Create app.py

app.py

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+import cv2
+import numpy as np
+from ultralytics import YOLO
+import easyocr
+import gradio as gr
+import tempfile
+import os
+
+# Load YOLOv8 plate detection model
+model = YOLO("best.pt")   # <-- your trained plate model
+
+# Initialize OCR
+reader = easyocr.Reader(['en'], gpu=False)
+
+def preprocess_plate(plate_img):
+    gray = cv2.cvtColor(plate_img, cv2.COLOR_BGR2GRAY)
+    blur = cv2.GaussianBlur(gray, (5, 5), 0)
+    thresh = cv2.adaptiveThreshold(
+        blur, 255,
+        cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+        cv2.THRESH_BINARY, 11, 2
+    )
+    return thresh
+
+def recognize_plate(plate_img):
+    processed = preprocess_plate(plate_img)
+    ocr_result = reader.readtext(processed)
+
+    plate_text = ""
+    for (bbox, text, prob) in ocr_result:
+        if prob > 0.4:
+            plate_text += text + " "
+
+    return plate_text.strip()
+
+def process_frame(frame):
+    detected_plates = []
+
+    results = model(frame)
+
+    for r in results:
+        if r.boxes is None:
+            continue
+
+        boxes = r.boxes.xyxy.cpu().numpy()
+        confs = r.boxes.conf.cpu().numpy()
+
+        for box, conf in zip(boxes, confs):
+            x1, y1, x2, y2 = map(int, box)
+
+            plate_img = frame[y1:y2, x1:x2]
+            if plate_img.size == 0:
+                continue
+
+            plate_text = recognize_plate(plate_img)
+
+            detected_plates.append({
+                "plate_text": plate_text,
+                "confidence": float(conf)
+            })
+
+            # Draw bounding box
+            cv2.rectangle(frame, (x1, y1), (x2, y2),
+                          (0, 255, 0), 2)
+
+            # Draw plate text
+            label = plate_text if plate_text else "Plate"
+            cv2.putText(frame, label,
+                        (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX,
+                        0.8, (255, 0, 0), 2)
+
+    return frame, detected_plates
+
+# =========================
+# IMAGE MODE
+# =========================
+def process_image(image):
+    frame = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    annotated_frame, plates = process_frame(frame)
+    annotated_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
+
+    plate_texts = [p["plate_text"] for p in plates if p["plate_text"]]
+    result_text = "\n".join(plate_texts) if plate_texts else "No plates detected."
+
+    return annotated_frame, result_text
+
+# =========================
+# VIDEO MODE
+# =========================
+def process_video(video_file):
+    cap = cv2.VideoCapture(video_file)
+
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+
+    temp_out = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    out_path = temp_out.name
+    temp_out.close()
+
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
+
+    all_detected = set()
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        annotated_frame, plates = process_frame(frame)
+
+        for p in plates:
+            if p["plate_text"]:
+                all_detected.add(p["plate_text"])
+
+        out.write(annotated_frame)
+
+    cap.release()
+    out.release()
+
+    result_text = "\n".join(all_detected) if all_detected else "No plates detected."
+
+    return out_path, result_text
+
+# =========================
+# GRADIO UI
+# =========================
+with gr.Blocks() as demo:
+    gr.Markdown("## Smart Traffic & EV Analytics System")
+    gr.Markdown("Upload an image or video to detect multiple vehicle number plates.")
+
+    with gr.Tabs():
+        with gr.Tab("Image"):
+            image_input = gr.Image(type="numpy", label="Upload Image")
+            image_output = gr.Image(label="Detected Plates")
+            image_text = gr.Textbox(label="Recognized Plate Numbers")
+
+            image_button = gr.Button("Detect Plates")
+            image_button.click(process_image,
+                               inputs=image_input,
+                               outputs=[image_output, image_text])
+
+        with gr.Tab("Video"):
+            video_input = gr.Video(label="Upload Video")
+            video_output = gr.Video(label="Processed Video")
+            video_text = gr.Textbox(label="Recognized Plate Numbers")
+
+            video_button = gr.Button("Detect Plates")
+            video_button.click(process_video,
+                               inputs=video_input,
+                               outputs=[video_output, video_text])
+
+demo.launch()