initial working app

.gitignore

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+# Created by .ignore support plugin (hsz.mobi)
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# IPython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
+### VirtualEnv template
+# Virtualenv
+# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
+[Bb]in
+[Ii]nclude
+[Ll]ib
+[Ll]ib64
+[Ll]ocal
+[Ss]cripts
+pyvenv.cfg
+.venv
+pip-selfcheck.json
+
+### JetBrains template
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+# idea folder, uncomment if you don't need it
+.idea
+
+venv
+models
+media-directory/**

app.py

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+import os
+import cv2
+from PIL import Image
+import pandas as pd
+import streamlit as st
+from ultralytics import YOLO
+from streamlit_image_comparison import image_comparison
+from ultralytics.utils.ops import scale_image
+import numpy as np
+
+model = YOLO('models/last.pt')
+
+st.title("Image Segmentation with YOLOv8: A Web Integration")
+st.subheader("Implementing for Image Segmentation and Object Detection")
+st.write("Image segmentation is a critical task in computer vision that involves dividing an image into multiple "
+         "segments or regions. YOLOv8 is a state-of-the-art deep learning model that can be used for "
+         "image segmentation and object detection. In this web app, we will implement an interesting example "
+         "using YOLOv8 for image segmentation. We can simply drop an image, View the identified segment "
+         "a piece, a whole and the distribution of the identified segments. The essence of this application "
+         "is to build a practical understanding and implementation of the powerful and light YOLOv8 "
+         "for image segmentation and object detection")
+
+url = "https://github.com/AkanimohOD19A/img-segmentation"
+link = f'<a href="{url}">This sample app was heavily based on code shared by Akan Daniel. Huge credits to him.</a>'
+st.markdown(link, unsafe_allow_html=True)
+
+def save_uploadedfile(uploadedfile):
+    with open(os.path.join("./media-directory/", "captured_picture.jpg"), "wb") as f:
+        f.write(uploadedfile.getbuffer())
+
+def convert_to_jpg(uploaded_image):
+    im = Image.open(uploaded_image)
+    if im.mode in ("RGBA", "P"):
+        im = im.convert("RGB")
+    uploaded_image_path = os.path.join(parent_media_path, "uploaded_image.jpg")
+    im.save(uploaded_image_path)
+
+
+def overlay(image, mask, color, alpha, resize=None):
+    """Combines image and its segmentation mask into a single image.
+
+    Params:
+        image: Training image. np.ndarray,
+        mask: Segmentation mask. np.ndarray,
+        color: Color for segmentation mask rendering.  tuple[int, int, int] = (255, 0, 0)
+        alpha: Segmentation mask's transparency. float = 0.5,
+        resize: If provided, both image and its mask are resized before blending them together.
+        tuple[int, int] = (1024, 1024))
+
+    Returns:
+        image_combined: The combined image. np.ndarray
+
+    """
+    # color = color[::-1]
+    colored_mask = np.expand_dims(mask, 0).repeat(3, axis=0)
+    colored_mask = np.moveaxis(colored_mask, 0, -1)
+    masked = np.ma.MaskedArray(image, mask=colored_mask, fill_value=color)
+    image_overlay = masked.filled()
+
+    if resize is not None:
+        image = cv2.resize(image.transpose(1, 2, 0), resize)
+        image_overlay = cv2.resize(image_overlay.transpose(1, 2, 0), resize)
+
+    image_combined = cv2.addWeighted(image, 1 - alpha, image_overlay, alpha, 0)
+
+    return image_combined
+
+st.divider()
+
+st.markdown('')
+st.markdown('##### Segmented Pieces')
+
+## Placeholder Image
+parent_media_path = "media-directory"
+img_file = './media-directory/example_input.png'
+
+## Application States
+APPLICATION_MODE = st.sidebar.selectbox("Our Options",
+                                        ["Take Picture", "Upload Picture"]
+                                        )
+
+## captured_picture Image
+if APPLICATION_MODE == "Take Picture":
+    st.sidebar.write(
+        """
+            A computer aided application that segments your input image, built on 
+            the powerful YOLOv8 object detection algorithm developed by *ultralytics*.
+
