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 __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
self.class_names = read_classes(project_path +
'/data/coco_classes.txt')
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()
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()
from utils.yolo_utils import get_training_data, read_anchors, read_classes, get_dac_batch_data
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()
# Add argument
def argument():
parser = argparse.ArgumentParser(description='COCO or VOC or boat')
parser.add_argument('--COCO', action='store_true', help='COCO flag')
parser.add_argument('--VOC', action='store_true', help='VOC flag')
parser.add_argument('--boat', action='store_true', help='boat flag')
args = parser.parse_args()
return args
# Get Data #############################################################################################################
PATH = path
classes_paths = PATH + '/model/classes.txt'
classes_data = read_classes(classes_paths)
anchors_paths = PATH + '/model/yolo_anchors.txt'
anchors = read_anchors(anchors_paths)
annotation_path_train = PATH + '/model/train.txt'
annotation_path_valid = PATH + '/model/valid.txt'
annotation_path_test = PATH + '/model/test.txt'
data_path_train = PATH + '/model/train.npz'
data_path_valid = PATH + '/model/valid.npz'
data_path_test = PATH + '/model/test.npz'
VOC = False
args = argument()
if args.VOC == True:
VOC = True
classes_paths = PATH + '/model/classes.txt'
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)
