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.streamlit/config.toml

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+[browser]
+gatherUsageStats = false

app.py

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+# Import required modules
+import streamlit as st
+from ultralytics import YOLO
+from PIL import Image
+import os
+import json
+import logging
+import tempfile
+import pandas as pd
+import matplotlib.pyplot as plt
+
+st.set_page_config(
+    page_title="Fish Detector",
+    page_icon="🐟",
+    layout="wide"
+)
+sample_images_folder = "./images/sample_images"
+logging.basicConfig(level=logging.INFO)
+
+# Model loading
+model_folder = "./models"
+st.sidebar.title("🐟 Fish or No Fish Detector")
+st.sidebar.markdown("""
+### For more information:
+- Contact: Michael.Akridge@NOAA.gov
+- Visit the [GitHub repository](https://github.com/MichaelAkridge-NOAA/Fish-or-No-Fish-Detector/)
+""")
+# Display model links
+st.sidebar.markdown("### Model Links")
+st.sidebar.markdown("- [YOLO11 Fish Detector - Grayscale](https://huggingface.co/akridge/yolo11-fish-detector-grayscale)")
+st.sidebar.markdown("- [YOLO11 Segment Fish - Grayscale](https://huggingface.co/akridge/yolo11-segment-fish-grayscale)")
+model_name = st.sidebar.selectbox("Select a YOLO model", os.listdir(model_folder))
+model_path = os.path.join(model_folder, model_name)
+if not os.path.exists(model_path):
+    st.error(f"Model file not found at {model_path}. Please check your setup.")
+    st.stop()
+model = YOLO(model_path)
+
+# Sidebar configuration
+st.sidebar.header("Model Parameters")
+confidence = st.sidebar.slider("Detection Confidence Threshold", 0.0, 1.0, 0.35)
+final_confidence = st.sidebar.slider("Final Yes/No Confidence Threshold", 0.0, 1.0, 0.5)
+
+# Title and description
+st.title("🐟 Fish or No Fish Detector")
+st.write("""
+Is there a fish 🐟 or not? Upload one or more images to detect fish. Using a trained [Ultralytics YOLO11 Model](https://github.com/ultralytics/ultralytics) for its object detection capabilities.
+
+""")
+
+# Custom CSS for button and uploader alignment
+st.markdown("""
+    <style>
+    .custom-file-uploader {
+        display: flex;
+        align-items: center;
+        margin-top: -10px; /* Adjust to move button closer */
+        justify-content: flex-start;
+    }
+    .css-1cpxqw2 {
+        flex-grow: 1;  /* Let file uploader take remaining space */
+    }
+    .sample-button {
+        font-size: 14px;
+        padding: 8px;
+        background-color: #007BFF;
+        color: white;
+        border: none;
+        border-radius: 5px;
+        cursor: pointer;
+        margin-left: 10px;
+        height: 38px; /* Ensure button matches uploader height */
+    }
+    .sample-button:hover {
+        background-color: #0056b3;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# Custom CSS for default button styling
+st.markdown("""
+    <style>
+    .stButton>button, .stDownloadButton>button {
+        width: 100%;
+        padding: 10px;
+        border-radius: 5px;
+        font-size: 18px;
+        font-weight: bold;
+        background-color: #007BFF;
+        color: white;
+        border: none;
+        cursor: pointer;
+    }
+    .stButton>button:hover, .stDownloadButton>button:hover {
+        background-color: #0056b3;
+    }
+    </style>
+""", unsafe_allow_html=True)
+# Load sample images function
+def load_sample_images():
+    return [os.path.join(sample_images_folder, img) for img in os.listdir(sample_images_folder) if img.lower().endswith(('png', 'jpg', 'jpeg'))]
+
+# Prediction function
+def run(image_path):
+    results = model.predict(image_path, conf=confidence)
+    boxes = []
+    fish_count = 0
+    confidences = []
+
+    for box in results[0].boxes:
+        x1, y1, x2, y2 = box.xyxy[0].tolist()
+        conf = box.conf[0].item()
+        class_id = int(box.cls[0].item())
+        class_label = model.names[class_id].lower()  # Normalize to lowercase
+
+        if class_label == "fish" and conf > confidence:
+            fish_count += 1
+        confidences.append(conf)
+
+        boxes.append({"x1": x1, "y1": y1, "x2": x2, "y2": y2, "confidence": conf, "class_id": class_id, "class_label": class_label})
+
+    return results[0].plot()[:, :, ::-1], {"fish_count": fish_count, "confidences": confidences}
+
+# Process images function with directory creation
+# Reusable function to handle multiple image uploads and display results
+def process_images(uploaded_files):
+    all_detections = []
+    result_images = []
+    summary_data = []
+    confidences = []
+    temp_dir = tempfile.gettempdir()
+
+    for uploaded_file in uploaded_files:
+        if isinstance(uploaded_file, str):  # Check if it's a sample image path
+            image_path = uploaded_file
+            image = Image.open(image_path)
+        else:
+            image = Image.open(uploaded_file)
+            image_path = os.path.join(temp_dir, f"{uploaded_file.name}")
+            image.save(image_path)
+
+        st.write(f"Detecting in {os.path.basename(image_path)}...")
+        with st.spinner('Running detection...'):
+            result_image, detection_metadata = run(image_path)
+
+        if result_image is not None:
+            result_images.append((result_image, os.path.basename(image_path)))
+            all_detections.append(detection_metadata)
+
+            summary_data.append({
+                "image_name": os.path.basename(image_path),
+                "fish_detected": detection_metadata["fish_count"] > 0,
+                "fish_count": detection_metadata["fish_count"]
+            })
+
+            confidences.extend(detection_metadata["confidences"])
+
+            # Display fish status
+            fish_detected = detection_metadata['fish_count'] > 0
+            fish_status = f"<b><span style='color: green; font-size: 24px;'>YES</span></b> 🐟" if fish_detected else f"<b><span style='color: red; font-size: 24px;'>NO</span></b>"
