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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+demo.mp4 filter=lfs diff=lfs merge=lfs -text
+temp/output_demo.mp4 filter=lfs diff=lfs merge=lfs -text

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ca30bc0b183d5932931b21c53b2e3f6ab8a773e26b0e737d36066da195507b05
+size 6217113

demo.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e707d2ae1c912beb3e661a4c9f2b1587250e0abaa34bd524a7ceef0cdd26e93d
+size 9563349

main.py

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+import streamlit as st
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from PIL import Image
+import os
+st.title("YOLO Image and Video Processing")
+
+# Allow users to upload images or videos
+uploaded_file = st.file_uploader("Upload an image or video", type=["jpg", "jpeg", "png", "bmp", "mp4", "avi", "mov", "mkv"])
+try:
+    model = YOLO(r'C:\Users\vedan\PycharmProjects\license_plate\best.pt')  # Replace with the path to your trained YOLO model
+
+except Exception as e:
+    st.error(f"Error loading YOLO model: {e}")
+
+
+def predict_and_save_image(path_test_car, output_image_path):
+    """
+    Predicts and saves the bounding boxes on the given test image using the trained YOLO model.
+
+    Parameters:
+    path_test_car (str): Path to the test image file.
+    output_image_path (str): Path to save the output image file.
+
+    Returns:
+    str: The path to the saved output image file.
+    """
+    try:
+        results = model.predict(path_test_car, device='cpu')
+        image = cv2.imread(path_test_car)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        for result in results:
+            for box in result.boxes:
+                x1, y1, x2, y2 = map(int, box.xyxy[0])
+                confidence = box.conf[0]
+                cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                cv2.putText(image, f'{confidence * 100:.2f}%', (x1, y1 - 10),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        cv2.imwrite(output_image_path, image)
+        return output_image_path
+    except Exception as e:
+        st.error(f"Error processing image: {e}")
+        return None
+
+
+def predict_and_plot_video(video_path, output_path):
+    """
+    Predicts and saves the bounding boxes on the given test video using the trained YOLO model.
+
+    Parameters:
+    video_path (str): Path to the test video file.
+    output_path (str): Path to save the output video file.
+
+    Returns:
+    str: The path to the saved output video file.
+    """
+    try:
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            st.error(f"Error opening video file: {video_path}")
+            return None
+        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps = int(cap.get(cv2.CAP_PROP_FPS))
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+            rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            results = model.predict(rgb_frame, device='cpu')
+            for result in results:
+                for box in result.boxes:
+                    x1, y1, x2, y2 = map(int, box.xyxy[0])
+                    confidence = box.conf[0]
+                    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                    cv2.putText(frame, f'{confidence * 100:.2f}%', (x1, y1 - 10),
+                                cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
+            out.write(frame)
+        cap.release()
+        out.release()
+        return output_path
+    except Exception as e:
+        st.error(f"Error processing video: {e}")
+        return None
+
+
+def process_media(input_path, output_path):
+    """
+    Processes the uploaded media file (image or video) and returns the path to the saved output file.
+
+    Parameters:
+    input_path (str): Path to the input media file.
+    output_path (str): Path to save the output media file.
+
+    Returns:
+    str: The path to the saved output media file.
+    """
+    file_extension = os.path.splitext(input_path)[1].lower()
+    if file_extension in ['.mp4', '.avi', '.mov', '.mkv']:
+        return predict_and_plot_video(input_path, output_path)
+    elif file_extension in ['.jpg', '.jpeg', '.png', '.bmp']:
+        return predict_and_save_image(input_path, output_path)
+    else:
+        st.error(f"Unsupported file type: {file_extension}")
+        return None
+
+
+if uploaded_file is not None:
+    input_path = os.path.join("temp", uploaded_file.name)
+    output_path = os.path.join("temp", f"output_{uploaded_file.name}")
+    try:
+        with open(input_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        st.write("Processing...")
+        result_path = process_media(input_path, output_path)
+        if result_path:
+            if input_path.endswith(('.mp4', '.avi', '.mov', '.mkv')):
+                video_file = open(result_path, 'rb')
+                video_bytes = video_file.read()
+                st.video(video_bytes)
+            else:
+                st.image(result_path)
+    except Exception as e:
+        st.error(f"Error uploading or processing file: {e}")

requirements.txt

@@ -0,0 +1,8 @@
+pandas
+matplotlib
+streamlit
+opencv-python
+ultralytics
+numpy
+pillow
+

temp/demo.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e707d2ae1c912beb3e661a4c9f2b1587250e0abaa34bd524a7ceef0cdd26e93d
+size 9563349

temp/output_demo.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:099c667151f6e6cdf3f0615b185e227bcd55076e0a7437f68c6a24129c4b1709
+size 16161833