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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"
)
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