Update app.py

app.py

@@ -1,229 +1,51 @@
-'''
-This is the originall CLL Explorer application that allows users to upload, process, and save images.
-
-The application provides the following functionalities:
-- Upload microscope images.
-- Adjust image view with zoom and enhancement controls.
-- Detect and measure cells automatically.
-- Save analysis results and annotations.
-
-The application is divided into the following sections:
-1. **Upload Images**: Users can upload microscope images in JPG or PNG format.
-2. **Select Image**: Users can select an image from the uploaded files.
-3. **Processed Image**: Displays the processed image with zoom and enhancement controls.
-4. **Image Controls**: Allows users to adjust the image view with sliders for X and Y coordinates, zoom, contrast, brightness, and sharpness.
-5. **Save Options**: Provides options to save the processed image, image description, and image parameters.
-
-To run the application:
-1. Save the script in a Python file (e.g., app.py).
-2. Run the script using the Streamlit command:
-```bash
-streamlit run app.py
-
-'''
-
 import streamlit as st
 from PIL import Image, ImageEnhance
 import pandas as pd
 import numpy as np
 import io
 import os
-import tempfile
-import zipfile
-import cv2
-import numpy as np
 
 def zoom_at(img, x, y, zoom):
-    '''
-    Zoom into an image at a specific location.
-
-    Parameters:
-    ----------
-    img : PIL.Image
-        Input image.
-
-    x : int
-        X-coordinate of the zoom center.
-
-    y : int
-        Y-coordinate of the zoom center.
-
-    zoom : float
-        Zoom factor.
-
-    Returns:
-    -------
-    PIL.Image
-        Zoomed image resized to 500x500 pixels.
-    '''
     w, h = img.size
-    zoom_half = zoom / 2
-    left = max(x - w * zoom_half, 0)
-    upper = max(y - h * zoom_half, 0)
-    right = min(x + w * zoom_half, w)
-    lower = min(y + h * zoom_half, h)
-    img_cropped = img.crop((left, upper, right, lower))
-    return img_cropped.resize((500, 500), Image.LANCZOS)
-
-@st.cache_data
-def apply_enhancements(img, x, y, zoom, contrast, brightness, sharpness):
-    '''
-    Apply zoom and image enhancements to the input image.
-
-    Parameters:
-    ----------
-    img : PIL.Image
-        Input image.
-    x : int
-        X-coordinate of the zoom center.
-    y : int
-        Y-coordinate of the zoom center.
-    zoom : float
-        Zoom factor.
-    contrast : float
-        Contrast adjustment factor.
-    brightness : float
-        Brightness adjustment factor.
-    sharpness : float
-        Sharpness adjustment factor.
-
-    Returns:
-    -------
-    PIL.Image
-        Enhanced image resized to 500x500 pixels.
-    '''
-    zoomed = zoom_at(img, x, y, zoom)
-    enhanced_contrast = ImageEnhance.Contrast(zoomed).enhance(contrast)
-    enhanced_brightness = ImageEnhance.Brightness(enhanced_contrast).enhance(brightness)
-    enhanced_sharpness = ImageEnhance.Sharpness(enhanced_brightness).enhance(sharpness)
-    return enhanced_sharpness
-
-def apply_enhancements_cv(img, x, y, zoom, contrast, brightness, sharpness):
-    """
-    Use OpenCV for zoom and enhancements.
-    """
-    # Convert PIL to OpenCV format
-    img_cv = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
-    h, w = img_cv.shape[:2]
-
-    # Zoom
-    zoom_half = int(zoom / 2)
-    left = max(x - w * zoom_half, 0)
-    top = max(y - h * zoom_half, 0)
-    right = min(x + w * zoom_half, w)
-    bottom = min(y + h * zoom_half, h)
-    cropped = img_cv[int(top):int(bottom), int(left):int(right)]
-    resized = cv2.resize(cropped, (500, 500), interpolation=cv2.INTER_LANCZOS4)
-
-    # Convert back to PIL for other enhancements
-    pil_img = Image.fromarray(cv2.cvtColor(resized, cv2.COLOR_BGR2RGB))
-    enhanced_contrast = ImageEnhance.Contrast(pil_img).enhance(contrast)
-    enhanced_brightness = ImageEnhance.Brightness(enhanced_contrast).enhance(brightness)
-    enhanced_sharpness = ImageEnhance.Sharpness(enhanced_brightness).enhance(sharpness)
-    return enhanced_sharpness
-
-def create_zip(processed_img, description, params):
-    '''
-    Create a zip archive containing the processed image and annotations.
-
-    Parameters:
-    ----------
-    processed_img : PIL.Image
-        The processed image.
-    description : str
-        Description of the image.
-    params : dict
-        Image parameters.
-
-    Returns:
-    -------
-    bytes
-        Byte content of the zip file.
-    '''
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        img_path = os.path.join(tmpdirname, "processed_image.jpg")
-        desc_path = os.path.join(tmpdirname, "description.txt")
-        params_path = os.path.join(tmpdirname, "parameters.json")
+    zoom2 = zoom * 2
+    img = img.crop((x - w / zoom2, y - h / zoom2,
+                    x + w / zoom2, y + h / zoom2))
+    return img.resize((w, h), Image.LANCZOS)
 
