def main(): # 创建一个解析器对象,并告诉它将会有些什么参数。当程序运行时,该解析器就可以用于处理命令行参数。 parser = argparse.ArgumentParser() # 定义参数 parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) # 定义了所有参数之后,你就可以给 parse_args() 传递一组参数字符串来解析命令行。默认情况下,参数是从 sys.argv[1:] 中获取 # parse_args() 的返回值是一个命名空间,包含传递给命令的参数。该对象将参数保存其属性 args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu # 设置环境变量 yolo = YOLONet(False) # 创建YOLO网络对象 weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) # 加载检查点文件 detector = Detector(yolo, weight_file) # 创建测试对象 # detect from camera # cap = cv2.VideoCapture(-1) # detector.camera_detector(cap) # detect from image file # imname = 'test/person.jpg' # detector.image_detector(imname) # detect from camera # GTX 1060 25FPS detector.camera_detector()
def main(): ''' parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu ''' datautil = MyDataUtil('DIYdata', 'test') yolo = YOLONet(False) #weight_file = os.path.join('DIYdata', 'output/V3', 'yolo.ckpt') #detector = Detector(yolo, weight_file) #weight_dir = os.path.join('DIYdata', 'output/V3', 'yolo.ckpt') #detector = Detector(yolo, weight_dir=weight_dir) detector = Detector( yolo, weight_file= '/home/wlk/Develop/gitDownload/yolo_tensorflow/data/YOLO_small.ckpt') # detect from camera cap = cv2.VideoCapture(-1) detector.camera_detector(cap)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--weights', default="D:\\reference\\5-dataset\\YOLO_small.ckpt", type=str) # 预训练的数据集 parser.add_argument('--data_dir', default="D:\\reference\\5-dataset", type=str) # voc数据集文件夹pascal_voc所在路径 parser.add_argument('--threshold', default=0.2, type=float) # 格子有目标的置信度阈值 parser.add_argument('--iou_threshold', default=0.5, type=float) parser.add_argument('--gpu', default='0', type=str) args = parser.parse_args() if args.gpu is not None: cfg.GPU = args.gpu if args.data_dir != cfg.DATA_PATH: update_config_paths(args.data_dir, args.weights) os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU # 获取系统环境变量 yolo = YOLONet() pascal = pascal_voc('train') solver = Solver(yolo, pascal) print('Start training ...') solver.train() print('Done training.')
def main(): #自定义参数 parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) #定义权重文件 parser.add_argument('--data_dir', default="data", type=str) #定义数据文件夹 parser.add_argument('--threshold', default=0.2, type=float) #阈值 parser.add_argument('--iou_threshold', default=0.5, type=float) #IOU阈值 parser.add_argument('--gpu', default='', type=str) #是否用gpu训练 args = parser.parse_args() if args.gpu is not None: #是否用gpu训练 cfg.GPU = args.gpu if args.data_dir != cfg.DATA_PATH: update_config_paths(args.data_dir, args.weights) os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU yolo = YOLONet() #Yolo网络 pascal = pascal_voc('train') #获得训练的数据, 包含了经过水平翻转后的训练实例 solver = Solver(yolo, pascal) #准备训练的环境,包括设置优化器,学习率等内容 print('Start training ...') solver.train() #start training print('done!!!')
