first commit

Dockerfile

@@ -1,20 +1,31 @@
-FROM python:3.13.5-slim
+FROM python:3.11.13
 
 WORKDIR /app
 
+# System deps (add ffmpeg for moviepy)
 RUN apt-get update && apt-get install -y \
     build-essential \
     curl \
     git \
+    ffmpeg \
     && rm -rf /var/lib/apt/lists/*
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# Install PDM and use pyproject/pdm.lock for deps
+RUN pip3 install --no-cache-dir -U pip setuptools wheel pdm
+
+# Copy only dependency files first for better layer caching
+COPY pyproject.toml ./
+COPY pdm.lock ./
 
-RUN pip3 install -r requirements.txt
+# Install project dependencies (prod only)
+RUN pdm install --prod --frozen
+
+# Copy application source
+COPY src/ ./src/
 
 EXPOSE 8501
 
 HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Run via PDM so PEP 582 (__pypackages__) is on sys.path
+ENTRYPOINT ["pdm", "run", "streamlit", "run", "src/app.py", "--server.port=8501", "--server.address=0.0.0.0"]

models/modelYOLOv8n_datasetDIOR_epochs36_batch16_reduced_classes.pt

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models/modelYOLOv8n_datasetDIOR_epochs50_batch16.pt

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models/modelYOLOv8n_datasetDOTAv2_epochs5_batch1.pt

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pdm.lock

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+    {file = "watchdog-6.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:e3df4cbb9a450c6d49318f6d14f4bbc80d763fa587ba46ec86f99f9e6876bb26"},
+    {file = "watchdog-6.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:2cce7cfc2008eb51feb6aab51251fd79b85d9894e98ba847408f662b3395ca3c"},
+    {file = "watchdog-6.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:20ffe5b202af80ab4266dcd3e91aae72bf2da48c0d33bdb15c66658e685e94e2"},
+    {file = "watchdog-6.0.0-py3-none-win32.whl", hash = "sha256:07df1fdd701c5d4c8e55ef6cf55b8f0120fe1aef7ef39a1c6fc6bc2e606d517a"},
+    {file = "watchdog-6.0.0-py3-none-win_amd64.whl", hash = "sha256:cbafb470cf848d93b5d013e2ecb245d4aa1c8fd0504e863ccefa32445359d680"},
+    {file = "watchdog-6.0.0-py3-none-win_ia64.whl", hash = "sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f"},
+    {file = "watchdog-6.0.0.tar.gz", hash = "sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282"},
+]

