Update app.py

app.py

@@ -1,199 +1,189 @@
-"""
-CS5330 Fall 2024
-Author: Calvin Lo
-Lab 1: ASCII Art
-
-This program generates the ASCII art representation of
-an input image and compares the result to the original image
-using SSIM.
-"""
-
-import gradio as gr
-import cv2
-import numpy as np
-from PIL import Image, ImageDraw, ImageFont, ImageFilter, ImageEnhance
-from skimage.metrics import structural_similarity as ssim
-
-# ASCII characters to map pixel intensity
-ASCII_CHARS = [
-    ' ', '.', ',', ':', ';', 'i', 'l', '!', 'I', '1', 't', 'o', 'r', 'x', 'z', 'v', 
-    'u', 'n', 'm', 'w', 'Q', 'B', 'N', 'M', '@'
-]
-
-
-def preprocess_image(image, new_width=150):
-    """
-    This function performs the following operations on the input image:
-    -enhance contrast
-    -perform edge detection (Sobel)
-    -denoise edges
-    -enhance contrast
-    -resize image
-    """
-    # Open the image and convert to grayscale
-    image = Image.fromarray(image)
-    image = image.convert("L")
-
-    # Contrast limited adaptive histogram equalization
-    opencv_image = np.array(image)
-    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-    equalized_image = clahe.apply(opencv_image)
-
-    # Apply Sobel edge detection
-    sobelx = cv2.Sobel(equalized_image, cv2.CV_64F, 1, 0, ksize=5)
-    sobely = cv2.Sobel(equalized_image, cv2.CV_64F, 0, 1, ksize=5)
-    edges = np.hypot(sobelx, sobely)  # Combine gradients
-
-    # Normalize the result to fit into the 0-255 grayscale range
-    edges = np.uint8(255 * edges / np.max(edges))
-
-    # Denoise the edges using Non-Local Means Denoising
-    edges = cv2.fastNlMeansDenoising(edges, None, h=30, templateWindowSize=7, searchWindowSize=21)
-
-    # Improve contrast of edges
-    edges_image = Image.fromarray(edges)
-    enhancer = ImageEnhance.Contrast(edges_image)
-    enhanced_edges = enhancer.enhance(1.5)
-    # enhanced_edges_array = np.array(enhanced_edges)
-
-    # Invert the grayscale values of the original image
-    inverted_image = 255 - np.array(equalized_image)
-
-    # Convert sharpened edges to array for blending
-    sharpened_edges_array = np.array(enhanced_edges )
-
-    # Blend the inverted image with the sharpened edges using the blend_percentage
-    blended_image = cv2.addWeighted(sharpened_edges_array, 1 - blend_percentage,
-                                    inverted_image, blend_percentage, 0)
-
-    # Resize the image for ASCII aspect ratio
-    width, height = image.size
-    aspect_ratio = height / width * 0.55  # Adjusting for ASCII aspect ratio
-    new_height = int(aspect_ratio * new_width)
-    resized_blended_image = Image.fromarray(blended_image).resize((new_width, new_height))
-
-    return resized_blended_image
-
-
-def image_to_ascii(image):
-    """
-    This function generates an ASCII representation of images.
-    """
-    pixels = np.array(image)
-    ascii_str = ""
-    for row in pixels:
-        for pixel in row:
-            # Mapping pixel intensity to ASCII
-            ascii_str += ASCII_CHARS[pixel // 10]
-        ascii_str += "\n"
-    return ascii_str
-
-
-def ascii_to_image(ascii_art, font_size=12, output_file='ascii_art.png'):
-    """"
-    This function converts ASCII text art to a png image.
-    """
-
-    # Specify font
-    font_path = "DejaVuSansMono.ttf"
-    font = ImageFont.truetype(font_path, font_size)
-
-    # Get the required image dimensions
-    lines = ascii_art.splitlines()
-    max_width = max(font.getbbox(line)[2] for line in lines)  # Get max width
-    img_height = len(lines) * font_size
-
-    # Generate image with white background
-    image = Image.new("RGB", (max_width, img_height), "white")
-    draw = ImageDraw.Draw(image)
-
-    # Draw each character from the ASCII text on the image
-    for y, line in enumerate(lines):
-        draw.text((0, y * font_size), line, fill="black", font=font)
-
-    image.save(output_file)
-    return image
-
-
-def compare_ssim(image1, image2):
-    """
-    This function generates the structural similarity index
-    measure for the two input images.
-    """
-    # Convert original color image to grayscale
-    image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
-    
-    # Convert ASCII image to grayscale and cv2 format
-    image2 = np.array(image2.convert("L"))
-
-    # Resize so both images have the same dimensions.