def main(): parser = argparse.ArgumentParser() # parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weights', default="yolo-1000.ckpt", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu yolo = YOLONet(False) weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) detector = Detector(yolo, weight_file) # detect from camera # cap = cv2.VideoCapture(-1) # detector.camera_detector(cap) # detect from image file # imname = 'test/person.jpg' imname = 'data/test_jpg/CAM01-2014-02-15-20140215161032-20140215162620-frame415.jpg' # imname = 'data/test_jpg/CAM01-2014-02-20-20140220170007-20140220171314-frame4700.jpg' detector.image_detector(imname)
def main(): parser = argparse.ArgumentParser() #参数解析 parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weight_dir', default='./YOLO_small.ckpt', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu yolo = YOLONet(False) #定义网络的框架 # weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) #模型文件路径 weight_file = args.weight_dir detector = Detector(yolo, weight_file) #初始化Detector类 # detect from camera # cap = cv2.VideoCapture(-1) # detector.camera_detector(cap) # detect from image file imname = 'test/street1.jpeg' #测试文件 detector.image_detector(imname) imname = 'test/car2.jpeg' detector.image_detector(imname) imname = 'test/car3.jpeg' detector.image_detector(imname) imname = 'test/person2.jpeg' detector.image_detector(imname)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu yolo = YOLONet(False) weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) detector = Detector(yolo, weight_file) # detect from camera # cap = cv2.VideoCapture(-1) # detector.camera_detector(cap) # detect from image file lb = 1 ub = 5 for i in xrange(lb, ub + 1): folderName = 'test/' index = i nZeroes = 6 - len(str(index)) fileName = '0' * nZeroes + str(index) + '.jpg' imageName = os.path.join(folderName, fileName) imname = imageName detector.image_detector(imname)
def main(): parser = argparse.ArgumentParser() # 创建一个解析器对象,并告诉它将会有些什么参数 # 当程序运行时,该解析器就可以用于处理命令行参数 '''定义参数''' parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) # 权重文件名 parser.add_argument('--data_dir', default="data", type=str) # 数据集目录 parser.add_argument('--threshold', default=0.2, type=float) # 目标存在阈值 parser.add_argument('--iou_threshold', default=0.5, type=float) parser.add_argument('--gpu', default='', type=str) # 定义了所有参数之后,就可以给parse_args()传递一组参数字符串来解析命令行 # 默认情况下参数从sys.argv[1:]中获取 args = parser.parse_args() #将命令行中的参数保存相应的属性 if args.gpu is not None: cfg.GPU = args.gpu # 判断是否使用GPU if args.data_dir != cfg.DATA_PATH: # 检查当前数据集路径,并进行更新 update_config_paths(args.data_dir, args.weights) os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU # 设置环境变量 yolo = YOLONet() #创建yolo pascal = pascal_voc('train') #数据集 solver = Solver(yolo, pascal) # 求解器 print('Start training ...') solver.train() print('Done training.')
def main(): ''' parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--threshold', default=0.2, type=float) parser.add_argument('--iou_threshold', default=0.5, type=float) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() args = easydict.EasyDict( { "weights":"YOLO_small.ckpt", "data_dir":"data", "threshold":0.2, "gpu":"", "iou_threshold":0.5 }) if args.gpu is not None: cfg.GPU = args.gpu if args.data_dir != cfg.DATA_PATH: update_config_paths(args.data_dir, args.weights) os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU ''' yolo = YOLONet() print("build yolo model done") pascal = pascal_voc('train') solver = Solver(yolo, pascal) print('Start training ...') #solver.train() print("假装已经训练玩啦") print('Done training.')
def main(): parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() # os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU yolo = YOLONet(False) # weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) weight_file = cfg.WEIGHTS_DIR detector = Detector(yolo, weight_file) # detect from camera # cap = cv2.VideoCapture(0) # detector.camera_detector(cap) # detect from image file # imname = os.path.join(cfg.PASCAL_PATH, 'VOCdevkit', 'VOC2007+2012', 'JPEGImages', '000005.jpg') imname = './test/person.jpg' # detector.detect_test(imname, 5000) detector.detect_non_max(imname, 5000)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--threshold', default=0.2, type=float) parser.add_argument('--iou_threshold', default=0.5, type=float) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() if args.gpu is not None: cfg.GPU = args.gpu if args.data_dir != cfg.DATA_PATH: update_config_paths(args.data_dir, args.weights) os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU yolo = YOLONet() pascal = pascal_voc('train') solver = Solver(yolo, pascal) print('Start training ...') solver.train() print('Done training.')
