(img)
| 31 | |
| 32 | |
| 33 | def preprocess(img): |
| 34 | img = img.resize((224, 224)) |
| 35 | img = img.crop((0, 0, 224, 224)) |
| 36 | img = np.array(img).astype(np.float32) / 255. |
| 37 | img = np.rollaxis(img, 2, 0) |
| 38 | for channel, mean, std in zip(range(3), [0.485, 0.456, 0.406], |
| 39 | [0.229, 0.224, 0.225]): |
| 40 | img[channel, :, :] -= mean |
| 41 | img[channel, :, :] /= std |
| 42 | img = np.expand_dims(img, axis=0) |
| 43 | return img |
| 44 | |
| 45 | |
| 46 | def get_image_label(): |