You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
294 lines
11 KiB
294 lines
11 KiB
#!/usr/bin/python3
|
|
|
|
import pickle
|
|
import threading
|
|
import sys
|
|
import cv2
|
|
import os
|
|
import numpy as np
|
|
|
|
from utils import label_map_util
|
|
from utils import visualization_utils as vis_util
|
|
|
|
if sys.platform == "win32":
|
|
|
|
import tensorflow as tf
|
|
from distutils.version import StrictVersion
|
|
|
|
if StrictVersion(tf.__version__) < StrictVersion('1.12.0'):
|
|
raise ImportError('Please upgrade your TensorFlow installation to v1.12.*.')
|
|
else:
|
|
import psutil
|
|
|
|
import json
|
|
|
|
import base64
|
|
from PIL import Image
|
|
from io import BytesIO
|
|
|
|
switch = 1
|
|
|
|
import socket
|
|
|
|
# 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
|
|
|
|
|
|
|
|
# What model to download.
|
|
|
|
|
|
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 90]
|
|
|
|
#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17' #not even worth trying
|
|
#MODEL_NAME = "ssd_inception_v2_coco_2017_11_17" # 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_NAME = "ssd_resnet101_v1_fpn_shared_box_predictor_oid_512x512_sync_2019_01_20"
|
|
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')
|
|
|
|
|
|
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
|
|
|
|
if sys.platform == "win32":
|
|
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='')
|
|
|
|
|
|
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
|
|
|
|
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
|
|
if not sys.platform == "win32":
|
|
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
|
|
|
|
kill = True
|
|
|
|
def listener(port=8385):
|
|
serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
|
serversocket.bind((socket.gethostname(), port))
|
|
serversocket.listen(5)
|
|
while kill:
|
|
serversocket.accept()
|
|
|
|
print('Bye!')
|
|
|
|
cut = [115, 100, 400, 150]
|
|
cut_send = [0, 0, 0, 0]
|
|
img_counter = 0
|
|
socket_switch = True
|
|
|
|
thread = threading.Thread(target=listener)
|
|
thread.start()
|
|
|
|
if sys.platform == "win32":
|
|
with detection_graph.as_default():
|
|
sess = tf.Session()
|
|
cam = cv2.VideoCapture(0)
|
|
else:
|
|
cam = cv2.VideoCapture('debug_data/amb_1.mp4')
|
|
with open("debug_data/frame_data.pkl","rb") as pkl_file:
|
|
frame_data = pickle.load(pkl_file)
|
|
|
|
switch = 0
|
|
get_temps()
|
|
amb_center = {'x': (400 + 550)/2, 'y': (115+215)/2}
|
|
|
|
reps = -1
|
|
reps_vid = 0
|
|
|
|
while 1:
|
|
ret,image = cam.read()
|
|
reps_vid += 1
|
|
if not sys.platform == "win32" and not reps_vid % 2 == 0:
|
|
continue
|
|
reps += 1
|
|
try: # Kavşak
|
|
t1 = time.time()
|
|
image_np = image
|
|
image_np_expanded = np.expand_dims(image_np, axis=0)
|
|
if sys.platform == "win32":
|
|
output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)
|
|
else:
|
|
output_dict = frame_data[reps]
|
|
|
|
height, width, channels = image_np.shape
|
|
|
|
out_dict = {'detection_boxes': [], 'detection_classes': [], 'detection_scores': []}
|
|
for i in output_dict['detection_classes']:
|
|
cont = False
|
|
if i in [3, 6, 8]: # Car, bus, truck
|
|
index = np.where(output_dict['detection_classes'] == i)[0][0]
|
|
score = output_dict['detection_scores'][index]
|
|
if score > 0.3:
|
|
if not any((output_dict['detection_boxes'][index] == b).all() for b in out_dict['detection_boxes']):
|
|
avg_x = (output_dict['detection_boxes'][index][0] + output_dict['detection_boxes'][index][2])/2
|
|
avg_y = (output_dict['detection_boxes'][index][1] + output_dict['detection_boxes'][index][3])/2
|
|
for box in out_dict['detection_boxes']:
|
|
avg_box_x = (box[0] + box[2])/2
|
|
avg_box_y = (box[1] + box[3])/2
|
|
if abs(avg_x-avg_box_x) < 0.1 and abs(avg_y-avg_box_y) < 0.1:
|
|
cont = True
|
|
continue
|
|
|
|
out_dict['detection_classes'].append(i)
|
|
out_dict['detection_boxes'].append(output_dict['detection_boxes'][index])
|
|
out_dict['detection_scores'].append(output_dict['detection_scores'][index])
|
|
|
|
out_dict['detection_classes'] = np.array(out_dict['detection_classes'])
|
|
out_dict['detection_boxes'] = np.array(out_dict['detection_boxes'])
|
|
out_dict['detection_scores'] = np.array(out_dict['detection_scores'])
|
|
|
|
print(len(out_dict['detection_classes']), ' cars.')
|
|
|
|
vis_util.visualize_boxes_and_labels_on_image_array(
|
|
image_np,
|
|
out_dict['detection_boxes'],
|
|
out_dict['detection_classes'],
|
|
out_dict['detection_scores'],
|
|
category_index,
|
|
instance_masks=out_dict.get('detection_masks'),
|
|
use_normalized_coordinates=True,
|
|
line_thickness=8,
|
|
min_score_thresh=0.3
|
|
)
|
|
cv2.imshow('frame', image_np)
|
|
ex_c = [27, ord("q"), ord("Q")]
|
|
if cv2.waitKey(1) & 0xFF in ex_c:
|
|
break
|
|
|
|
t2 = time.time()
|
|
print("time taken for {}".format(t2-t1))
|
|
if not sys.platform == "win32":
|
|
time.sleep(0.1-(t2-t1))
|
|
send_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
|
|
if socket_switch:
|
|
try:
|
|
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
|
client_socket.settimeout(0.1)
|
|
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")
|
|
lens = [len(send_image), 0, len(send_image[0])]
|
|
for i in range(0,len(cut), 2):
|
|
if cut[i] < 0:
|
|
cut_send[i] = lens[i] + cut[i]
|
|
cut_send[i+1] = abs(cut[i])-abs(cut[i+1])
|
|
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()
|
|
|
|
except Exception as e:
|
|
if hasattr(e, 'message'):
|
|
print(e.message)
|
|
else:
|
|
print(e)
|
|
break
|
|
|
|
|
|
|
|
if not socket_switch:
|
|
client_socket.sendall(b"Bye\n")
|
|
cam.release()
|
|
|
|
|
|
cv2.destroyAllWindows()
|
|
cam.release()
|
|
kill = False
|
|
thread.join()
|