-    image2 = cv2.resize(image2, (image1.shape[1], image1.shape[0]))
-
-    score, _ = ssim(image1, image2, full=True)
-    return score
-
-
-def process_image(image):
-    """
-    This function takes an image and generates:
-        -ASCII art as a string
-        -ASCII text file
-        -Compute SSIM for the ASCII art image and the original image
-    """
-    # Get image edges and convert to ASCII
-    edges = preprocess_image(image)
-    ascii_art = image_to_ascii(edges)
-
-    # Convert ASCII art back to an image for SSIM calculation
-    ascii_image = ascii_to_image(ascii_art)
-
-    # Calculate SSIM
-    ssim_score = compare_ssim(image, ascii_image)
-
-    # Write ASCII art to txt file
-    ascii_txt_path = "ascii_art_output.txt"
-    with open(ascii_txt_path, "w") as f:
-        f.write(ascii_art)
-
-    return ascii_art, ssim_score, ascii_txt_path
-
-
-def gradio_interface(image):
-    """
-    This function generates the interface outputs for a given input image.
-    The outputs include the formatted SSIM, ASCII art, and ASCII art txt file path.
-    """
-    ascii_art, ssim_score, ascii_txt_path = process_image(image)
-    ssim_formatted = f'<div style="font-size: 20px;">The structural similarity index measure is {ssim_score:.3f}</div>'
-    # Wrap the ASCII art in HTML with custom font size
-    styled_ascii_art = f'<pre style="font-family: monospace; font-size: 7px;">{ascii_art}</pre>'
-
-    return styled_ascii_art, ssim_formatted, ascii_txt_path
-
-
-def main():
-    # Setup Gradio interface
-    with gr.Blocks() as interface:
-        # Add title and description
-        gr.Markdown("<h1 style='font-size: 40px;'>Image to ASCII Art Converter</h1>")
-        gr.Markdown("<p style='font-size: 18px;'>Upload an image, and this tool will generate an ASCII art version of the image. It also calculates the Structural Similarity Index (SSIM) to evaluate the quality.</p>")
-
-        # Image input and ASCII art + SSIM score output
-        image_input = gr.Image(label="Upload an image")
-        ascii_art_output = gr.HTML()
-        ssim_score_output = gr.HTML()
-
-        # File output for downloading, initially hidden
-        ascii_file_output = gr.File(label="Download ASCII Art")
-
-        # Update interface when image is uploaded
-        image_input.change(fn=gradio_interface,
-                           inputs=image_input,
-                           outputs=[ascii_art_output, ssim_score_output, ascii_file_output])
-
-    # Launch interface
-    interface.launch()
-
-
-if __name__ == "__main__":
+"""
+CS5330 Fall 2024
+Author: Calvin Lo
+Lab 1: ASCII Art
+
+This program generates the ASCII art representation of
+an input image and compares the result to the original image
+using SSIM.
+"""
+
+import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont, ImageFilter, ImageEnhance
+from skimage.metrics import structural_similarity as ssim
+
+# ASCII characters to map pixel intensity
+ASCII_CHARS = [
+    ' ', '.', ',', ':', ';', 'i', 'l', '!', 'I', '1', 't', 'o', 'r', 'x', 'z', 'v', 
+    'u', 'n', 'm', 'w', 'Q', 'B', 'N', 'M', '@'
+]
+
+
+def preprocess_image(image, new_width=150):
+    """
+    This function performs the following operations on the input image:
+    -enhance contrast
+    -perform edge detection (Sobel)
+    -denoise edges
+    -enhance contrast
+    -resize image
+    """
+    # Open the image and convert to grayscale
+    image = Image.fromarray(image)
+    image = image.convert("L")
+
+    # Contrast limited adaptive histogram equalization
+    opencv_image = np.array(image)
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    equalized_image = clahe.apply(opencv_image)
+
+    # Apply Sobel edge detection
+    sobelx = cv2.Sobel(equalized_image, cv2.CV_64F, 1, 0, ksize=5)
+    sobely = cv2.Sobel(equalized_image, cv2.CV_64F, 0, 1, ksize=5)
+    edges = np.hypot(sobelx, sobely)  # Combine gradients
+
+    # Normalize the result to fit into the 0-255 grayscale range
+    edges = np.uint8(255 * edges / np.max(edges))
+
+    # Denoise the edges using Non-Local Means Denoising
+    edges = cv2.fastNlMeansDenoising(edges, None, h=30, templateWindowSize=7, searchWindowSize=21)
+
+    # Improve contrast of edges
+    edges_image = Image.fromarray(edges)
+    enhancer = ImageEnhance.Contrast(edges_image)
+    enhanced_edges = enhancer.enhance(1.5)
+    enhanced_edges = np.array(enhanced_edges)
+
+    # Resize the image for ASCII aspect ratio
+    width, height = image.size
+    aspect_ratio = height / width * 0.45  # Adjusting for ASCII aspect ratio
+    new_height = int(aspect_ratio * new_width)
+    resized_image = Image.fromarray(enhanced_edges).resize((new_width, new_height))
+
+    return resized_image
+
+
+def image_to_ascii(image):
+    """
+    This function generates an ASCII representation of images.
