def __init__(self):
print("[INFO] Loading model...")
global yolo
yolo = Load_Yolo_model()
print("[INFO] Loading modules...")
self.bridge = CvBridge()
classNum = len(list(read_class_names(YOLO_COCO_CLASSES).values()))
self.ClassNames = read_class_names(YOLO_COCO_CLASSES)
self.OH = Object_handler(classNum)
print("[INFO] Loading videofeed...")
self.image_sub_depth = rospy.Subscriber(
"/zed2/zed_node/depth/depth_registered", Image,
self.callback_depth)
self.image_sub_camera = rospy.Subscriber(
"/zed2/zed_node/left/image_rect_color", Image, self.callback_cam)
print("[INFO] initializing config...")
self.show = 1
self.dep_active = 0
self.cal_active = 0
self.min_depth = 0.3
#self.Update_Images()
print("[INFO] Loading complete")
def __init__(self):
print("[INFO] Loading model...")
#global yolo
self.yolo = Load_Yolo_model()
print("[INFO] Loading modules...")
self.bridge = CvBridge()
classNum = len(list(read_class_names(YOLO_COCO_CLASSES).values()))
self.ClassNames = read_class_names(YOLO_COCO_CLASSES)
self.OH = Object_handler(classNum)
print("[INFO] Loading videofeed...")
image_sub = message_filters.Subscriber(
"/zed2/zed_node/left/image_rect_color", Image)
cloud_sub = message_filters.Subscriber(
"/zed2/zed_node/point_cloud/cloud_registered", PointCloud2)
self.pose_pub = rospy.Publisher('/Published_pose',
PoseStamped,
queue_size=1)
self.reduc_cloud_pub = rospy.Publisher("/Reduced_cloud",
PointCloud2,
queue_size=1)
print("[INFO] initializing config...")
self.show = False # Show tracker
self.seg_plot = False # Create segmentation plot
print("[INFO] Initialize Display...")
Frank = cv2.imread(
os.path.join(os.path.dirname(__file__), "Frank/Frank.png"),
cv2.IMREAD_COLOR)
print("[INFO] Loading complete")
mf = message_filters.TimeSynchronizer([image_sub, cloud_sub], 1)
mf.registerCallback(self.callback)
def __init__(self):
self.no_decbbox = 0
print("[INFO] Initializing ROS...")
rospy.init_node('Detection')
print("[INFO] Loading modules...")
self.yolo = Load_Yolo_model()
self.bridge = CvBridge()
print("[INFO] Loading config...")
# Create local variables
self.timer = TimeReference()
print("[INFO] Initialize ROS publishers...")
# Create Topics to publish
self.boxes_pub = rospy.Publisher("/Tracker/Detection/Boxes",
Detection2DArray,
queue_size=1)
self.timer_pub = rospy.Publisher("/Tracker/Timer",
TimeReference,
queue_size=1)
print("[INFO] Initialize ROS Subscribers...")
rospy.Subscriber("/zed2/zed_node/left/image_rect_color/compressed",
CompressedImage,
self.callback,
queue_size=400)
# Create subscriptions
print("[INFO] Loading complete")
def detection(request):
# base64이미지를 jpg로 저장
im = get_image_from_data_url(request.data['image'])
image = Images()
image.images = im
image.save()
image = Images.objects.last()
image.images = im
image.save()
# 비교할 정답
answer = request.data['question']
# YOLO사용 준비
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
video_path = ""
# YOLO 모델로 detecting
last_image = Images.objects.last().images
yolo = Load_Yolo_model()
is_correct, info = detect_image(yolo,
image.images.url,
answer,
input_size=YOLO_INPUT_SIZE,
show=True,
rectangle_colors=(255, 0, 0))
return Response({
'message': '사진 테스트 완료!!',
'is_correct': is_correct,
'info': info
})
def predict():
if request.method == 'POST':
# Read File and file data
im_data = request.get_data()
with open("capture.jpg", "wb") as im:
im.write(base64.decodebytes(im_data))
# image_path = file_path
image_path = 'capture.jpg'
# Model expects a 4 dimensional Tensor that has the layout: [Batch index,Width,Height,Channel]
# Make prediction
yolo = Load_Yolo_model()
image, score, label = detect_image(yolo,
image_path,
"detect.jpg",
input_size=YOLO_INPUT_SIZE,
show=True,
CLASSES=TRAIN_CLASSES,
rectangle_colors=(255, 0, 0))
label = str(label)
score = str(score)
result = str([score, label])
return result
# detect_predict(data)
# return get_detected_object
return None
def __init__(self):
print("[INFO] Initializing ROS...")