def main(): parser = argparse.ArgumentParser() # parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weights', default="save.ckpt-19000", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu yolo = YOLONet(False) # weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) weight_file = os.path.join(args.data_dir, 'pascal_voc', 'output', '2018_01_03_09_17', args.weights) detector = Detector(yolo, weight_file) # detect from camera # cap = cv2.VideoCapture(-1) # detector.camera_detector(cap) # detect from image file imname = 'test/000015.jpg' # image = cv2.imread(imname) # cv2.rectangle(image, (20, 20), (250, 250), (255, 0, 0), 10) # cv2.imshow('a',image) # cv2.waitKey(0) detector.image_detector(imname)
def main(): # 创建一个解析器对象,并告诉它将会有些什么参数。当程序运行时,该解析器就可以用于处理命令行参数。 parser = argparse.ArgumentParser() # 定义参数 parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) # 权重文件名 parser.add_argument('--data_dir', default="data", type=str) # 数据集路径 parser.add_argument('--threshold', default=0.2, type=float) parser.add_argument('--iou_threshold', default=0.5, type=float) parser.add_argument('--gpu', default='', type=str) # 定义了所有参数之后,你就可以给 parse_args() 传递一组参数字符串来解析命令行。默认情况下,参数是从 sys.argv[1:] 中获取 # parse_args() 的返回值是一个命名空间,包含传递给命令的参数。该对象将参数保存其属性 args = parser.parse_args() # 判断是否是使用gpu if args.gpu is not None: cfg.GPU = args.gpu # 设定数据集路径,以及检查点文件路径 if args.data_dir != cfg.DATA_PATH: update_config_paths(args.data_dir, args.weights) # 设置环境变量 os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU yolo = YOLONet() # 创建YOLO网络对象 pascal = pascal_voc('train') # 数据集对象 solver = Solver(yolo, pascal) # 求解器对象 print('Start training ...') solver.train() # 开始训练 print('Done training.')
def main(): # TO clear defualt graph and nodes tf.reset_default_graph() # here, no use parse, only use default value to set weight parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) yolo = YOLONet(False) detector = Detector(yolo, weight_file) # ---data source select only choose one--- # ----'detect from camera' # cap = cv2.VideoCapture(0) # detector.camera_detector(cap) # ----'detect from image file' imname = './test/bahe.jpg' detector.image_detector(imname)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--weights', default="save.ckpt-30000", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='/gpu:0', type=str) parser.add_argument('--mode', default='0', type=str) args = parser.parse_args() #os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu yolo = YOLONet(False) weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) detector = Detector(yolo, weight_file) # detect from image file if args.mode == '0': list = np.random.randint(0,1019,5) for i in list: img = "data/mydata/Pic/img/"+str(i)+".jpg" print img detector.image_detector(img) #detect from camera elif args.mode == '1': cap = cv2.VideoCapture(0) cap.set(cv2.cv.CV_CAP_PROP_FPS, 30) detector.camera_detector(cap)
def oncle(self): if self.statusbar.currentMessage() != '': if self.comboBox.currentText() == "YOLO": parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu yolo = YOLONet(False) weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) detector = Detector(yolo, weight_file) # detect from camera # cap = cv2.VideoCapture(-1) # detector.camera_detector(cap) # detect from image file imname = self.file detector.image_detector(imname) scene = QGraphicsScene() pixmap = QPixmap("D:\\result.jpg") scene.addPixmap(pixmap) self.graphicsView_2.setScene(scene) elif self.comboBox.currentText() == "MaskRCNN": self.maskRCNN = demo.MaskRCNN() self.maskRCNN.detect(self.file) scene = QGraphicsScene() pixmap = QPixmap("D:\\result3.jpg") scene.addPixmap(pixmap) self.graphicsView_2.setScene(scene)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--threshold', default=0.2, type=float) parser.add_argument('--iou_threshold', default=0.5, type=float) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() if args.gpu is not None: cfg.GPU = args.gpu os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU if args.data_dir != cfg.DATA_PATH: update_config_paths(args.data_dir, args.weights) yolo = YOLONet(True) data = pascal_voc('train') for i in range(10): display_gt(data.gt_labels[i]) solver = Solver(yolo, data) print('Start training ...') solver.train() print('Done training.')
