Upload app.py

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+from skimage.metrics import structural_similarity as compare_ssim
+
+
+def ascii_art_generator(input_image, contrast, threshold1, threshold2, ascii_chars):
+    # Resize the image
+    new_width = 100
+    height, width = input_image.shape[:2]
+    aspect_ratio = height / width
+    new_height = int(aspect_ratio * new_width * 0.55)
+    resized_image = cv2.resize(input_image, (new_width, int(new_height)))
+
+    # Convert to grayscale
+    gray_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY)
+
+    # Apply transformation algorithm with user parameters
+    adjusted_image = cv2.convertScaleAbs(gray_image, alpha=contrast, beta=0)
+    edges = cv2.Canny(
+        adjusted_image, threshold1=int(threshold1), threshold2=int(threshold2)
+    )
+    processed_image = cv2.bitwise_not(edges)
+
+    # Map intensities to ASCII characters
+    def pixel_to_ascii(pixel_value):
+        num_chars = len(ascii_chars)
+        index = int(pixel_value / 255 * (num_chars - 1))
+        return ascii_chars[index]
+
+    # Generate the ASCII art string
+    ascii_art_lines = []
+    for row in processed_image:
+        line = "".join([pixel_to_ascii(pixel) for pixel in row])
+        ascii_art_lines.append(line)
+    ascii_art = "\n".join(ascii_art_lines)
+
+    # Save the ASCII art to a text file
+    with open("ascii_art.txt", "w") as f:
+        f.write(ascii_art)
+
+    # Convert processed_image to RGB format for display
+    processed_image_rgb = cv2.cvtColor(processed_image, cv2.COLOR_GRAY2RGB)
+
+    # Convert ASCII art back to image
+    def ascii_to_image(ascii_art, ascii_chars):
+        # Create a mapping from characters to grayscale values
+        char_to_intensity = {
+            char: int(i / (len(ascii_chars) - 1) * 255)
+            for i, char in enumerate(ascii_chars)
+        }
+        ascii_lines = ascii_art.split("\n")
+        height = len(ascii_lines)
+        width = len(ascii_lines[0]) if height > 0 else 0
+        ascii_image = np.zeros((height, width), dtype=np.uint8)
+        for y, line in enumerate(ascii_lines):
+            for x, char in enumerate(line):
+                ascii_image[y, x] = char_to_intensity.get(char, 0)
+        return ascii_image
+
+    ascii_image = ascii_to_image(ascii_art, ascii_chars)
+
+    # Normalize the original image
+    def normalize_images(original_image, ascii_image_shape):
+        resized_original = cv2.resize(
+            original_image, (ascii_image_shape[1], ascii_image_shape[0])
+        )
+        if len(resized_original.shape) == 3:
+            resized_original = cv2.cvtColor(resized_original, cv2.COLOR_BGR2GRAY)
+        return resized_original
+
+    normalized_original = normalize_images(gray_image, ascii_image.shape)
+
+    # Calculate similarity metrics
+    def calculate_mse(image1, image2):
+        mse = np.mean((image1.astype("float") - image2.astype("float")) ** 2)
+        return mse
+
+    def calculate_ssim(image1, image2):
+        ssim, _ = compare_ssim(image1, image2, full=True)
+        return ssim
+
+    def calculate_psnr(image1, image2):
+        mse = calculate_mse(image1, image2)
+        if mse == 0:
+            return float("inf")
+        PIXEL_MAX = 255.0
+        psnr = 20 * np.log10(PIXEL_MAX / np.sqrt(mse))
+        return psnr
+
+    mse = calculate_mse(normalized_original, ascii_image)
+    ssim = calculate_ssim(normalized_original, ascii_image)
+    psnr = calculate_psnr(normalized_original, ascii_image)
+
+    # Prepare metrics output
+    metrics_text = f"MSE: {mse:.2f}\nSSIM: {ssim:.4f}\nPSNR: {psnr:.2f} dB"
+
+    return ascii_art, "ascii_art.txt", processed_image_rgb, metrics_text
+
+
+# Define the Gradio interface using the updated components
+iface = gr.Interface(
+    fn=ascii_art_generator,
+    inputs=[
+        gr.Image(type="numpy", label="Upload Image"),
+        gr.Slider(0.5, 3.0, step=0.1, value=1.5, label="Contrast"),
+        gr.Slider(0, 255, step=1, value=50, label="Edge Detection Threshold1"),
+        gr.Slider(0, 255, step=1, value=150, label="Edge Detection Threshold2"),
+        gr.Textbox(value="@%#*+=-:. ", label="ASCII Characters (Dark to Light)"),
+    ],
+    outputs=[
+        gr.Textbox(label="ASCII Art"),
+        gr.File(label="Download ASCII Art as Text File"),
+        gr.Image(type="numpy", label="Processed Image"),
+        gr.Textbox(label="Performance Metrics"),
+    ],
+    title="Interactive ASCII Art Generator with Performance Metric",
+    description="Upload an image and adjust parameters to convert it into ASCII art. The performance metric quantifies the similarity between the original image and the ASCII art.",
+    allow_flagging="never",
+)
+
+iface.launch(debug=True, share=True)