Upload Web_application2.py

Web_application2.py

@@ -0,0 +1,161 @@
+import os
+import streamlit as st
+import cv2
+from PIL import Image
+from ultralytics import YOLO
+import base64
+import fitz
+os.chdir(r'D:\WebApplication_YOLO_AD_detectsystem\The Trail Image Folder')
+# Define a function to apply custom CSS
+pathimage = r"D:\WebApplication_YOLO_AD_detectsystem\BackgroundImage\rm314-adj-10.jpg"
+
+def get_base64(bin_file):
+    with open(bin_file, 'rb') as f:
+        data = f.read()
+    return base64.b64encode(data).decode()
+
+
+def set_background(png_file):
+    bin_str = get_base64(png_file)
+    page_bg_img = '''
+    <style>
+    .stApp {
+    background-image: url("data:image/png;base64,%s");
+    background-size: cover;
+    }
+    </style>
+    ''' % bin_str
+    st.markdown(page_bg_img, unsafe_allow_html=True)
+
+set_background(pathimage)
+
+st.title(':orange[Advertisement Detection Web App]')
+
+# Define custom CSS to style the text area and text color
+# Define custom CSS to style the text area with a black background and white text color
+custom_css = """
+<style>
+    /* Add a border to the text area */
+    .custom-text-area {
+        border: 1px solid #000; /* You can adjust the border properties as needed */
+        border-radius: 5px;
+        padding: 10px;
+        background-color: black; /* Black background color */
+        color: white; /* White text color */
+    }
+</style>
+"""
+
+# Apply the custom CSS
+st.markdown(custom_css, unsafe_allow_html=True)
+
+# Instructions:
+multi = """ Instructions:--
+
+1. The Model Trained with English & Tamil NewsPapers.
+
+2. Use any type of News paper wether PDF or Image file, the Model will automaticall Detect adds.
+
+3. The model will take at a time whole newspaper but it is recommended to upload single page or image. It is very useful for us to count and verify the published ads.
+
+4. The Model accuracy is around 80%. 
+
+5. To convert PDF to images and to get single pages use the Below website upload the news paper and download the single pages.
+
+            'http://172.17.4.69:8501'
+"""
+st.markdown(multi)
+
+# Function to convert PDF to images
+def pdf_to_img(uploaded_file, img_path_prefix):
+    # Save the uploaded file
+    with open(uploaded_file.name, "wb") as f:
+        f.write(uploaded_file.getvalue())
+
+    file_extension = os.path.splitext(uploaded_file.name)[1].lower()
+
+    if file_extension == ".pdf":
+        pdf = fitz.open(uploaded_file.name)
+        image_paths = []
+        for page_number in range(pdf.page_count):
+            page = pdf[page_number]
+            # Convert the page to a pixmap
+            pixmap = page.get_pixmap()
+
+            # Convert the Pixmap to a Pillow Image
+            img = Image.frombytes("RGB", [pixmap.width, pixmap.height], pixmap.samples)
+
+            # Save the image as JPEG
+            image_path = f"{img_path_prefix}_page_{page_number + 1}.jpeg"
+            img.save(image_path)
+            image_paths.append(image_path)
+
+        pdf.close()
+        return image_paths
+    elif file_extension == ".jpeg" or file_extension == ".jpg":
+        # If the uploaded file is already an image, return its path
+        image_path = f"{img_path_prefix}_uploaded_image.jpeg"
+        with open(image_path, "wb") as f:
+            f.write(uploaded_file.getvalue())
+        return [image_path]
+    else:
+        st.error("Unsupported file format. Please upload a PDF or JPEG image.")
+        return []
+
+# Function to perform object detection
+def perform_object_detection(image_path):
+    # Load the YOLO model
+    model = YOLO(r"D:\ADS_Project_Deployment\Models\Detection Models\best31_1000_epochs.pt")
+
+    # Load and preprocess the image
+    img = cv2.imread(image_path)
+
+    results = model(img)
+
+    detections = []  # Store tuples of bounding box and confidence
+    # Access the detected objects and their properties
+    if isinstance(results, list):
+        for res in results:
+            if res.boxes is not None:
+                for det, confidence in zip(res.boxes.xyxy, res.boxes.conf):
+                    x1, y1, x2, y2 = map(int, det[:4])
+                    confidence_value = round(confidence.item(), 2)
+                    detections.append(((x1, y1, x2, y2), confidence_value))
+            else:
+                print("No detections found in the current element.")
+    else:
+        print("No results found.")
+
+    return detections
+
+
+# Main function
+def main():
+    # st.title("Advertisement Detection Web App")
+
+    # File upload
+    uploaded_file = st.file_uploader("Choose a file", type=["pdf", "jpeg", "jpg"])
+
+    if uploaded_file is not None:
+        # Convert PDF to images or use the uploaded image directly
+        image_paths = pdf_to_img(uploaded_file, "uploaded_image")
+
+        if image_paths:
+            # Perform object detection for each image
+            for idx, image_path in enumerate(image_paths):
+                st.image(image_path, caption=f"Page {idx + 1}", use_column_width=True)
+                st.write(f"### Detected Advertisements - Page {idx + 1}")
+                detections = perform_object_detection(image_path)
+
+                # Iterate through the detections and extract the detected images
+                for i, (detection, confidence) in enumerate(detections):
+                    x1, y1, x2, y2 = detection
+                    x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)  # Convert the coordinates to integers
+                    # Crop the image using the bounding box coordinates
+                    detected_image = cv2.imread(image_path)[y1:y2, x1:x2]
+                    # Display the detected image
+                    st.image(detected_image, caption=f"Detected Image {i + 1}", use_column_width=True)
+                    st.write(f"Confidence: {confidence}")
+                    
+if __name__ == "__main__":
+    main()