-        # Save processed image
-        processed_img.save(img_path)
+st.title("Cell Explorer")
 
-        # Save description
-        with open(desc_path, "w") as f:
-            f.write(description)
-
-        # Save parameters
-        pd.DataFrame([params]).to_json(params_path, orient="records")
-
-        # Create zip
-        zip_buffer = io.BytesIO()
-        with zipfile.ZipFile(zip_buffer, "w") as zipf:
-            zipf.write(img_path, arcname="processed_image.jpg")
-            zipf.write(desc_path, arcname="description.txt")
-            zipf.write(params_path, arcname="parameters.json")
-
-        zip_buffer.seek(0)
-        return zip_buffer
-
-# Streamlit App Configuration
-st.set_page_config(page_title="CLL Explorer", layout="wide")
-st.title("CLL Explorer: Cell Image Analysis Prep Tool")
-
-st.markdown("""
-### About This Application
-This tool assists researchers in analyzing microscope images of any cell type.
-- **Upload** microscope images.
-- **Adjust** image view with zoom and enhancement controls.
-- **Detect** and measure cells automatically.
-- **Save** analysis results and annotations.
-""")
-
-uploaded_files = st.file_uploader("Upload Images", accept_multiple_files=True, type=["jpg", "png"])
+uploaded_files = st.file_uploader("Upload Images", accept_multiple_files=True, type="jpg")
 
 if uploaded_files:
-    img_index = st.selectbox(
-        "Select Image",
-        range(len(uploaded_files)),
-        format_func=lambda x: uploaded_files[x].name
-    )
-    img_data = uploaded_files[img_index].read()
-    img = Image.open(io.BytesIO(img_data)).convert("RGB").resize((500, 500))
-
-    # Create columns with image on the left and controls on the right
-    image_col, controls_col = st.columns([3, 1])
-
-    with image_col:
-        st.subheader("Processed Image")
-        if 'processed_img' in st.session_state:
-            st.image(st.session_state.processed_img, use_container_width=True, caption="Processed Image")
-        else:
-            st.image(img, use_container_width=True, caption="Processed Image")
+    img_index = st.selectbox("Select Image", range(len(uploaded_files)))
+    x = st.slider("X Coordinate", 0, 500, 205)
+    y = st.slider("Y Coordinate", 0, 500, 250)
+    zoom = st.slider("Zoom", 1, 10, 5)
+    contrast = st.slider("Contrast", 0.0, 5.0, 1.0)
+    brightness = st.slider("Brightness", 0.0, 5.0, 1.0)
+    sharpness = st.slider("Sharpness", 0.0, 2.0, 1.0)
+    save_image = st.checkbox("Save Image")
 
