Create app.py

app.py

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+from transformers import DetrImageProcessor, DetrForObjectDetection, TrOCRProcessor, VisionEncoderDecoderModel
+import cv2
+from PIL import Image, ImageDraw
+import torch
+import streamlit as st
+
+# Load Hugging Face Models
+detr_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+detr_model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+trocr_processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-stage1")
+trocr_model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-stage1")
+
+# Detect license plates
+def detect_license_plate(frame):
+    pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+    inputs = detr_processor(images=pil_image, return_tensors="pt")
+    outputs = detr_model(**inputs)
+
+    target_sizes = torch.tensor([pil_image.size[::-1]])
+    results = detr_processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)
+
+    return results[0]["boxes"], pil_image
+
+# Recognize text
+def recognize_text_from_plate(cropped_plate):
+    inputs = trocr_processor(images=cropped_plate, return_tensors="pt")
+    outputs = trocr_model.generate(**inputs)
+    return trocr_processor.batch_decode(outputs, skip_special_tokens=True)[0]
+
+
+
+
+# Streamlit configuration
+st.title("Real-Time Car Number Plate Recognition")
+st.text("This application uses Hugging Face Transformers to detect and recognize car plates.")
+
+# Authorized car database
+authorized_cars = {"KA01AB1234", "MH12XY5678", "DL8CAF9090"}
+
+# Verification function
+def verify_plate(plate_text):
+    if plate_text in authorized_cars:
+        return f"✅ Access Granted: {plate_text}"
+    else:
+        return f"❌ Access Denied: {plate_text}"
+
+
+# Live video feed and processing
+def live_feed():
+    cap = cv2.VideoCapture(0)  # Open the webcam
+    stframe = st.empty()  # Streamlit frame for displaying video
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Detect license plates
+        boxes, pil_image = detect_license_plate(frame)
+        draw = ImageDraw.Draw(pil_image)
+
+        recognized_plates = []
+        for box in boxes:
+            # Crop the detected plate
+            cropped_plate = pil_image.crop((box[0], box[1], box[2], box[3]))
+
+            # Recognize text
+            plate_text = recognize_text_from_plate(cropped_plate)
+            recognized_plates.append(plate_text)
+
+            # Draw bounding box and text
+            draw.rectangle(box.tolist(), outline="red", width=2)
+            draw.text((box[0], box[1]), plate_text, fill="red")
+
+        # Convert PIL image back to OpenCV format
+        processed_frame = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
+
+        # Stream the video to Streamlit
+        stframe.image(processed_frame, channels="BGR")
+
+        # Show results
+        for plate_text in recognized_plates:
+            st.write(verify_plate(plate_text))
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+if st.button("Start Camera"):
+    live_feed()
+