rospy.init_node('Detection')
print("[INFO] Loading modules...")
self.yolo = Load_Yolo_model()
self.bridge = CvBridge()
print("[INFO] Loading config...")
self.timer = TimeReference()
print("[INFO] Initialize ROS publishers...")
self.boxes_pub = rospy.Publisher("/Tracker/Detection/Boxes",
Detection2DArray,
queue_size=1)
self.timer_pub = rospy.Publisher("/Tracker/Timer",
TimeReference,
queue_size=1)
print("[INFO] Initialize ROS Subscribers...")
rospy.Subscriber("/zed2/zed_node/left/image_rect_color/compressed",
CompressedImage,
self.callback_sub,
queue_size=1)
print("[INFO] Loading complete")
self.init_time_delay = 0
self.callback()
def __init__(self, detect_classes=None):
'''
YOLOv3 TF2 module class for vehicle inference, training, crop. Supports object both normal and tiny implementations.
TO DO: 1.add tiny and normal implementations
2. add choice of detect_classes
'''
detect_classes = detect_classes
VehicleDetector.yolo_obj = Load_Yolo_model()
class YoloClass():
yolo = Load_Yolo_model()
def load_cnn_data(self, double_check_list):
w, h = 128, 128
x = []
for fp in double_check_list:
img = cv2.resize(fp, (w, h), interpolation=cv2.INTER_AREA)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
x.append(img_rgb)
x_arr = np.asarray(x).astype('float32')
x_arr = x_arr / 255
x_arr = x_arr.reshape(x_arr.shape[0], w, h, 3)
return x_arr
_name = ["牛肉", "水煮蛋", "花椰菜", "高麗菜", "雞肉", "鯖魚", "番薯", "鮭魚", "番茄"]
def food_name(self, food):
return [self._name[i] for i in food]
def aiPredict(self, image_path):
# block_image_path = "./block"
# image_path = "./test10.jpg"
# video_path = "./IMAGES/test2.mp4"
image = cv2.imread(image_path)
_, cord = detect_image(self.yolo,
image_path,
output_path="./answer.jpg",
input_size=YOLO_INPUT_SIZE,
show=False,
CLASSES=TRAIN_CLASSES,
rectangle_colors=(255, 0, 0))
double_check_list = []
for i, block in enumerate(cord):
x1 = block[0][0]
x2 = block[1][0]
y1 = block[0][1]
y2 = block[1][1]
crop_img = image[y1:y2, x1:x2]
double_check_list.append(crop_img)
#cv2.imwrite(f"./block/block_{i}.jpg", crop_img)
x_test = self.load_cnn_data(double_check_list)
reload_model = tf.keras.models.load_model(
'./cnn_model/resNet_model3_w128_h128.h5')
predict_class = reload_model.predict(x_test)
predict_class = np.argmax(predict_class, axis=1)
ret = self.food_name(predict_class)
# print(ret)
return ret
def __init__(self):
global yolo
self.image_pub = rospy.Publisher("image_topic_2", Image, queue_size=1)
self.bridge = CvBridge()
#self.image_sub = rospy.Subscriber("/usb_cam/image_raw",Image,self.callback)
self.image_sub = rospy.Subscriber(
"/zed2/zed_node/left/image_rect_color", Image,
self.callback) #, queue_size=1,buff_size=2**24)
#self.image_sub = rospy.Subscriber("/zed2/zed_node/left/image_rect_color/compressed",CompressedImage,self.callback)#, queue_size=1,buff_size=2**8)
yolo = Load_Yolo_model()
def __init__(self):
global yolo
self.show=1 # 0: don't show 1: show
self.active=1#0
self.cv_image_cam=[]
self.cv_image_depth=[]
self.image_pub = rospy.Publisher("image_topic_2",Image)
self.bridge = CvBridge()
self.image_sub_camera = rospy.Subscriber("/zed2/zed_node/left/image_rect_color",Image,self.callback_cam)
self.image_sub = rospy.Subscriber("/zed2/zed_node/depth/depth_registered",Image,self.callback_depth)
yolo = Load_Yolo_model()
def __init__(self):
print("[INFO] Loading model...")
