
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connection = client_socket.makefile('wb')
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socket_switch = False
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except:
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socket_switch=True
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continue
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try:
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crop_img = send_image.copy(order='C')
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crop_img = Image.fromarray(crop_img,"RGB")
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buffered = BytesIO()
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crop_img.save(buffered, format="JPEG")
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img = base64.b64encode(buffered.getvalue()).decode("ascii")
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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")
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img_counter += 1
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except:
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socket_switch=True
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if img_counter % 10 ==0:
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get_temps()
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t2 = time.time()
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print("time taken for {}".format(t2-t1))
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ex_c = [27, ord("q"), ord("Q")]
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if cv2.waitKey(1) & 0xFF in ex_c:
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break
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except KeyboardInterrupt:
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if not socket_switch:
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client_socket.sendall(b"Bye\n")
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cam.release()
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exit(0)
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cv2.destroyAllWindows()
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cam.release()
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