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| import pandas as pd | |
| from PIL import Image | |
| import streamlit as st | |
| from streamlit_drawable_canvas import st_canvas | |
| from streamlit_image_select import image_select | |
| from streamlit_sortables import sort_items | |
| def expand2square(imgpath, background_color=(0, 0, 0)): | |
| pil_img = Image.open(imgpath) | |
| width, height = pil_img.size | |
| if width == height: | |
| return pil_img | |
| elif width > height: | |
| result = Image.new(pil_img.mode, (width, width), background_color) | |
| result.paste(pil_img, (0, (width - height) // 2)) | |
| return result.resize((700, 700)) | |
| else: | |
| result = Image.new(pil_img.mode, (height, height), background_color) | |
| result.paste(pil_img, ((height - width) // 2, 0)) | |
| return result.resize((700, 700)) | |
| def loading_data(files): | |
| imgs = [] | |
| imgs_names = [] | |
| for file in files: | |
| imgs.append(expand2square(file)) | |
| imgs_names.append(file.name) | |
| return imgs, imgs_names | |
| if 'uploaded' not in st.session_state: | |
| st.session_state['uploaded'] = False | |
| images = st.sidebar.file_uploader("Upload here the images (max 4 imgs for demo version):", | |
| type=["png", "jpg"], accept_multiple_files=True) | |
| if len(images) > 0: | |
| st.session_state['uploaded'] = True | |
| imgs_path = [] | |
| imgs = [] | |
| else: | |
| st.session_state['uploaded'] = False | |
| if st.session_state['uploaded'] is True: | |
| # Loading uploaded images and cache the data | |
| imgs, imgs_path = loading_data(images) | |
| # Specify canvas parameters in application | |
| drawing_mode = st.sidebar.selectbox( | |
| "Drawing tool:", ("point", "line", "rect", "circle", "transform") | |
| ) | |
| stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 3) | |
| if drawing_mode == 'point': | |
| point_display_radius = st.sidebar.slider("Point display radius: ", 1, 25, 3) | |
| stroke_color = st.sidebar.color_picker("Stroke color hex: ") | |
| bg_color = st.sidebar.color_picker("Background color hex: ", "#eee") | |
| realtime_update = st.sidebar.checkbox("Update in realtime", False) | |
| master_index = image_select("Uploaded images", imgs, captions=imgs_path, return_value="index") | |
| # Create a canvas component | |
| canvas_result = st_canvas( | |
| fill_color="rgba(255, 165, 0, 0.3)", # Fixed fill color with some opacity | |
| stroke_width=stroke_width, | |
| stroke_color=stroke_color, | |
| background_color=bg_color, | |
| background_image= imgs[master_index], #expand2square(bg_image) if bg_image else expand2square("./IMG_02099.jpg"), | |
| update_streamlit=realtime_update, | |
| height=700, | |
| width=700, | |
| drawing_mode=drawing_mode, | |
| point_display_radius=point_display_radius if drawing_mode == 'point' else 0, | |
| key="canvas", | |
| ) | |
| test = st.sidebar.write("Select the processing order of slave images") | |
| with st.sidebar: | |
| imgs_path2 = imgs_path.copy() | |
| imgs_path2.pop(master_index) | |
| sorted_items = sort_items(imgs_path2, multi_containers=False, direction='vertical') | |
| # if canvas_result.image_data is not None: | |
| # st.image(canvas_result.image_data) | |
| if canvas_result.json_data is not None: | |
| objects = pd.json_normalize(canvas_result.json_data["objects"]) # need to convert obj to str because PyArrow | |
| for col in objects.select_dtypes(include=['object']).columns: | |
| objects[col] = objects[col].astype("str") | |
| st.dataframe(objects) | |