+    """
+    pixels = np.array(image)
+    ascii_str = ""
+    for row in pixels:
+        for pixel in row:
+            # Mapping pixel intensity to ASCII
+            ascii_str += ASCII_CHARS[pixel // 10]
+        ascii_str += "\n"
+    return ascii_str
+
+
+def ascii_to_image(ascii_art, font_size=12, output_file='ascii_art.png'):
+    """"
+    This function converts ASCII text art to a png image.
+    """
+
+    # Specify font
+    font_path = "DejaVuSansMono.ttf"
+    font = ImageFont.truetype(font_path, font_size)
+
+    # Get the required image dimensions
+    lines = ascii_art.splitlines()
+    max_width = max(font.getbbox(line)[2] for line in lines)  # Get max width
+    img_height = len(lines) * font_size
+
+    # Generate image with white background
+    image = Image.new("RGB", (max_width, img_height), "white")
+    draw = ImageDraw.Draw(image)
+
+    # Draw each character from the ASCII text on the image
+    for y, line in enumerate(lines):
+        draw.text((0, y * font_size), line, fill="black", font=font)
+
+    image.save(output_file)
+    return image
+
+
+def compare_ssim(image1, image2):
+    """
+    This function generates the structural similarity index
+    measure for the two input images.
+    """
+    # Convert original color image to grayscale
+    image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
+    
+    # Convert ASCII image to grayscale and cv2 format
+    image2 = np.array(image2.convert("L"))
+
+    # Resize so both images have the same dimensions.
+    image2 = cv2.resize(image2, (image1.shape[1], image1.shape[0]))
+
+    score, _ = ssim(image1, image2, full=True)
+    return score
+
+
+def process_image(image):
+    """
+    This function takes an image and generates:
+        -ASCII art as a string
+        -ASCII text file
+        -Compute SSIM for the ASCII art image and the original image
+    """
+    # Get image edges and convert to ASCII
+    edges = preprocess_image(image)
+    ascii_art = image_to_ascii(edges)
+
+    # Convert ASCII art back to an image for SSIM calculation
+    ascii_image = ascii_to_image(ascii_art)
+
+    # Calculate SSIM
+    ssim_score = compare_ssim(image, ascii_image)
+
+    # Write ASCII art to txt file
+    ascii_txt_path = "ascii_art_output.txt"
+    with open(ascii_txt_path, "w") as f:
+        f.write(ascii_art)
+
+    return ascii_art, ssim_score, ascii_txt_path
+
+
+def gradio_interface(image):
+    """
+    This function generates the interface outputs for a given input image.
+    The outputs include the formatted SSIM, ASCII art, and ASCII art txt file path.
+    """
+    ascii_art, ssim_score, ascii_txt_path = process_image(image)
+    ssim_formatted = f'<div style="font-size: 20px;">The structural similarity index measure is {ssim_score:.3f}</div>'
+    # Wrap the ASCII art in HTML with custom font size
+    styled_ascii_art = f'<pre style="font-family: monospace; font-size: 7px;">{ascii_art}</pre>'
+    
+    return styled_ascii_art, ssim_formatted, ascii_txt_path
+
+
+def main():
+    # Setup Gradio interface
+    with gr.Blocks() as interface:
+        # Add title and description
+        gr.Markdown("<h1 style='font-size: 40px;'>Image to ASCII Art Converter</h1>")
+        gr.Markdown("<p style='font-size: 18px;'>Upload an image, and this tool will generate an ASCII art version of the image. It also calculates the Structural Similarity Index (SSIM) to evaluate the quality.</p>")
+
+        # Image input and ASCII art + SSIM score output
+        image_input = gr.Image(label="Upload an image")
+        ascii_art_output = gr.HTML()
+        ssim_score_output = gr.HTML()
+
+        # File output for downloading, initially hidden
+        ascii_file_output = gr.File(label="Download ASCII Art")
+
+        # Update interface when image is uploaded
+        image_input.change(fn=gradio_interface,
+                           inputs=image_input,
+                           outputs=[ascii_art_output, ssim_score_output, ascii_file_output])
+
+    # Launch interface
+    interface.launch()
+
+
+if __name__ == "__main__":
     main()