def __init__(self):
self.anchors_path = './model_data/yolo_anchors.txt'
self.classes_path = './model_data/coco_classes.txt'
self.class_names = read_classes(self.classes_path)
self.anchors = read_anchors(self.anchors_path)
self.threshold = threshold
self.ignore_thresh = ignore_thresh
self.INPUT_SIZE = (Input_shape, Input_shape
) # fixed size or (None, None)
self.is_fixed_size = self.INPUT_SIZE != (None, None)
def vehicle_detection_YOLO(self,img):
#Read in the class names and anchor locations
self.class_names = yolo_utils.read_class_names(self.class_path)
self.anchors = yolo_utils.read_anchors(self.anchors_path)
#Pre-process the incoming frame
resized_img = cv2.resize(img,(416,416))
batch_img = resized_img/255.
batch_img = np.expand_dims(batch_img, axis=0)
#Perform the prediction
n_classes = len(self.class_names)
out_boxes, out_scores, out_classes = yolo_utils.YOLO_predict(img, batch_img, self.sess, self.model, self.anchors, n_classes)
return out_boxes, out_scores, out_classes
def __init__(self):
self.anchors_path = path + '/model/yolo_anchors.txt'
self.COCO = False
self.trainable = True
args1 = sys.argv[2]
if args1 == 'COCO':
print("-----------COCO-----------")
self.COCO = True
self.classes_path = path + '/model/coco_classes.txt'
self.trainable = False
elif args1 == 'VOC':
print("-----------VOC------------")
self.classes_path = path + '/model/voc_classes.txt'
elif args1 == 'boat':
print("-----------boat-----------")
self.classes_path = path + '/model/boat_classes.txt'
# args = self.argument()
# if args.COCO:
# print("-----------COCO-----------")
# self.COCO = True
# self.classes_path = self.PATH + '/model/coco_classes.txt'
# self.trainable = False
# elif args.VOC:
# print("-----------VOC------------")
# self.classes_path = self.PATH + '/model/voc_classes.txt'
# elif args.boat:
# print("-----------boat-----------")
# self.classes_path = self.PATH + '/model/boat_classes.txt'
self.class_names = read_classes(self.classes_path)
self.anchors = read_anchors(self.anchors_path)
self.threshold = 0.5 # threshold
self.ignore_thresh = ignore_thresh
self.INPUT_SIZE = (Input_shape, Input_shape
) # fixed size or (None, None)
self.is_fixed_size = self.INPUT_SIZE != (None, None)
# LOADING SESSION...
self.boxes, self.scores, self.classes, self.sess = self.load()
def __init__(self):
self.anchors_path = anchors_path
self.COCO = False
self.trainable = True
# self.args = self.argument()
if args.COCO:
print("-----------COCO-----------")
self.COCO = True
self.trainable = False
else:
print("----------{}-----------".format(dataset_name))
self.class_names = read_classes(dataset_class_file)
self.anchors = read_anchors(self.anchors_path)
self.threshold = threshold # threshold
self.ignore_thresh = ignore_thresh
self.INPUT_SIZE = (Input_shape, Input_shape
) # fixed size or (None, None)
self.is_fixed_size = self.INPUT_SIZE != (None, None)
# LOADING SESSION...
self.boxes, self.scores, self.classes, self.sess = self.load()
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
import argparse
import numpy as np
import tensorflow as tf
import time
import os
os.environ["CUDA_VISIBLE_DEVICES"] = visible_GPU
np.random.seed(101)
# PATH = path + '/yolo3'
classes_data = read_classes(dataset_class_file)
anchors = read_anchors(anchors_path)
data_path_train = 'Do_not_use_for_now'
data_path_valid = 'Do_not_use_for_now'
data_path_test = 'Do_not_use_for_now'
input_shape = (Input_shape, Input_shape) # multiple of 32
########################################################################################################################
"""
# Clear the current graph in each run, to avoid variable duplication
# tf.reset_default_graph()
"""
print("Starting 1st session...")
# Explicitly create a Graph object
graph = tf.Graph()
with graph.as_default():
colors = generate_colors(class_names)
image = draw_boxes(image, out_scores, out_boxes, out_classes, class_names,
colors)
return image
if __name__ == "__main__":
sess = K.get_session()
yolo_model = load_model("model_data/tiny-yolo.h5")
#yolo_model.summary()
class_names = read_classes("model_data/yolo_coco_classes.txt")
anchors = read_anchors("model_data/yolo_anchors.txt")
'''
# image detection
image_file = "dog.jpg"
image_path = "images/"
image_shape = np.float32(cv2.imread(image_path + image_file).shape[:2])
yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
scores, boxes, classes = yolo_eval(yolo_outputs, image_shape=image_shape)
out_scores, out_boxes, out_classes = image_detection(sess, image_path, image_file)
'''
# video detection
camera = cv2.VideoCapture(0)
#camera.set(cv2.CAP_PROP_FRAME_WIDTH, 288) # 設計解析度