Initial commit

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image
+from ultralytics import YOLO  # YOLOv8 from Ultralytics
+
+# Load YOLOv8 model (pre-trained on COCO dataset)
+model = YOLO("yolov8n.pt")  # Using the "nano" model (fast & lightweight)
+
+#apply smoothing using OpenCV's medianBlur
+def smooth_image(image):
+    image = np.array(image)  # Convert PIL image to NumPy array
+    smoothed = cv2.medianBlur(image, 15)  # Apply median blur with kernel size 5
+    return Image.fromarray(smoothed)  # Convert back to PIL image
+
+#apply Erosion Morphological Transformation
+def erode_image(image):
+    image = np.array(image)
+    kernel = np.ones((3, 3), np.uint8)  # Define a 3x3 kernel
+    eroded = cv2.erode(image, kernel, iterations=1)  # Apply erosion
+    return Image.fromarray(eroded)  # Convert back to PIL image
+
+#apply image segmentation using Otsu's Thresholding
+def segment_image(image):
+    image = np.array(image)
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  # Convert to grayscale
+    _, segmented = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)  # Apply Otsu's thresholding
+    return Image.fromarray(segmented)  # Convert back to PIL image
+
+#apply Fourier Transform and display magnitude spectrum
+def fourier_transform(image):
+    image = np.array(image)
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  # Convert to grayscale
+    dft = np.fft.fft2(gray)  # Compute Fourier Transform
+    dft_shift = np.fft.fftshift(dft)  # Shift zero frequency to center
+    magnitude_spectrum = 20 * np.log(np.abs(dft_shift) + 1)  # Compute magnitude spectrum
+    magnitude_spectrum = np.uint8(255 * (magnitude_spectrum / np.max(magnitude_spectrum)))  # Normalize for display
+    return Image.fromarray(magnitude_spectrum)
+
+def detect_objects(image):
+    image = np.array(image)  # Convert PIL image to NumPy array
+
+    # Perform object detection
+    results = model(image)
+
+    # Process detections
+    for result in results:
+        boxes = result.boxes.xyxy  # Bounding boxes (x1, y1, x2, y2)
+        confidences = result.boxes.conf  # Confidence scores
+        class_ids = result.boxes.cls.int().tolist()  # Class labels
+
+        for box, conf, class_id in zip(boxes, confidences, class_ids):
+            x1, y1, x2, y2 = map(int, box.tolist())
+            label = f"{model.names[class_id]} ({conf:.2f})"
+
+            # Draw bounding box & label
+            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+    return Image.fromarray(image)  # Convert back to PIL Image for Gradio
+
+
+
+def create_interface():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(label="Upload Image", type="pil")
+            with gr.Column():
+                output_image = gr.Image(label="Processed Image", type="pil")
+
+        with gr.Row():
+            smoothing_button = gr.Button("Smoothing/ Blurring")
+            morphological_transform_button = gr.Button("Morphological Transformations")
+            fourier_transform_button = gr.Button("Fourier Transform")
+            segmentation_button = gr.Button("Segmentation")
+            object_recognition_button = gr.Button("Object Recognition (YOLO)")
+
+        # Link buttons to their respective functions
+        smoothing_button.click(smooth_image, inputs=image_input, outputs=output_image)
+        morphological_transform_button.click(erode_image, inputs=image_input, outputs=output_image)
+        fourier_transform_button.click(fourier_transform, inputs=image_input, outputs=output_image)
+        segmentation_button.click(segment_image, inputs=image_input, outputs=output_image)
+        object_recognition_button.click(detect_objects, inputs=image_input, outputs=output_image)
+
+    return demo
+
+
+# Launch the Gradio app
+app = create_interface()
+app.launch()

requirements.txt

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+gradio==5.23.3
+numpy==2.2.4
+opencv_python==4.11.0.86
+Pillow==11.1.0
+ultralytics==8.3.100

yolov8n.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f59b3d833e2ff32e194b5bb8e08d211dc7c5bdf144b90d2c8412c47ccfc83b36
+size 6549796