def main(): yolo = YOLONet(False) weight_file = 'YOLO_small.ckpt' # model directory detector = Detector(yolo, weight_file) # detect from camera cap = cv2.VideoCapture('test.mp4') # test video detector.camera_detector(cap)
def oncedectect(self): if self.file != '': _translate = QtCore.QCoreApplication.translate classesall = ['person', 'car', 'cat', 'dog'] classesallnum = [7, 8, 15, 12] detect_timer = Timer() yolo = YOLONet(False) detector = Detector(yolo) detect_timer.tic() yolostringresults = detector.image_detector(self.file, classesall) yolotime = detect_timer.toc() self.yoloTime.setText(_translate("MainWindow", str(yolotime) + 's')) pixmap = QPixmap("D:\\result.jpg") self.yolo_result.setPixmap(pixmap) self.yolo_result.setScaledContents(True) self.save.alt('YOLO', yolotime) self.save.dl(self.fileName(), 'YOLO', yolostringresults) self.maskRCNN = demo.MaskRCNN() detect_timer.tic() maskRCNNstringresults = self.maskRCNN.detect(self.file, classesall) MaskRCNNtime = detect_timer.toc() self.maskTime.setText( _translate("MainWindow", str(MaskRCNNtime) + 's')) pixmap = QPixmap("D:\\result2.jpg") self.Mask_result.setPixmap(pixmap) self.Mask_result.setScaledContents(True) self.save.alt('MaskRCNN', MaskRCNNtime) self.save.dl(self.fileName(), 'MaskRCNN', maskRCNNstringresults) detect_timer.tic() ssdstringresult = ssd_notebook.dome(self.file, classesallnum) SSDtime = detect_timer.toc() self.ssdTime.setText(_translate("MainWindow", str(SSDtime) + 's')) pixmap = QPixmap("D:\\result3.jpg") self.SSD_result.setPixmap(pixmap) self.SSD_result.setScaledContents(True) self.save.alt('SSD', SSDtime) self.save.dl(self.fileName(), 'SSD', ssdstringresult) detect_timer.tic() fasterrcnnresult = test.dectect(self.file, classesall) fasterTime = detect_timer.toc() self.fasterTime.setText( _translate("MainWindow", str(fasterTime) + 's')) pixmap = QPixmap(r"D:\result4.jpg") self.Faster_result.setPixmap(pixmap) self.Faster_result.setScaledContents(True) self.save.alt('FasterRCNN', fasterTime) self.save.dl(self.fileName(), 'FasterRCNN', fasterrcnnresult)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() yolo = YOLONet(False) weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) detector = Detector(yolo, weight_file) imname = 'test/image1.jpg' detector.image_detector(imname)
def main(): os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU yolo = YOLONet('test') weight_file = 'data/weights/YOLO_small.ckpt' detector = Detector(yolo, weight_file) # detect from camera cap = cv2.VideoCapture(-1) detector.camera_detector(cap) # detect from image file imname = 'test/person.jpg' detector.image_detector(imname)
def oncle(self): if self.file != '': if self.checkBox.isChecked(): self.classes.append("person") if self.checkBox_2.isChecked(): self.classes.append("car") if self.checkBox_3.isChecked(): self.classes.append("dog") if self.checkBox_4.isChecked(): self.classes.append("cat") if self.comboBox.currentText() == "YOLO": yolo = YOLONet(False) detector = Detector(yolo) # detect from camera # cap = cv2.VideoCapture(-1) # detector.camera_detector(cap) # detect from image file imname = self.file detector.image_detector(imname, self.classes) pixmap = QPixmap("D:\\result.jpg") self.image2.setPixmap(pixmap) self.image2.setScaledContents(True) elif self.comboBox.currentText() == "MaskRCNN": self.maskRCNN = demo.MaskRCNN() self.maskRCNN.detect(self.file, self.classes) scene = QGraphicsScene() pixmap = QPixmap("D:\\result2.jpg") self.image2.setPixmap(pixmap) self.image2.setScaledContents(True) elif self.comboBox.currentText() == "SSD": classes1 = [] for item in self.classes: if (item == 'person'): classes1.append(15) elif (item == 'dog'): classes1.append(12) elif (item == 'cat'): classes1.append(8) elif (item == 'car'): classes1.append(7) ssd_notebook.dome(self.file, classes1) pixmap = QPixmap("D:\\result3.jpg") self.image2.setPixmap(pixmap) self.image2.setScaledContents(True) elif self.comboBox.currentText() == "FasterRCNN": test.dectect(self.file, self.classes) pixmap = QPixmap(r"D:\result4.jpg") self.image2.setPixmap(pixmap) self.image2.setScaledContents(True) self.classes.clear()