pyproject.toml

@@ -0,0 +1,140 @@
+[project]
+name = "object_detection_project"
+version = "0.1.0"
+description = "Default template for PDM package"
+authors = [
+    {name = "", email = ""},
+]
+dependencies = [
+    "streamlit>=1.49.1",
+    "moviepy>=2.2.1",
+    "pillow>=10.0.0",
+    "ultralytics>=8.3.0",
+]
+requires-python = "==3.11.*"
+readme = "README.md"
+license = {text = "MIT"}
+
+
+[tool.pdm]
+distribution = false
+
+
+
+[tool.ruff]
+ignore = [
+
+    # (missing public docstrings) These work off of the Python sense of "public", rather than our
+    # bespoke definition based off of `@public`. When ruff supports custom plugins then we can write
+    # appropriate rules to require docstrings for `@public`.
+    "D100",
+    "D101",
+    "D102",
+    "D103",
+    "D104",
+    "D105",
+    "D106",
+    "D107",
+
+    # (docstring imperative mood) Overly restrictive.
+    "D401",
+
+    # (module level import not at top) There are several places where we use e.g.
+    # warnings.filterwarings calls before imports.
+    "E402",
+
+    # (line too long): This fires for comments, which black won't wrap.
+    # Disabling until there is an autoformat solution available for comments.
+    "E501",
+
+    # Pandas sometime need `==` for comparison instead of `is`
+    # Comparision to `None` should be done with `is` rather than `==`.
+    "E712",
+
+    # (no type comparison): There are a few places where we use `== type(None)` which are more clear
+    # than the equivalent `isinstance` check.
+    'E721',
+
+    # (bare exception): There are many places where we want to catch a maximally generic exception.
+    'E722',
+
+    # (no assign lambda): existing code assigns lambdas in a few places. With black formatting
+    # requiring extra empty lines between defs, disallowing lambda assignment can make code less
+    # readable.
+    "E731",
+
+    # (try-except-in-loop) we use this pattern in many places and the performance impact is negligible
+    "PERF203",
+
+    # (no concatenation) Existing codebase has many concatentations, no reason to disallow them.
+    "RUF005",
+
+    # (use ClassVar for attr declarations with defaults) This is a good rule for vanilla Python, but
+    # triggers false positives for many libs that have DSLs that make use of attr defaults.
+    "RUF012",
+
+
+    ##### TEMPORARY DISABLES
+
+    # (assorted docstring rules) There are too many violations of these to enable
+    # right now, but we should enable after fixing the violations.
+    "D200", # (one-line docstring should fit)
+    "D205", # (blank line after summary)
+    "D417", # (missing arg in docstring)
+    # (assorted perf rules) We have a lot of violations, enable when autofix is available
+    "PERF401", # (manual-list-comprehension)
+    "PERF402", # (manual-list-copy)
+]
+# By default, ruff only uses all "E" (pycodestyle) and "F" (pyflakes) rules.
+# Here we append to the defaults.
+select = [
+    # (flake8-builtins) detect shadowing of python builtin symbols by variables and arguments.
+    # Attributes are OK (which is why A003) is not included here.
+    "A001",
+    "A002",
+
+    # (useless expression): Expressions that aren't assigned to anything are typically bugs.
+    "B018",
+
+    # (pydocstyle) Docstring-related rules. A large subset of these are ignored by the
+    # "convention=google" setting, we set under tool.ruff.pydocstyle.
+    "D",
+
+    # (pycodestyle) pycodestyle rules
+    "E",
+
+    # (pyflakes) pyflakes rules
+    "F",
+
+    # (isort) detect improperly sorted imports
+    "I001",
+
+    # (performance) perflint rules
+    "PERF",
+
+    # (pylint) use all pylint rules from categories "Convention", "Error", and "Warning" (ruff
+    # currently implements only a subset of pylint's rules)
+    "PLE",
+    "PLW",
+
+    # (no commented out code) keep commented out code blocks out of the codebase
+    # "ERA001",
+
+    # (ruff-specific) Enable all ruff-specific checks (i.e. not ports of
+    # functionality from an existing linter).
+    "RUF",
+
+    # (private member access) Flag access to `_`-prefixed symbols. By default the various special
+    # methods on `NamedTuple` are ignored (e.g. `_replace`).
+    "SLF001",
+
+    # (flake8-type-checking) Auto-sort imports into TYPE_CHECKING blocks depending on whether
+    # they are runtime or type-only imports.
+    "TCH",
+
+    # (f-strings) use f-strings instead of .format()
+    "UP032",
+
+    # (invalid escape sequence) flag errant backslashes
+    "W605",
+]