-    with controls_col:
-        st.subheader("Image Controls")
-        x = st.slider("X Coordinate", 0, 500, 250)
-        y = st.slider("Y Coordinate", 0, 500, 250)
-        zoom = st.slider("Zoom", 1.0, 10.0, 5.0, step=0.1)
-
-        with st.expander("Enhancement Settings", expanded=True):
-            contrast = st.slider("Contrast", 0.0, 5.0, 1.0, step=0.1)
-            brightness = st.slider("Brightness", 0.0, 5.0, 1.0, step=0.1)
-            sharpness = st.slider("Sharpness", 0.0, 2.0, 1.0, step=0.1)
+    img_data = uploaded_files[img_index].read()
+    img = Image.open(io.BytesIO(img_data)).resize((500, 500))
+    img_zoomed = zoom_at(img, x, y, zoom)
+    img_contrast = ImageEnhance.Contrast(img_zoomed).enhance(contrast)
+    img_bright = ImageEnhance.Brightness(img_contrast).enhance(brightness)
+    img_sharp = ImageEnhance.Sharpness(img_bright).enhance(sharpness)
 
-        if st.button("Apply Adjustments"):
-            processed_img = apply_enhancements(img, x, y, zoom, contrast, brightness, sharpness)
-            st.session_state.processed_img = processed_img
+    if save_image:
+        img_sharp.save("image-processed.jpg")
+        st.success("Image saved as image-processed.jpg")
 
-    # Display Original Image Below
-    st.subheader("Original Image")
-    st.image(img, use_container_width=True, caption="Original Image")
+    st.image(img_sharp, caption="Processed Image", use_column_width=True)
 
-    # Save and Export Options
-    st.markdown("---")
-    st.subheader("Save and Export Options")
+    description = st.text_area("Describe the image", "")
+    if st.button("Save Description"):
+        with open("saved_image_description.txt", "w") as f:
+            f.write(description)
+        st.success("Description saved as saved_image_description.txt")
 
-    with st.expander("Add Annotations", expanded=True):
-        description = st.text_area("Describe the image", "")
+    if st.button("Save Image Parameters"):
         params = {
             "coordinates_x": x,
             "coordinates_y": y,
@@ -232,47 +54,13 @@ if uploaded_files:
             "brightness": brightness,
             "sharpness": sharpness
         }
+        with open("saved_image_parameters.json", "w") as f:
+            f.write(pd.DataFrame([params]).to_json(orient="records"))
+        st.success("Image parameters saved as saved_image_parameters.json")
 
-    if st.button("Prepare Download"):
-        if 'processed_img' in st.session_state and description:
-            zip_buffer = create_zip(st.session_state.processed_img, description, params)
-            st.download_button(
-                label="Download Zip",
-                data=zip_buffer,
-                file_name="processed_image_and_annotations.zip",
-                mime="application/zip"
-            )
-            st.success("Zip file is ready for download.")
-        else:
-            st.warning("Ensure that the processed image is available and description is provided.")
-
-    # Optional: Save Processed Image Locally
-    save_image = st.checkbox("Save Processed Image Locally")
-    if save_image:
-        if 'processed_img' in st.session_state:
-            processed_img_path = os.path.join("processed_image_500x500.jpg")
-            st.session_state.processed_img.save(processed_img_path)
-            st.success(f"Image saved as `{processed_img_path}`")
-        else:
-            st.warning("No processed image to save.")
-
-    # Optional: Rename Files
     if st.button("Rename Files"):
-        if 'processed_img' in st.session_state:
-            file_ext = str(np.random.randint(100))
-            new_img_name = f"img_processed_{file_ext}.jpg"
-            processed_img_path = "processed_image_500x500.jpg"
-            if os.path.exists(processed_img_path):
-                os.rename(processed_img_path, new_img_name)
-
-            # Save parameters and description
-            params_path = f"parameters_{file_ext}.json"
-            description_path = f"description_{file_ext}.txt"
-
-            pd.DataFrame([params]).to_json(params_path, orient="records")
-            with open(description_path, "w") as f:
-                f.write(description)
-
-            st.success(f"Files renamed to `{new_img_name}`, `{params_path}`, and `{description_path}`")
-        else:
-            st.warning("No processed image to rename.")
+        file_ext = str(np.random.randint(100))
+        os.rename("image-processed.jpg", f"img_processed{file_ext}.jpg")
+        os.rename("saved_image_parameters.json", f"saved_image_parameters{file_ext}.json")
+        os.rename("saved_image_description.txt", f"saved_image_description{file_ext}.txt")
+        st.success("Files renamed successfully")