+
+            st.markdown(f"**Summary for {os.path.basename(image_path)}:** Fish detected: {fish_status}", unsafe_allow_html=True)
+
+            # Display images side by side
+            col1, col2 = st.columns(2)
+            with col1:
+                st.image(image, caption=f"Uploaded Image - {os.path.basename(image_path)}", use_column_width=True)
+            with col2:
+                st.image(result_image, caption=f"Detection Results - {os.path.basename(image_path)}", use_column_width=True)
+
+            st.success(f"Detection completed for {os.path.basename(image_path)} successfully! 🐟")
+
+        else:
+            st.warning(f"No marine ecosystems detected in {os.path.basename(image_path)}.")
+
+    st.session_state["all_detections"] = all_detections
+    return summary_data, confidences
+
+
+# Function to display a summary table and scatter plot side by side with image labels
+def display_summary(summary_data, confidences):
+    if summary_data:
+        df = pd.DataFrame(summary_data)
+
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.subheader("Summary of Detections")
+            st.table(df[["image_name", "fish_count"]])
+
+        with col2:
+            st.subheader("Fish Detection Confidence Levels")
+            fig, ax = plt.subplots()
+            confidence_index = 0
+
+            for i, row in df.iterrows():
+                num_confidences_for_image = len([c for c in confidences[confidence_index:confidence_index + row["fish_count"]]])
+
+                for j in range(num_confidences_for_image):
+                    if confidence_index < len(confidences):
+                        ax.scatter(confidence_index, confidences[confidence_index], c='blue')
+                        ax.text(confidence_index, confidences[confidence_index], row['image_name'], 
+                                fontsize=10, ha='center', va='bottom', rotation=0)
+                        confidence_index += 1
+
+            ax.axhline(final_confidence, color='red', linestyle='--', label=f'Final Threshold ({final_confidence})')
+            ax.set_xlabel('Detections')
+            ax.set_ylabel('Confidence Level')
+            ax.legend(loc='lower left')
+            st.pyplot(fig)
+
+        if st.session_state.get("all_detections"):
+            json_data = json.dumps(st.session_state["all_detections"], indent=4)
+            st.download_button(
+                label="Download Results as JSON & Reset",
+                data=json_data,
+                file_name="all_detections.json",
+                mime="application/json",
+                key="download_json_bottom"
+            )
+
+# Image uploader with multiple file support
+st.markdown('<div class="custom-file-uploader">', unsafe_allow_html=True)
+uploaded_files = st.file_uploader("Choose image(s)...", type=["png", "jpg", "jpeg"], accept_multiple_files=True)
+
+# Check if files are uploaded, hide the "Auto Run with Sample Images" button if they are
+if not uploaded_files and not st.session_state.get('use_sample_images', False):
+    use_sample_images = st.button("Or Auto Run Using Sample Images", key="sample_button")
+else:
+    use_sample_images = None
+st.markdown('</div>', unsafe_allow_html=True)
+
+# Add the functionality for the "Try it with Sample Images" button
+if use_sample_images:
+    sample_images = load_sample_images()
+    st.session_state['use_sample_images'] = True
+    for sample_image in sample_images:
+        st.session_state.setdefault('uploaded_files', []).append(sample_image)
+    st.session_state['run_automatically'] = True
+
+# Display the Run, Clear, and Download buttons with enhanced styling
+if uploaded_files or st.session_state.get('uploaded_files'):
+    col1, col2, col3 = st.columns([1, 1, 1], gap="small")
+
+    if not st.session_state.get('use_sample_images', False):
+        with col1:
+            run_button = st.button("Click to Run", key="run_button")
+    else:
+        run_button = None
+
+    # Initialize clear_button to None to avoid NameError
+    clear_button = None
+
+    # Conditionally hide the "Clear Results" button while processing
+    with col2:
+        if not st.session_state.get('processing', False):
+            clear_button = st.button("Clear Results", key="clear_button")
+
+    # Run automatically if triggered by the sample images button or the run button
+    if run_button or st.session_state.get('run_automatically'):
+        st.session_state['processing'] = True  # Set the processing flag
+        summary_data, confidences = process_images(uploaded_files or st.session_state['uploaded_files'])
+        display_summary(summary_data, confidences)
+        st.session_state['processing'] = False  # Reset the processing flag after processing is done
+        st.session_state['run_automatically'] = False
+        st.session_state['use_sample_images'] = False
+
+    # Now this check will work, even if clear_button is not defined earlier
+    if clear_button:
+        st.session_state.clear()
+
+    if st.session_state.get("all_detections"):
+        with col3:
+            json_data = json.dumps(st.session_state["all_detections"], indent=4)
+            st.download_button(
+                label="Download Results as JSON & Reset",
+                data=json_data,
+                file_name="all_detections.json",
+                mime="application/json",
+                key="download_json"
+            )

models/yolo11n_fish_detector.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0b754914ce3999cc52ed228d22102f7e91a32c8c1d7fcc9ecbef040e1702a5b8
+size 5536958

models/yolo11n_fish_segment.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d30ab4878b7f526bba0c13ffd4ec718ab3dfeee7b2f8cc919e70d1962b8628dd
+size 6105809

requirements.txt

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+streamlit
+ultralytics
+Pillow
+pandas
+numpy
+matplotlib