requirements.txt

@@ -1,3 +0,0 @@
-altair
-pandas
-streamlit

src/app.py

@@ -0,0 +1,180 @@
+import os
+import tempfile
+import time
+from pathlib import Path
+from shutil import which
+
+import streamlit as st
+from PIL import Image
+
+from model import *
+
+
+def main():
+    model = YOLOModelv2()
+    minimum_confidence_threshold = 0.5
+
+    st.set_page_config(page_title="SatSense Demo")
+    st.title(":satellite: SatSense Demo")
+    st.markdown(
+        """
+        The SatSense demo app simplifies annotating images and videos taken by satellites. 
+        It employs cutting-edge object detection models to automatically analyze and recognize 
+        various objects in satellite imagery, including vehicles and ships.
+
+        #### How to get started
+
+        1. **Upload Satellite Imagery:** Use the sidebar to upload your satellite imagery media 
+        files for analysis.
+        2. **Review Identified Objects:** Explore the annotated objects marked by the model.
+
+        #### Tips for usage
+
+        1. Please clear any existing uploads in the sidebar before uploading a new file.
+        2. For optimal results, please upload clear and high-resolution satellite media files.
+        3. [Location SA Map Viewer](https://location.sa.gov.au/viewer/) provides satellite imagery that can be used as image input.
+
+        SatSense simplifies the process of annotating satellite imagery and allows you to 
+        export the annotated media files. Start annotating and discovering objects of interest 
+        effortlessly!
+
+        ***Note:** In its current MVP stage, the SatSense demo offers a glimpse into the 
+        world of automatic object detection in satellite imagery. Your feedback can help shape 
+        its future improvements!*
+        """
+    )
+
+    # Sidebar to set minimum confidence threshold
+    st.sidebar.header("Parameters")
+    minimum_confidence_threshold = st.sidebar.slider(
+        "Minimum confidence threshold",
+        min_value=0.0,
+        max_value=1.0,
+        step=0.1,
+        value=minimum_confidence_threshold,
+        format="%.1f",
+    )
+    st.sidebar.markdown("---")
+
+    # Sidebar for image detection
+    st.sidebar.header("Image Detection")
+    uploaded_image = st.sidebar.file_uploader(
+        "Upload an image", type=["jpg", "jpeg", "png"]
+    )
+
+    st.sidebar.markdown("---")
+
+    # Sidebar for video detection
+    st.sidebar.header("Video Detection")
+    uploaded_video = st.sidebar.file_uploader(
+        "Upload a video", type=["mp4", "avi", "mov"]
+    )
+
+    if uploaded_image:
+        st.markdown("---")
+        st.write("")
+
+        st.markdown("#### Uploaded image")
+        image = Image.open(uploaded_image)
+        st.image(image, use_column_width=True)
+
+        st.write("")
+        st.write("")
+
+        with st.spinner("Processing..."):
+            annotated_image = model.predict_image(
+                image, min_confidence=minimum_confidence_threshold
+            )
+
+        st.markdown("#### Annotated image")
+        st.image(annotated_image, use_column_width=True)
+
+    if uploaded_video:
+        st.markdown("---")
+        st.write("")
+
+        temp_dir = tempfile.mkdtemp()
+        # Preserve uploaded extension to maximize compatibility with OpenCV/YOLO
+        uploaded_ext = Path(uploaded_video.name).suffix.lower() or ".mp4"
+        temp_video_path = os.path.join(temp_dir, f"temp_video{uploaded_ext}")
+        annotated_dir = "./annotated_video"
+        os.makedirs(annotated_dir, exist_ok=True)
+        annotated_video_path_input_ext = os.path.join(
+            annotated_dir, f"temp_video{uploaded_ext}"
+        )
+        annotated_video_path_mp4 = os.path.join(annotated_dir, "temp_video.mp4")
+
+        st.markdown("#### Uploaded video")
+        uploaded_video_bytes = uploaded_video.getvalue()
+        st.video(uploaded_video_bytes)
+
+        st.write("")
+        st.write("")
+
+        progress_bar = st.progress(0.3, text="Performing object detection...")
+
+        with open(temp_video_path, "wb") as video_file:
+            video_file.write(uploaded_video.getvalue())
+
+        model.predict_video(
+            temp_video_path,
+            min_confidence=minimum_confidence_threshold,
+            target_dir_name="annotated_video",
+        )
+
+        final_video_path = annotated_video_path_input_ext
+
+        # If the annotated output isn't mp4, try converting with ffmpeg if available
+        if uploaded_ext != ".mp4":
+            progress_bar.progress(0.67, text="Converting video format...")
+            if which("ffmpeg"):
+                import subprocess
+
+                try:
+                    subprocess.run(
+                        [
+                            "ffmpeg",
+                            "-y",
+                            "-i",
+                            annotated_video_path_input_ext,
+                            "-c:v",
+                            "libx264",
+                            "-pix_fmt",
+                            "yuv420p",
+                            "-crf",
+                            "23",
+                            "-preset",
+                            "veryfast",
+                            "-an",
+                            annotated_video_path_mp4,
+                        ],
+                        check=True,
+                        stdout=subprocess.DEVNULL,
+                        stderr=subprocess.STDOUT,
+                    )
+                    final_video_path = annotated_video_path_mp4
+                except Exception:
+                    st.warning(
+                        "ffmpeg failed to convert the video. Attempting to display original format."
+                    )
+            else:
+                st.info(
+                    "Install ffmpeg to enable conversion to mp4 (e.g. `brew install ffmpeg` on macOS) or use the provided Dockerfile."
+                )
+
+        progress_bar.progress(1.0, text="Done!")
+        time.sleep(1)
+        progress_bar.empty()
+
+        st.markdown("#### Annotated video")
+        annotated_video_file = open(final_video_path, "rb")
+        annotated_video_bytes = annotated_video_file.read()
+        # Let Streamlit infer format from the file when possible
+        st.video(annotated_video_bytes)
+
+    st.markdown("---")
+    st.markdown("Demo built by [Lucid Insights Pty Ltd](https://lucidinsights.com.au).")
+
+
+if __name__ == "__main__":
+    main()