+            Simply take a captured_picture and it gets segmentated in real time.
+        """
+    )
+    picture = st.camera_input("Take a picture")
+    st.markdown('')
+    if picture:
+        st.sidebar.divider()
+        st.sidebar.image(picture, caption="captured_picture")
+        if st.button("Segment!"):
+            ## Function to save image
+            save_uploadedfile(picture)
+            st.sidebar.success("Saved File")
+            captured_picture_img = os.path.join(parent_media_path, "/captured_picture.jpg")
+        st.write("Click on **Clear photo** to retake picture")
+        img_file = './media-directory/captured_picture.jpg'
+    st.divider()
+
+elif APPLICATION_MODE == "Upload Picture":
+    st.sidebar.write(
+        """
+            A computer aided application that segments your input image, built on 
+            the powerful YOLOv8 object detection algorithm developed by *ultralytics*.
+
+            Simply drop your image and it gets segmentated in real time.
+        """
+    )
+    st.sidebar.divider()
+    # uploaded_file = st.sidebar.file_uploader("Upload your Image here", type=['png', 'jpeg', 'jpg'])
+    uploaded_file = st.sidebar.file_uploader("Drop a JPG/PNG file", accept_multiple_files=False, type=['jpg', 'png'])
+    if uploaded_file is not None and uploaded_file.type != ".jpg":
+        convert_to_jpg(uploaded_file)
+    if uploaded_file is not None:
+        file_details = {"FileName": uploaded_file.name, "FileType": uploaded_file.type}
+        new_file_name = "uploaded_image.jpg"
+        with open(os.path.join(parent_media_path, new_file_name), "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        img_file = os.path.join(parent_media_path, new_file_name)
+        st.sidebar.success("File saved successfully")
+        print(f"File saved successfully to {os.path.abspath(os.path.join(parent_media_path, new_file_name))}")
+    else:
+        st.sidebar.write("You are using a placeholder image, Upload your Image (.jpg for now) to explore")
+
+# def make_segmentation(img_file):
+results = model(img_file)
+img = cv2.imread(img_file)
+names_list = []
+for result in results:
+    boxes = result.boxes.cpu().numpy()
+    # segmentation
+    masks = result.masks.data.cpu().numpy()  # masks, (N, H, W)
+    h, w, _ = img.shape
+
+    numCols = len(masks)
+    if numCols > 0:
+        cols = st.columns(numCols)
+    else:
+        print(f"Number of Boxes found: {numCols}")
+        st.warning("Unable to id Distinct items - Please retry with a clearer Image")
+    for mask in masks:
+        mask = cv2.resize(mask, (w, h))
+        img = overlay(img, mask, color=(0, 255, 0), alpha=0.3)
+#     st.image(rect)
+#     render image-comparison
+
+    st.markdown('')
+    st.markdown('##### Slider of Uploaded Image and Segments')
+    image_comparison(
+        img1=img_file,
+        img2=img,
+        label1="Actual Image",
+        label2="Segmented Image",
+        width=700,
+        starting_position=50,
+        show_labels=True,
+        make_responsive=True,
+        in_memory=True
+    )
+    # for i, box in enumerate(boxes):
+    #     r = box.xyxy[0].astype(int)
+    #     crop = img[r[1]:r[3], r[0]:r[2]]
+    #     predicted_name = result.names[int(box.cls[0])]
+    #     names_list.append(predicted_name)
+    #     with cols[i]:
+    #         st.write(str(predicted_name) + ".jpg")
+    #         st.image(crop)
+
+st.sidebar.divider()
+st.sidebar.markdown('')
+st.sidebar.markdown('#### Distribution of identified items')
+
+# Boolean to resize the dataframe, stored as a session state variable
+st.sidebar.checkbox("Use container width", value=False, key="use_container_width")
+if len(names_list) > 0:
+    df_x = pd.DataFrame(names_list)
+    summary_table = df_x[0].value_counts().rename_axis('unique_values').reset_index(name='counts')
+    st.sidebar.dataframe(summary_table, use_container_width=st.session_state.use_container_width)
+else:
+    st.sidebar.warning("Unable to id Distinct items - Please retry with a clearer Image")
+
+st.markdown('')
+st.markdown('')
+st.markdown('')
+st.markdown('')
+st.markdown('')
+st.markdown('')
+st.sidebar.divider()

requirements.txt

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+opencv-python-headless
+streamlit-image-comparison
+pandas
+ultralytics
+streamlit
+Pillow