#global yolo
self.yolo = Load_Yolo_model()
print("[INFO] Loading modules...")
self.bridge = CvBridge()
classNum = len(list(read_class_names(YOLO_COCO_CLASSES).values()))
self.ClassNames = read_class_names(YOLO_COCO_CLASSES)
self.OH = Object_handler(classNum)
print("[INFO] Loading videofeed...")
image_sub = message_filters.Subscriber(
"/zed2/zed_node/left/image_rect_color", Image)
cloud_sub = message_filters.Subscriber(
"/zed2/zed_node/point_cloud/cloud_registered", PointCloud2)
#Odometry_sub = message_filters.Subscriber('/odometry/filtered_map', Odometry)
#self.vehicle_pose = rospy.Subscriber('/odometry/filtered_map', Odometry, self.callback_odom)
self.pose_pub = rospy.Publisher('/Published_pose',
PoseStamped,
queue_size=1)
self.reduc_cloud_pub = rospy.Publisher("/Reduced_cloud",
PointCloud2,
queue_size=1)
#self.seg_plot_pub = rospy.Publisher("/seg_img",Image,queue_size=1)
self.timed_cloud_pub = rospy.Publisher("/Timed_cloud",
PointCloud2,
queue_size=1)
self.boxed_image_pub = rospy.Publisher("/Boxed_image",
Image,
queue_size=1)
print("[INFO] initializing config...")
self.Target_class = 0 # Class 0 is person
self.Target_Found = False
self.Target_UID = []
self.show = True # Show tracker
self.seg_plot = True # Create segmentation plot
print("[INFO] Initialize Display...")
Frank = cv2.imread(
os.path.join(os.path.dirname(__file__), "Frank/Frank.png"),
cv2.IMREAD_COLOR)
print("[INFO] Loading complete")
#mf = message_filters.ApproximateTimeSynchronizer([image_sub,depth_sub,cloud_sub],1,0.07)
#mf = message_filters.ApproximateTimeSynchronizer([image_sub,cloud_sub],1,5) #Set close to zero in order to syncronize img and point cloud (be aware of frame rate)
mf = message_filters.TimeSynchronizer([image_sub, cloud_sub], 10)
mf.registerCallback(self.callback)
def __init__(self):
print("[INFO] Loading model...")
global yolo
yolo = Load_Yolo_model()
print("[INFO] Loading modules...")
self.bridge = CvBridge()
classNum = len(list(read_class_names(YOLO_COCO_CLASSES).values()))
self.ClassNames = read_class_names(YOLO_COCO_CLASSES)
self.OH = Object_handler(classNum)
print("[INFO] Loading videofeed...")
self.image_sub = rospy.Subscriber("/usb_cam/image_raw",Image,self.callback)
print("[INFO] Loading complete")
def __init__(self):
self.yolo = Load_Yolo_model()
self.bridge = CvBridge()
self.bbox_pub = rospy.Publisher("/yolo/bboxes",
Detection2DArray,
queue_size=1)
self.time_pub = rospy.Publisher("/yolo/Time",
CompressedImage,
queue_size=1)
time_sub = rospy.Subscriber(
"/zed2/zed_node/left/image_rect_color/compressed", CompressedImage,
self.callback_images)
some_sub = rospy.Subscriber("/yolo/Time", CompressedImage,
self.callback_yolo)
def __init__(self):
self.yolo = Load_Yolo_model()
self.bridge = CvBridge()
self.bbox_pub = rospy.Publisher("/yolo/bboxes",
Detection2DArray,
queue_size=1)
self.time_pub = rospy.Publisher("/yolo/Timer",
Image,
queue_size=1,
tcp_nodelay=True)
rospy.Subscriber("/zed2/zed_node/left/image_rect_color", Image,
self.callback_images)
rospy.Subscriber("/yolo/Time",
Image,
self.callback_yolo,
tcp_nodelay=True)
def __init__(self):
print("[INFO] Loading model...")