src/model.py

@@ -0,0 +1,79 @@
+from abc import ABC, abstractmethod
+
+from PIL import Image
+from ultralytics import YOLO
+
+
+class BaseModel(ABC):
+    @abstractmethod
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def predict_image(self, image):
+        pass
+
+    @abstractmethod
+    def predict_video(self, video):
+        pass
+
+
+class YOLOModelv1(BaseModel):
+    """Model: modelYOLOv8n_datasetDOTAv2_epochs5_batch1.pt"""
+
+    def __init__(self):
+        self.model = YOLO(
+            "./models/modelYOLOv8n_datasetDOTAv2_epochs5_batch1.pt", task="detect"
+        )
+
+    def predict_image(self, image, min_confidence):
+        results = self.model.predict(image, save=False, imgsz=640, conf=min_confidence)
+        annotated_image_filename = "annotated_image.png"
+        for result in results:
+            im_array = result.plot()
+            im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+            im.save(annotated_image_filename)
+        return annotated_image_filename
+
+    def predict_video(self, video, min_confidence, target_dir_name="annotated_video"):
+        self.model.predict(
+            video,
+            save=True,
+            project=".",
+            name=target_dir_name,
+            exist_ok=True,
+            imgsz=640,
+            conf=min_confidence,
+        )
+
+
+class YOLOModelv2(BaseModel):
+    """Model: modelYOLOv8n_datasetDIOR_epochs50_batch16.pt"""
+
+    def __init__(self):
+        self.model = YOLO(
+            "./models/modelYOLOv8n_datasetDIOR_epochs50_batch16.pt", task="detect"
+        )
+
+    def predict_image(self, image, min_confidence, classes=None):
+        results = self.model.predict(
+            image, save=False, imgsz=800, conf=min_confidence, classes=classes
+        )
+        annotated_image_filename = "annotated_image.png"
+        for result in results:
+            im_array = result.plot()
+            im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+            im.save(annotated_image_filename)
+        return annotated_image_filename
+
+    def predict_video(self, video, min_confidence, target_dir_name="annotated_video", classes=None):
+        self.model.predict(
+            video,
+            save=True,
+            project=".",
+            name=target_dir_name,
+            exist_ok=True,
+            imgsz=800,
+            conf=min_confidence,
+            classes=classes,
+        )

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))