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| import torch | |
| import numpy as np | |
| import cv2 | |
| import tempfile, base64 | |
| def readb64(uri): | |
| encoded_data = uri.split(',')[-1] | |
| nparr = np.frombuffer(base64.b64decode(encoded_data), np.uint8) | |
| img = cv2.imdecode(nparr, cv2.IMREAD_COLOR) | |
| return img | |
| def img2base64(img, extension="jpg"): | |
| _, img_encoded = cv2.imencode(f".{extension}", img) | |
| img_base64 = base64.b64encode(img_encoded) | |
| img_base64 = img_base64.decode('utf-8') | |
| return img_base64 | |
| def binary2video(video_binary): | |
| # byte_arr = BytesIO() | |
| # byte_arr.write(video_binary) | |
| temp_ = tempfile.NamedTemporaryFile(suffix='.mp4') | |
| # decoded_string = base64.b64decode(video_binary) | |
| temp_.write(video_binary) | |
| video_capture = cv2.VideoCapture(temp_.name) | |
| ret, frame = video_capture.read() | |
| return video_capture | |
| def extract_frames(data_path, interval=30, max_frames=50): | |
| """Method to extract frames""" | |
| cap = cv2.VideoCapture(data_path) | |
| frame_num = 0 | |
| frames = list() | |
| while cap.isOpened(): | |
| success, image = cap.read() | |
| if not success: | |
| break | |
| # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| # image = torch.tensor(image) - torch.tensor([104, 117, 123]) | |
| if frame_num % interval == 0: | |
| frames.append(image) | |
| frame_num += 1 | |
| if len(frames) > max_frames: | |
| break | |
| cap.release() | |
| # if len(frames) > max_frames: | |
| # samples = np.random.choice( | |
| # np.arange(0, len(frames)), size=max_frames, replace=False) | |
| # return [frames[_] for _ in samples] | |
| return frames | |
| """FilePicker for streamlit. | |
| Still doesn't seem to be a good solution for a way to select files to process from the server Streamlit is running on. | |
| Here's a pretty functional solution. | |
| Usage: | |
| ``` | |
| import streamlit as st | |
| from filepicker import st_file_selector | |
| tif_file = st_file_selector(st, key = 'tif', label = 'Choose tif file') | |
| ``` | |
| """ | |
| import os | |
| import streamlit as st | |
| def update_dir(key): | |
| choice = st.session_state[key] | |
| if os.path.isdir(os.path.join(st.session_state[key+'curr_dir'], choice)): | |
| st.session_state[key+'curr_dir'] = os.path.normpath(os.path.join(st.session_state[key+'curr_dir'], choice)) | |
| files = sorted(os.listdir(st.session_state[key+'curr_dir'])) | |
| if "images" in files: | |
| files.remove("images") | |
| st.session_state[key+'files'] = files | |
| def st_file_selector(st_placeholder, path='.', label='Select a file/folder', key = 'selected'): | |
| if key+'curr_dir' not in st.session_state: | |
| base_path = '.' if path is None or path == '' else path | |
| base_path = base_path if os.path.isdir(base_path) else os.path.dirname(base_path) | |
| base_path = '.' if base_path is None or base_path == '' else base_path | |
| files = sorted(os.listdir(base_path)) | |
| files.insert(0, 'Choose a file...') | |
| if "images" in files: | |
| files.remove("images") | |
| st.session_state[key+'files'] = files | |
| st.session_state[key+'curr_dir'] = base_path | |
| else: | |
| base_path = st.session_state[key+'curr_dir'] | |
| selected_file = st_placeholder.selectbox(label=label, | |
| options=st.session_state[key+'files'], | |
| key=key, | |
| on_change = lambda: update_dir(key)) | |
| if selected_file == "Choose a file...": | |
| return None | |
| return selected_file |