def main(): parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) parser.add_argument('--read_image_path', default='/run/images/', type=str) parser.add_argument('--result_path', default='/run/result_path/', type=str) args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu yolo = YOLONet(False) weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) detector = Detector(yolo, weight_file) detector.detector_run(args.read_image_path, args.result_path)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--gpu', default=None, type=int) args = parser.parse_args() cfg.WEIGHTS_FILE = os.path.join(cfg.WEIGHTS_DIR, args.weights) if args.gpu is not None: cfg.GPU = str(args.gpu) yolo = YOLONet('train') pascal = pascal_voc('train') solver = Solver(yolo, pascal) solver.train()
def __init__(self): self.net = YOLONet(False) self.weights_file = cfg.WERIGHTS_READ self.cell_size = cfg.CELL_SIZE self.boxes_per_cell = cfg.BOXES_PER_CELL self.ap_iou_threshold = cfg.TEST_IOU_THRESHOLD self.nms_iou_threshold = cfg.NMS_IOU_THERSHOLD self.boundary2 = self.cell_size * self.cell_size * self.boxes_per_cell self.batch_size = cfg.TEST_batch_size self.total_obj = 0 self.recall = None self.precise = None self.pro_num = 0 """
def main(): parser = argparse.ArgumentParser() parser.add_argument('--weights',default = 'YOLO_small.ckpt',type = str) parser.add_argument('--weight_dir',default = 'weights',type = str) parser.add_argument('--data_dir',default = 'data',type = str) parser.add_argument('--gpu',default = '',type = str) args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu yolo = YOLONet(False) weight_file = os.path.join(args.data_dir,args.weight_dir,args.weights)#权重路径 detector = Detector(yolo,weight_file) imname = 'test/person.jpg' detector.image_detector(imname)
def main(): os.environ['CUDA_VISIBLE_DEVICES'] = '' #cfg.GPU yolo = YOLONet(False) weight_file = cfg.WERIGHTS_READ #os.path.join(cfg.WEIGHT_READ,'save.ckpt-{}'.format(cfg.LAST_STEP)) detector = Detector(yolo, weight_file) # detect from camera # cap = cv2.VideoCapture(-1) # detector.camera_detector(cap) # detect from image file imname = cfg.IMAGE_dir_file detector.image_detector(imname)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu yolo = YOLONet(False) weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) detector = Detector(yolo, weight_file) # detect from camera cap = cv2.VideoCapture(0) detector.camera_detector(cap)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--weights', default="YOLO_small.ckpt", type=str) parser.add_argument('--weight_dir', default='weights', type=str) parser.add_argument('--data_dir', default="data", type=str) parser.add_argument('--gpu', default='', type=str) args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu yolo = YOLONet(False) weight_file = os.path.join(args.data_dir, args.weight_dir, args.weights) detector = Detector(yolo, weight_file) # detect from image file image_file = 'test/cat.jpg' detector.detect(image_file)
def main(args): if args.gpu is not None: cfg.GPU = args.gpu if args.data_dir != cfg.DATA_PATH: update_config_paths(args.data_dir, args.weights) os.environ['CUDA_VISIBLE_DEVICES'] = cfg.GPU yolo = YOLONet() pascal = pascal_voc('train') solver = Solver(yolo, pascal) print('Start training ...') solver.train() print('Done training.')