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Configuration error
Configuration error
| import os | |
| import shutil | |
| import zipfile | |
| from os.path import join, isfile, basename | |
| import cv2 | |
| import numpy as np | |
| import gradio as gr | |
| import torch | |
| from resnet50 import resnet18 | |
| from sampling_util import furthest_neighbours | |
| from video_reader import video_reader | |
| model = resnet18( | |
| output_dim=0, | |
| nmb_prototypes=0, | |
| eval_mode=True, | |
| hidden_mlp=0, | |
| normalize=False) | |
| model.load_state_dict(torch.load("model.pt")) | |
| model.eval() | |
| avg_pool = torch.nn.AdaptiveAvgPool2d((1, 1)) | |
| def predict(input_file, downsample_size): | |
| downsample_size = int(downsample_size) | |
| base_directory = os.getcwd() | |
| selected_directory = os.path.join(base_directory, "selected_images") | |
| if os.path.isdir(selected_directory): | |
| shutil.rmtree(selected_directory) | |
| os.mkdir(selected_directory) | |
| file_name = (input_file.split('/')[-1]).split('.')[-1] | |
| zip_path = os.path.join(selected_directory, file_name + ".zip") | |
| mean = np.asarray([0.3156024, 0.33569682, 0.34337464], dtype=np.float32) | |
| std = np.asarray([0.16568947, 0.17827448, 0.18925823], dtype=np.float32) | |
| # img_vecs = [] | |
| # with torch.no_grad(): | |
| # for fp_i, file_path in enumerate([input_file]): | |
| # for i, in_img in enumerate(video_reader(file_path, | |
| # targetFPS=9, | |
| # targetWidth=100, | |
| # to_rgb=True)): | |
| # in_img = (in_img.astype(np.float32) / 255.) | |
| # in_img = (in_img - mean) / std | |
| # in_img = np.expand_dims(in_img, 0) | |
| # in_img = np.transpose(in_img, (0, 3, 1, 2)) | |
| # in_img = torch.from_numpy(in_img).float() | |
| # encoded = avg_pool(model(in_img))[0, :, 0, 0].cpu().numpy() | |
| # img_vecs += [encoded] | |
| # img_vecs = np.asarray(img_vecs) | |
| # print("images encoded") | |
| # rv_indices, _ = furthest_neighbours( | |
| # x=img_vecs, | |
| # downsample_size=downsample_size, | |
| # seed=0) | |
| # indices = np.zeros((img_vecs.shape[0],)) | |
| # indices[np.asarray(rv_indices)] = 1 | |
| # print("images selected") | |
| indices = [0] * 1000 | |
| indices[0] = 1 | |
| global_ctr = 0 | |
| for fp_i, file_path in enumerate([input_file]): | |
| for i, img in enumerate(video_reader(file_path, | |
| targetFPS=9, | |
| targetWidth=None, | |
| to_rgb=False)): | |
| if indices[global_ctr] == 1: | |
| cv2.imwrite(join(selected_directory, str(global_ctr) + ".jpg"), img) | |
| global_ctr += 1 | |
| print("selected images extracted") | |
| all_selected_imgs_path = [join(selected_directory, f) for f in os.listdir(selected_directory) if | |
| isfile(join(selected_directory, f))] | |
| zipf = zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) | |
| for i, f in enumerate(all_selected_imgs_path): | |
| zipf.write(f, basename(f)) | |
| zipf.close() | |
| print("selected images zipped") | |
| return zip_path | |
| demo = gr.Interface( | |
| enable_queue=True, | |
| title="Frame selection by visual difference", | |
| fn=predict, | |
| inputs=[gr.inputs.Video(label="Upload Video File"), | |
| gr.inputs.Number(label="Downsample size")], | |
| outputs=gr.outputs.File(label="Zip"), | |
| ) | |
| demo.launch(debug=True) | |