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import numpy as np | |
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 | |
from py4matching import template as m | |
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 = [] | |
imgs_dict = {} | |
for file in files: | |
image = expand2square(file) | |
imgs.append(image) | |
imgs_names.append(file.name) | |
imgs_dict[file.name] = image | |
return imgs, imgs_names, imgs_dict | |
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, imgs_dict = 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') | |
TEMPLATE_SIZE = 64 | |
SEARCH_BUFFER = 32 | |
# if canvas_result.image_data is not None: | |
# st.image(canvas_result.image_data) | |
if canvas_result.json_data is not None: | |
df = pd.json_normalize(canvas_result.json_data["objects"]) # need to convert obj to str because PyArrow | |
if 'type' in df.keys(): | |
#st.write(df.keys()) | |
df = df[['type', 'left', 'top']] | |
df[sorted_items] = np.nan | |
for index, row in df.iterrows(): | |
#print(row['type'], row['top']) | |
i = int(row['left']) | |
j = int(row['top']) | |
template = np.array(imgs[master_index])[j:j+TEMPLATE_SIZE, | |
i:i+TEMPLATE_SIZE] | |
# Loop on slave imgs | |
st.image(template) | |
for item in sorted_items: | |
st.write(item) | |
search_area = np.array(imgs_dict[item])[j-SEARCH_BUFFER:j+TEMPLATE_SIZE+SEARCH_BUFFER, | |
i-SEARCH_BUFFER:i+TEMPLATE_SIZE+SEARCH_BUFFER] | |
st.image(search_area) | |
px, py, max_val = m.template_match(template.astype('uint8'), search_area.astype('uint8')) | |
st.write(py) | |
#Covert df to string for printing | |
for col in df.select_dtypes(include=['object']).columns: | |
df[col] = df[col].astype("str") | |
st.dataframe(df) | |