from yolomodel import yolo_model # create yolo instance  yolo=yolo_model.yolo() # preload weights yolo.load(model='tiny-yolo',threshold=0.22,gpu=0.2) ## now you can choose either load 'tiny-yolo', 'tiny-yolo-voc' or 'yolo' model, and you can also pass in threshold  # run demo if you have a camera yolo.demo('camera') # use key [ESC] to exit demo # predict a image result = yolo.predict('dog.jpg')

import cv2 img_path = 'sample_img' imgs = os.listdir(img_path) cv_imgs = [cv2.imread(os.path.join(img_path,f),cv2.IMREAD_COLOR) for f in imgs] yolo=yolo_model.yolo(); yolo.load(model='tiny-yolo',threshold=0.22,gpu=0.2) ## now you can choose either load 'tiny-yolo' or 'yolo' model, and you can also pass in threshold ##### predict single memory image yolo.predict_imgcv(cv_imgs[0]) ##### predict images in batches inputs = cv_imgs outputs = yolo.predict_imgcv_list(inputs,threshold=0.3) ## now user can pass in threshold, if not, defaults to 0.35 print(outputs)