Upload 3 files

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import pytesseract
+from PIL import Image
+import os
+
+# Set page title and icon
+st.set_page_config(page_title="Motorcycle Helmet Detection", layout="wide")
+
+# Set Tesseract path
+pytesseract.pytesseract.tesseract_cmd = '/opt/homebrew/bin/tesseract'
+
+# Load models
+@st.cache_resource
+def load_models():
+    # Load YOLO models
+    detection_model = YOLO("yolov8n.pt")
+    
+    # Load face detection model
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    
+    return detection_model, face_cascade
+
+# Load the models
+try:
+    detection_model, face_cascade = load_models()
+    st.sidebar.success("✅ Models loaded successfully")
+except Exception as e:
+    st.error(f"Error loading models: {str(e)}")
+    st.stop()
+
+def extract_license_plate(image, roi=None):
+    """Extract license plate from image or region of interest"""
+    if roi is not None:
+        # If a region is specified, use it
+        target = roi
+    else:
+        # Otherwise use the whole image
+        target = image
+        
+    plate_text = None
+    
+    try:
+        # Convert to grayscale
+        gray = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)
+        gray = cv2.bilateralFilter(gray, 11, 17, 17)
+        edged = cv2.Canny(gray, 30, 200)
+        
+        # Find contours
+        contours, _ = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+        contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
+        
+        for contour in contours:
+            perimeter = cv2.arcLength(contour, True)
+            approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
+            
+            # License plates are typically rectangular (4 points)
+            if len(approx) == 4:
+                x, y, w, h = cv2.boundingRect(approx)
+                
+                # Filter by aspect ratio - license plates are typically wider than tall
+                aspect_ratio = float(w) / h
+                if 1.5 < aspect_ratio < 5.0:
+                    plate_roi = gray[y:y+h, x:x+w]
+                    
+                    if plate_roi.size > 0:
+                        # Resize for better OCR
+                        plate_roi = cv2.resize(plate_roi, None, fx=2, fy=2)
+                        # Apply threshold to improve text extraction
+                        _, plate_roi = cv2.threshold(plate_roi, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+                        # Extract text
+                        plate_text = pytesseract.image_to_string(plate_roi, 
+                                                                config='--psm 7 --oem 3 -c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789')
+                        plate_text = ''.join(e for e in plate_text if e.isalnum())
+                        
+                        if len(plate_text) > 3:  # Basic validation
+                            return plate_text
+    except Exception as e:
+        print(f"Error extracting license plate: {e}")
+        
+    return plate_text
+
+def detect_helmet_with_circle_detection(image, head_region):
+    """Detect helmet using circle detection"""
+    try:
+        # Extract head region
+        x, y, w, h = head_region
+        head = image[y:y+h, x:x+w]
+        
+        # Convert to grayscale
+        gray = cv2.cvtColor(head, cv2.COLOR_BGR2GRAY)
+        
+        # Apply GaussianBlur to reduce noise
+        gray = cv2.GaussianBlur(gray, (5, 5), 0)
+        
+        # Detect circles
+        circles = cv2.HoughCircles(
+            gray, cv2.HOUGH_GRADIENT, dp=1, minDist=20,
+            param1=50, param2=30, minRadius=int(w*0.2), maxRadius=int(w*0.6)
+        )
+        
+        # If circles detected, likely a helmet
+        return circles is not None and len(circles[0]) > 0
+    except:
+        return False
+
+def detect_helmet_with_color(image, head_region):
+    """Detect helmet using color analysis"""
+    try:
+        # Extract head region
+        x, y, w, h = head_region
+        head = image[y:y+h, x:x+w]
+        
+        # Convert to HSV
+        hsv = cv2.cvtColor(head, cv2.COLOR_BGR2HSV)
+        
+        # Define color ranges for common helmet colors (black, white, red, blue)
+        # Black
+        lower_black = np.array([0, 0, 0])
+        upper_black = np.array([180, 255, 50])
+        black_mask = cv2.inRange(hsv, lower_black, upper_black)
+        
+        # White
+        lower_white = np.array([0, 0, 200])
+        upper_white = np.array([180, 30, 255])
+        white_mask = cv2.inRange(hsv, lower_white, upper_white)
+        
+        # Red (two ranges)
+        lower_red1 = np.array([0, 100, 100])
+        upper_red1 = np.array([10, 255, 255])
+        red_mask1 = cv2.inRange(hsv, lower_red1, upper_red1)
+        
+        lower_red2 = np.array([160, 100, 100])
+        upper_red2 = np.array([180, 255, 255])
