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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")
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)
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