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app.py

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+import streamlit as st
+import cv2
+from ultralytics import YOLO
+import os
+import easyocr
+from moviepy import ImageSequenceClip
+
+
+st.title("🚗 PlateVision 🔍")
+st.caption("AI-powered license plate detection & recognition from images and videos")
+
+
+os.makedirs("input", exist_ok=True)
+os.makedirs("output", exist_ok=True)
+
+@st.cache_resource
+def load_yolo_model():
+    return YOLO("best_model.pt")
+
+@st.cache_resource
+def load_ocr_reader():
+    return easyocr.Reader(['en'], model_storage_directory='model')
+
+def process_and_find_plate(input_path, output_path):
+    extension = os.path.splitext(input_path)[1].lower()
+    if extension in ['.mp4', '.mkv']:
+        path = find_plate_on_video(input_path, output_path)
+
+    elif extension in ['.jpg', '.jpeg', '.png']:
+        path = find_plate_on_image(input_path, output_path)
+     
+    else:
+        st.error("Unsupported file type")
+        return None
+
+    return path
+    
+def find_plate_on_image(input_path, output_path):
+    
+    model = load_yolo_model()
+    reader = load_ocr_reader()
+    
+    image = cv2.imread(input_path)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    outputs = model.predict(image, verbose=False)
+    
+    for output in outputs:
+        for box in output.boxes:
+            x1, y1, x2, y2 = map(int, box.xyxy[0])
+            confidence = box.conf[0]
+            roi = image[y1:y2, x1:x2]
+            results = reader.readtext(roi)
+            try:
+                plate_num = results[0][1].strip()
+            except Exception as e:
+               plate_num = "" 
+                
+            if plate_num == "":
+                plate_num = "Not Visible!" 
+                
+            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 0, 255), 2)
+            cv2.putText(image, f'{confidence*100:.2f}%', (x1, y1-20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2, cv2.LINE_AA)
+            cv2.putText(image, f'Number: {plate_num}', (x1, y2+20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2, cv2.LINE_AA)            
+              
+    cv2.imwrite(output_path, image)
+    return output_path
+            
+
+def find_plate_on_video(input_path, output_path):
+    model = load_yolo_model()
+    reader = load_ocr_reader()
+
+    cap = cv2.VideoCapture(input_path)
+    if not cap.isOpened():
+        st.error("Error opening the video")
+        return None
+
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    frames = [] 
+    frame_idx = 0
+    skip_frame = 5
+
+    progress_bar = st.progress(0, text="🔍 Analyzing video frames...")
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        if frame_idx % skip_frame != 0:
+            continue
+
+        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        outputs = model.predict(rgb_frame, verbose=False)
+
+        for output in outputs:
+            for box in output.boxes:
+                x1, y1, x2, y2 = map(int, box.xyxy[0])
+                confidence = box.conf[0]
+                roi = frame[y1:y2, x1:x2]
+                results = reader.readtext(roi)
+                try:
+                    plate_num = results[0][1].strip()
+                except Exception:
+                    plate_num = ""
+
+                if plate_num == "":
+                    plate_num = "Not Visible!"
+                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
+                cv2.putText(frame, f'{confidence*100:.2f}%', (x1, y1 - 20),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+                cv2.putText(frame, f'Number: {plate_num}', (x1, y2 + 20),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+        frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        frame_idx += 1
+
+        # Update progress
+        progress = min(frame_idx / total_frames, 1.0)
+        progress_bar.progress(progress, text=f"📸 Processed {frame_idx}/{total_frames} frames...")
+
+    cap.release()
+    progress_bar.empty()
+
+    # Write final video using MoviePy
+    clip = ImageSequenceClip(frames, fps=fps // frame_skip if fps >= frame_skip else 1)
+    clip.write_videofile(output_path, codec='libx264', audio=False, logger=None)
+
+    return output_path
+
+        
+
+uploaded_file = st.file_uploader("📤 Upload an image or video", type=['jpg', 'jpeg', 'png', 'mp4', 'mkv'])
+
+if uploaded_file is not None:
+    input_path = f"input/{uploaded_file.name}"
+    output_path = f"output/{uploaded_file.name}"
+    with open(input_path, 'wb') as f:
+        f.write(uploaded_file.getbuffer())
+    with st.spinner("🚦 Detecting plates... please fasten your seatbelt!"):
+        path = process_and_find_plate(input_path, output_path)
+
+    if path.endswith(('.mp4', '.mkv')):
+        video_file = open(path, 'rb')
+        video_bytes = video_file.read()
+        st.video(video_bytes)
+    elif path.endswith(('.jpg', '.jpeg', '.png')):
+        st.image(path)
+    else:
+        st.error("Error occured while proccessing the file.")
+    
+
+
+    
+

best_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4ac0243025d1c33e3a71714baf889304f64100cb53bcd0ee7b368779d102d7c6
+size 6234533

model/craft_mlt_25k.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4a5efbfb48b4081100544e75e1e2b57f8de3d84f213004b14b85fd4b3748db17
+size 83152330

model/english_g2.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e2272681d9d67a04e2dff396b6e95077bc19001f8f6d3593c307b9852e1c29e8
+size 15143997

requirements.txt

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+streamlit == 1.50.0
+opencv-python == 4.12.0.88
+ultralytics == 8.3.221
+easyocr == 1.7.2
+moviepy == 2.2.1
+imageio-ffmpeg == 0.6.0