+        red_mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
+        
+        # Blue
+        lower_blue = np.array([100, 100, 100])
+        upper_blue = np.array([140, 255, 255])
+        blue_mask = cv2.inRange(hsv, lower_blue, upper_blue)
+        
+        # Combine masks
+        combined_mask = black_mask + white_mask + red_mask1 + red_mask2 + blue_mask
+        
+        # Calculate percentage of helmet colors
+        helmet_color_percentage = np.sum(combined_mask > 0) / (w * h)
+        
+        # If more than 30% of the head region has helmet-like colors, likely a helmet
+        return helmet_color_percentage > 0.3
+    except:
+        return False
+
+def process_image(image):
+    if isinstance(image, Image.Image):
+        # Convert PIL image to OpenCV format
+        image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    
+    # Make a copy for annotation
+    annotated_img = image.copy()
+    
+    # Run YOLO detection for motorcycle and person
+    results = detection_model(image)[0]
+    
+    # Initialize variables
+    helmet_detected = False
+    person_detected = False
+    motorcycle_detected = False
+    plate_text = None
+    
+    # Variables to store bounding boxes
+    person_boxes = []
+    motorcycle_boxes = []
+    
+    # Check detections
+    for box in results.boxes:
+        cls = int(box.cls[0])
+        conf = float(box.conf[0])
+        
+        if conf > 0.3:  # Confidence threshold
+            x1, y1, x2, y2 = map(int, box.xyxy[0])
+            
+            # Standard YOLOv8 classes
+            if cls == 0:  # Person
+                person_detected = True
+                person_boxes.append((x1, y1, x2, y2))
+                # Get head region (top 30% of person bounding box)
+                head_x = x1
+                head_y = y1
+                head_w = x2 - x1
+                head_h = int((y2 - y1) * 0.3)
+                head_region = (head_x, head_y, head_w, head_h)
+                
+                # Draw head region for debugging
+                cv2.rectangle(annotated_img, (head_x, head_y), (head_x + head_w, head_y + head_h), (0, 255, 255), 2)
+                
+                # Extract head image
+                head_img = image[head_y:head_y+head_h, head_x:head_x+head_w]
+                
+                # Use multiple methods to detect helmet
+                if head_img.size > 0:
+                    # Method 1: Check for faces - visible face might indicate no helmet
+                    gray = cv2.cvtColor(head_img, cv2.COLOR_BGR2GRAY)
+                    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+                    
+                    if len(faces) > 0:
+                        # Face detected, likely no helmet
+                        helmet_detected = False
+                        cv2.putText(annotated_img, "Face Detected", (head_x, head_y - 10), 
+                                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+                    else:
+                        # No face detected, check other methods
+                        # Method 2: Circle detection
+                        helmet_detected = detect_helmet_with_circle_detection(image, head_region)
+                        
+                        # Method 3: Color analysis
+                        if not helmet_detected:
+                            helmet_detected = detect_helmet_with_color(image, head_region)
+                
+            elif cls == 3:  # Motorcycle
+                motorcycle_detected = True
+                motorcycle_boxes.append((x1, y1, x2, y2))
+                
+                # Extract license plate from motorcycle region
+                roi = image[y1:y2, x1:x2]
+                if roi.size > 0:
+                    moto_plate = extract_license_plate(image, roi)
+                    if moto_plate:
+                        plate_text = moto_plate
+    
+    # If a person on a motorcycle is detected, but no license plate found yet, try on full image
+    if person_detected and motorcycle_detected and not plate_text:
+        plate_text = extract_license_plate(image)
+    
+    # Add manual override option via sidebar
+    st.sidebar.markdown("### Detection Override")
+    if st.sidebar.checkbox("Override automatic detection"):
+        helmet_detected = st.sidebar.radio("Helmet Status:", [True, False], index=0 if helmet_detected else 1)
+    
+    return helmet_detected, plate_text, cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB)
+
+# Streamlit UI
+st.title("🏍️ Motorcycle Helmet Detection System")
+st.write("Upload an image to detect helmet usage and license plate")
+
+uploaded_file = st.file_uploader("Choose an image", type=['jpg', 'jpeg', 'png'])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    with st.spinner('Processing image...'):
+        helmet_detected, plate_text, processed_image = process_image(image)
+        
