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import streamlit as st
import cv2
import numpy as np
from PIL import Image
from ultralytics import YOLO
import os
import time
import matplotlib.pyplot as plt
from streamlit_webrtc import webrtc_streamer, WebRtcMode
import functools
import threading
# --- Page Configuration --- MUST BE THE VERY FIRST STREAMLIT CALL
st.set_page_config(page_title="YOLOv11 Object Detection App", page_icon="🧊", layout="wide")
# --- Load YOLO Model ---
@st.cache_resource
def load_model():
def load_in_thread():
try:
model = YOLO("yolov8n.pt") # Or your custom model path
return model
except Exception as e:
st.error(f"Error loading YOLO model: {e}")
return None
model_thread = threading.Thread(target=load_in_thread)
model_thread.start()
model_thread.join()
return load_in_thread()
model = load_model()
# --- Functions ---
def detect_objects(image, confidence_threshold=0.5, iou_threshold=0.45):
if model is None:
st.warning("YOLOv11 model not loaded.")
return []
results = model(image, conf=confidence_threshold, iou=iou_threshold)
detections = []
for result in results:
if result.boxes is not None and len(result.boxes) > 0:
for box in result.boxes:
confidence = float(box.conf[0])
class_id = int(box.cls[0])
class_name = model.names[class_id]
xyxy = box.xyxy[0].tolist()
x1, y1, x2, y2 = map(int, xyxy)
detections.append(((x1, y1, x2, y2), class_name, confidence))
return detections
def draw_boxes(image, detections):
for (x1, y1, x2, y2), class_name, confidence in detections:
label = f"{class_name}: {confidence:.2f}"
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
return image
def process_video(video_file, confidence_threshold, iou_threshold):
try:
video_bytes = video_file.read()
video_path = "temp_video.mp4"
with open(video_path, "wb") as temp_file:
temp_file.write(video_bytes)
cap = cv2.VideoCapture(video_path)
frame_placeholder = st.empty()
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
detections = detect_objects(frame, confidence_threshold, iou_threshold)
frame_with_boxes = draw_boxes(frame.copy(), detections)
frame_with_boxes_rgb = cv2.cvtColor(frame_with_boxes, cv2.COLOR_BGR2RGB)
img = Image.fromarray(frame_with_boxes_rgb)
frame_placeholder.image(img, caption="Processed Video", use_column_width=True)
cap.release()
os.remove(video_path)
except Exception as e:
st.error(f"Error processing video: {e}")
def process_frame(frame, confidence_threshold, iou_threshold):
img = frame.to_ndarray(format="bgr24")
detections = detect_objects(img, confidence_threshold, iou_threshold)
img_with_boxes = draw_boxes(img.copy(), detections)
return img_with_boxes
def simulate_fine_tuning(epochs=10):
st.write("Simulating Fine-tuning...") # Placeholder message
time.sleep(2) # Simulate some processing time
st.success(f"Fine-tuning completed (simulated for {epochs} epochs).")
# --- Main App ---
def main():
st.title("YOLOv11 Object Detection App")
with st.sidebar:
st.header("Settings")
app_mode = st.selectbox("Choose the App mode", ["About", "Run on Image", "Run on Video", "Live Camera Feed", "Fine-Tune (Simulated)"])
confidence_threshold = st.slider("Confidence Threshold", 0.0, 1.0, 0.5, 0.01, help="Min confidence for detected objects.")
iou_threshold = st.slider("IoU Threshold", 0.0, 1.0, 0.45, 0.01, help="IoU threshold for NMS.")
if app_mode == "Fine-Tune (Simulated)":
num_epochs = st.slider("Number of Epochs", 1, 20, 5, 1)
if app_mode == "About":
st.subheader("About the App")
st.write("This app uses YOLOv8n for object detection.") # Updated model name
st.write("Upload an image or video, or use your camera.")
elif app_mode == "Run on Image":
st.subheader("Object Detection on Image")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image.", use_column_width=True)
image_cv = np.array(image)
image_cv = cv2.cvtColor(image_cv, cv2.COLOR_RGB2BGR)
if st.button("Detect Objects"):
detections = detect_objects(image_cv, confidence_threshold, iou_threshold)
image_with_boxes = draw_boxes(image_cv.copy(), detections)
image_with_boxes_rgb = cv2.cvtColor(image_with_boxes, cv2.COLOR_BGR2RGB)
st.image(image_with_boxes_rgb, caption="Detected Objects", use_column_width=True)
elif app_mode == "Run on Video":
st.subheader("Object Detection on Video")
video_file = st.file_uploader("Choose a video...", type=["mp4", "avi"])
if video_file is not None:
process_video(video_file, confidence_threshold, iou_threshold)
elif app_mode == "Live Camera Feed":
st.subheader("Live Camera Feed")
custom_process_frame = functools.partial(process_frame, confidence_threshold=confidence_threshold, iou_threshold=iou_threshold)
webrtc_streamer(key="live-feed", video_frame_callback=custom_process_frame, mode=WebRtcMode.SENDRECV, media_stream_constraints={"video": True, "audio": False})
elif app_mode == "Fine-Tune (Simulated)":
simulate_fine_tuning(epochs=num_epochs)
if __name__ == "__main__":
main() |