#global yolo
self.yolo = Load_Yolo_model()
print("[INFO] Loading modules...")
self.bridge = CvBridge()
classNum = len(list(read_class_names(YOLO_COCO_CLASSES).values()))
self.ClassNames = read_class_names(YOLO_COCO_CLASSES)
self.OH = Object_handler(classNum)
print("[INFO] Loading videofeed...")
image_sub = message_filters.Subscriber(
"/zed2/zed_node/left/image_rect_color", Image)
#depth_sub = message_filters.Subscriber("/zed2/zed_node/depth/depth_registered",Image)
cloud_sub = message_filters.Subscriber(
"/zed2/zed_node/point_cloud/cloud_registered", PointCloud2)
print("[INFO] initializing config...")
#self.show=1
self.seg_plot = True
#self.dep_active = 0
#self.cal_active = 0
#self.min_depth = 0.3
print("[INFO] Initialize Display...")
Frank = cv2.imread(
os.path.join(os.path.dirname(__file__), "Frank/Frank.png"),
cv2.IMREAD_COLOR)
#cv2.imshow("Image_window",Frank)
#detect_image(yolo, Frank, "", input_size=YOLO_INPUT_SIZE, show=False, rectangle_colors=(255,0,0))
#self.Update_Images()
print("[INFO] Loading complete")
#mf = message_filters.ApproximateTimeSynchronizer([image_sub,depth_sub,cloud_sub],1,0.07)
#mf = message_filters.ApproximateTimeSynchronizer([image_sub,cloud_sub],1,0.07)
mf = message_filters.TimeSynchronizer([image_sub, cloud_sub], 1)
mf.registerCallback(self.callback)
def __init__(self):
print("[INFO] Loading model...")
global yolo
yolo = Load_Yolo_model()
print("[INFO] Loading modules...")
self.bridge = CvBridge()
classNum = len(list(read_class_names(YOLO_COCO_CLASSES).values()))
self.ClassNames = read_class_names(YOLO_COCO_CLASSES)
self.OH = Object_handler(classNum)
print("[INFO] Loading videofeed...")
image_sub = message_filters.Subscriber(
"/zed2/zed_node/left/image_rect_color", Image)
cloud_sub = message_filters.Subscriber(
"/zed2/zed_node/point_cloud/cloud_registered", PointCloud2)
mf = message_filters.ApproximateTimeSynchronizer(
[image_sub, cloud_sub], 1, 10)
mf.registerCallback(self.callback)
print("INFO Initialize display")
print("[INFO] Loading complete")
# ================================================================ # # File name : detection_custom.py # Description : Performs object detection for images # #================================================================ import os os.environ['CUDA_VISIBLE_DEVICES'] = '0' import cv2 import numpy as np import tensorflow as tf from yolov3.utils import detect_image, detect_realtime, detect_video, Load_Yolo_model, detect_video_realtime_mp from yolov3.configs import * image_path = "/home/014505660/fl_project/kitti_dataset/testing/image_2/000010.png" yolo = Load_Yolo_model() detect_image(yolo, image_path, "./IMAGES/test_1_detect.jpg", input_size=YOLO_INPUT_SIZE, show=False, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import cv2
import numpy as np
import tensorflow as tf
import tensorflowjs as tfjs
from yolov3.utils import detect_image, detect_realtime, detect_video, Load_Yolo_model, detect_video_realtime_mp
from yolov3.configs import *
yolo = Load_Yolo_model()
yolo.summary()
# yolo.save('mymodel.h5')
# filepath = 'tfjs-models2'
# filepath = 'tfjs-models4'
# tf.keras.models.save_model(
# yolo, filepath, overwrite=True, include_optimizer=True, save_format=None,
# signatures=None, options=None
# )
# tf.saved_model.save(yolo, 'tfjs-models3')
# tfjs.converters.save_keras_model(yolo, 'tfjs-models')