+        with col2:
+            st.image(processed_image, caption="Processed Image", use_column_width=True)
+        
+        # Display results with custom styling
+        if helmet_detected:
+            st.markdown("""
+                <div style='padding: 20px; background-color: #d4edda; border-radius: 5px; margin: 10px 0;'>
+                    <h3 style='color: #155724; margin: 0;'>✅ Helmet Detected!</h3>
+                </div>
+                """, unsafe_allow_html=True)
+        else:
+            st.markdown("""
+                <div style='padding: 20px; background-color: #f8d7da; border-radius: 5px; margin: 10px 0;'>
+                    <h3 style='color: #721c24; margin: 0;'>❌ No Helmet Detected - Violation!</h3>
+                </div>
+                """, unsafe_allow_html=True)
+            
+            # Always show license plate if detected, but emphasize when violation occurs
+            if plate_text:
+                st.markdown(f"""
+                    <div style='padding: 20px; background-color: #f8d7da; border-radius: 5px; margin: 10px 0;'>
+                        <h3 style='color: #721c24; margin: 0;'>License Plate Number:</h3>
+                        <p style='font-size: 24px; margin: 10px 0 0 0;'>{plate_text}</p>
+                    </div>
+                    """, unsafe_allow_html=True)
+        
+        # Only show license plate in neutral box if compliant
+        if helmet_detected and plate_text:
+            st.markdown(f"""
+                <div style='padding: 20px; background-color: #e2e3e5; border-radius: 5px; margin: 10px 0;'>
+                    <h3 style='color: #383d41; margin: 0;'>License Plate Number:</h3>
+                    <p style='font-size: 24px; margin: 10px 0 0 0;'>{plate_text}</p>
+                </div>
+                """, unsafe_allow_html=True)

requirements.txt

@@ -0,0 +1,835 @@
+absl-py==2.1.0
+aiobotocore @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_71xswk40o_/croot/aiobotocore_1682537536268/work
+aiofiles @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_f56ag8l7kr/croot/aiofiles_1683773599608/work
+aiohappyeyeballs==2.4.4
+aiohttp==3.11.11
+aioitertools @ file:///tmp/build/80754af9/aioitertools_1607109665762/work
+aiosignal @ file:///tmp/build/80754af9/aiosignal_1637843061372/work
+aiosqlite @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_3d75lecab1/croot/aiosqlite_1683773918307/work
+alabaster @ file:///home/ktietz/src/ci/alabaster_1611921544520/work
+altair==5.0.1
+anaconda-catalogs @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_e8tmw882qa/croot/anaconda-catalogs_1685727305051/work
+anaconda-client==1.11.3
+anaconda-navigator==2.4.2
+anaconda-project @ file:///Users/cbousseau/work/recipes/ci_py311/anaconda-project_1677964558977/work
+aniso8601==9.0.1
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+appdirs==1.4.4
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+appnope @ file:///Users/cbousseau/work/recipes/ci_py311/appnope_1677917710869/work
+appscript @ file:///Users/cbousseau/work/recipes/ci_py311/appscript_1677956964648/work
+argon2-cffi @ file:///opt/conda/conda-bld/argon2-cffi_1645000214183/work
+argon2-cffi-bindings @ file:///Users/cbousseau/work/recipes/ci_py311/argon2-cffi-bindings_1677915727169/work
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+backports.tempfile @ file:///home/linux1/recipes/ci/backports.tempfile_1610991236607/work
+backports.weakref==1.0.post1
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+boltons @ file:///Users/cbousseau/work/recipes/ci_py311/boltons_1677965141748/work
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+-e git+https://github.com/browser-use/browser-use@2d0f95f80150bffe788041c1811d14bc394481e3#egg=browser_use
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+conda-build @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_83sb1t82jg/croot/conda-build_1685026138255/work
+conda-content-trust @ file:///Users/cbousseau/work/recipes/ci_py311/conda-content-trust_1677970020347/work
+conda-libmamba-solver @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_48qgqxi1ys/croot/conda-libmamba-solver_1685032355439/work/src
+conda-pack @ file:///tmp/build/80754af9/conda-pack_1611163042455/work
+conda-package-handling @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_57h_oo3f_8/croot/conda-package-handling_1685024802254/work
+conda-repo-cli==1.0.41
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+dataclasses-json==0.5.14
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+datashader @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_45d140pymm/croot/datashader_1685542975108/work
+datashape==0.5.4
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+Deprecated==1.2.15
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+gmpy2 @ file:///Users/cbousseau/work/recipes/ci_py311/gmpy2_1677937751357/work
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