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import streamlit as st
import cv2
import numpy as np
import base64
import io
from collections import Counter
from PIL import Image
from ultralytics import YOLO
import os
# Set page config
st.set_page_config(page_title="Object Detection App", layout="wide")
# Model paths
MODELS = {
'yolov8s.pt': './model/yolov8s.pt',
'yolov9m.pt': './model/yolov9m.pt'
}
# Load models on demand
@st.cache_resource
def get_model(model_name):
"""Load model if not already loaded"""
if model_name in MODELS and os.path.exists(MODELS[model_name]):
return YOLO(MODELS[model_name])
else:
raise ValueError(f"Model {model_name} not found")
def decode_base64_image(base64_string):
"""Base64 image string ko decode karna"""
# Remove data URL prefix if present
if ',' in base64_string:
base64_string = base64_string.split(',')[1]
image_data = base64.b64decode(base64_string)
image = Image.open(io.BytesIO(image_data))
return np.array(image)
def process_detections(results, model):
"""Process detection results into standard format"""
detections = []
for result in results:
boxes = result.boxes
for box in boxes:
# Bounding box coordinates
x1, y1, x2, y2 = box.xyxy[0]
# Confidence aur class
conf = box.conf[0]
cls = int(box.cls[0])
class_name = model.names[cls]
# Detection object banana
detection = {
'bbox': [float(x1), float(y1), float(x2-x1), float(y2-y1)],
'class': class_name,
'confidence': float(conf)
}
detections.append(detection)
return detections
# App title
st.title("Object Detection App")
# Sidebar for settings
st.sidebar.title("Settings")
# Available models info
available_models = [
{'name': 'yolov8s.pt', 'type': 'Object Detection', 'description': 'YOLOv8s (Fastest)'},
{'name': 'yolov9m.pt', 'type': 'Object Detection', 'description': 'YOLOv9m (Highest Accuracy)'},
]
# Model selection
model_options = {m['name']: f"{m['name']} - {m['description']}" for m in available_models}
model_name = st.sidebar.selectbox("Select Model", options=list(model_options.keys()), format_func=lambda x: model_options[x])
# Confidence threshold
confidence = st.sidebar.slider("Confidence Threshold", min_value=0.1, max_value=1.0, value=0.25, step=0.05)
# Tab selection
tab1, tab2 = st.tabs(["Single Image", "Multiple Images"])
with tab1:
st.header("Single Image Detection")
# Image upload
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Display uploaded image
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
# Process button
if st.button("Detect Objects"):
try:
with st.spinner("Detecting objects..."):
# Load model
model = get_model(model_name)
# Convert to numpy array
image_np = np.array(image)
# Object detection
results = model(image_np, conf=confidence)
# Process results
detections = process_detections(results, model)
# Object grouping
object_counts = Counter(det['class'] for det in detections)
grouped_objects = [
{'class': obj, 'count': count}
for obj, count in object_counts.items()
]
# Display results if any detections found
if detections:
# Draw bounding boxes on image
result_image = image_np.copy()
for det in detections:
x, y, w, h = [int(val) for val in det['bbox']]
cv2.rectangle(result_image, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.putText(result_image, f"{det['class']} {det['confidence']:.2f}",
(x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# Show image with detections
st.image(result_image, caption="Detection Results", use_column_width=True)
# Display summary
st.subheader("Detection Summary")
for obj in grouped_objects:
st.write(f"- {obj['class']}: {obj['count']}")
# Display detection details
st.subheader("Detection Details")
for i, det in enumerate(detections, 1):
st.write(f"#{i}: {det['class']} (Confidence: {det['confidence']:.2f})")
else:
st.info("No objects detected in the image.")
except Exception as e:
st.error(f"Error processing image: {str(e)}")
with tab2:
st.header("Multiple Images Detection")
uploaded_files = st.file_uploader("Upload multiple images", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
if uploaded_files:
st.write(f"{len(uploaded_files)} images uploaded")
# Process button
if st.button("Detect Objects in All Images"):
try:
with st.spinner("Detecting objects in multiple images..."):
# Load model
model = get_model(model_name)
# Process each image
all_detections = []
for i, file in enumerate(uploaded_files):
# Read image
image = Image.open(file)
image_np = np.array(image)
# Object detection
results = model(image_np, conf=confidence)
# Process results
detections = process_detections(results, model)
all_detections.append(detections)
# Create columns for image display
col1, col2 = st.columns(2)
with col1:
st.write(f"Image {i+1}: {file.name}")
st.image(image, caption=f"Original - {file.name}", use_column_width=True)
with col2:
# Draw bounding boxes
result_image = image_np.copy()
for det in detections:
x, y, w, h = [int(val) for val in det['bbox']]
cv2.rectangle(result_image, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.putText(result_image, f"{det['class']} {det['confidence']:.2f}",
(x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
st.image(result_image, caption=f"Detections - {file.name}", use_column_width=True)
# Display detections for this image
object_counts = Counter(det['class'] for det in detections)
st.write("Detected objects:")
for obj, count in object_counts.items():
st.write(f"- {obj}: {count}")
st.divider()
# Overall summary
st.subheader("Overall Detection Summary")
all_objects = []
for detections in all_detections:
all_objects.extend([det['class'] for det in detections])
total_counts = Counter(all_objects)
for obj, count in total_counts.items():
st.write(f"- {obj}: {count} (across all images)")
except Exception as e:
st.error(f"Error processing images: {str(e)}")
# About section
st.sidebar.markdown("---")
st.sidebar.header("About")
st.sidebar.info("""
This app uses YOLO models for object detection.
- YOLOv8s: Faster detection
- YOLOv9m: Higher accuracy
""") |