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Configuration error
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# 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")
# 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))]
zip_path = "asd.zip"
# 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)
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