add SII evaluation metric

app.py

@@ -2,6 +2,8 @@ from PIL import Image, ImageDraw, ImageOps, ImageFilter, ImageFont
 import numpy as np
 import math
 import gradio as gr
+from skimage.metrics import structural_similarity as ssim
+import cv2
 
 ##### CONSTANTS #####
 # ASCII characters used to represent image pixels, reversed for better contrast in mapping
@@ -14,9 +16,6 @@ CHAR_LEN = len(CHAR_ARRAY)
 # Grayscale level for each ASCII character, determining how many shades of gray each character represents
 GRAYSCALE_LEVEL = CHAR_LEN / 256
 
-# Scaling factor to resize the image
-SCALE = 0.2
-
 # Target number of characters per row
 TARGET_WIDTH = 200
 
@@ -24,7 +23,7 @@ TARGET_WIDTH = 200
 CHAR_W = 6
 CHAR_H = 14
 
-##### FUNCTIONS #####
+##### ASCII ART GENERATION #####
 def getChar(inputInt, gamma=1.8):
     """Map a grayscale pixel value to an ASCII character with gamma correction applied."""
     # Adjust the input pixel intensity using gamma correction for perceptual brightness adjustment
@@ -33,25 +32,11 @@ def getChar(inputInt, gamma=1.8):
     return CHAR_ARRAY[math.floor(inputInt * GRAYSCALE_LEVEL)]
 
 
-# def load_and_preprocess_image(image):
-#     """Resize and preprocess the input image, adjusting contrast and blurring for better ASCII conversion."""
-#     width, height = image.size
-#     # Resize image, adjusting aspect ratio to fit ASCII character dimensions
-#     im = image.resize((int(SCALE * width), int(SCALE * height * (CHAR_W / CHAR_H))))
-
-#     # Enhance contrast to bring out more detail in the ASCII representation
-#     im = ImageOps.equalize(im, mask=None)
-
-#     # Apply a slight blur to reduce noise and simplify pixel values
-#     im = im.filter(ImageFilter.GaussianBlur(radius=0.5))
-
-#     return im
-
 def load_and_preprocess_image(image):
     """Resize and preprocess the input image, adjusting contrast and blurring for better ASCII conversion."""
     width, height = image.size
     
-    # Calculate the scaling factor to match the target character width (300 characters per row)
+    # Calculate the scaling factor to match the target character width
     scale_factor = TARGET_WIDTH / width
     
     # Resize the image, maintaining the aspect ratio considering the character dimensions
@@ -88,15 +73,14 @@ def create_ascii_art(im):
     
     return ascii_art
 
-
 def draw_ascii_image(ascii_art_string, char_width, char_height, font_size):
     """Draw the ASCII art string onto an image."""
     # Split the ASCII art string into lines
     lines = ascii_art_string.split('\n')
     
     # Determine the dimensions of the image based on the number of characters
-    width = max(len(line) for line in lines)  # Maximum line width
-    height = len(lines)  # Number of lines (height)
+    width = max(len(line) for line in lines)
+    height = len(lines) 
     
     # Create a blank white image based on the ASCII art size and font size
     img_width = width * char_width
@@ -114,50 +98,90 @@ def draw_ascii_image(ascii_art_string, char_width, char_height, font_size):
     return ascii_image
 
 
+##### EVALUATION #####
+def ascii_to_image(ascii_art, width, height):
+    """Convert ASCII art back to an image by mapping characters to grayscale values."""
+    char_to_gray = {c: int((i / CHAR_LEN) * 255) for i, c in enumerate(CHAR_ARRAY)}
+    ascii_lines = ascii_art.split('\n')
+    
+    # Create an empty numpy array to store the pixel values
+    ascii_image = np.zeros((height, width), dtype=np.uint8)
+    
+    for i, line in enumerate(ascii_lines):
+        if i >= height:
+            break
+        for j, char in enumerate(line):
+            if j >= width:
+                break
+            ascii_image[i, j] = char_to_gray.get(char, 255)  # Default to white if character not in mapping
+
+    return Image.fromarray(ascii_image)
+
+def calculate_ssi(original_image, ascii_art_image):
+    """Calculate Structural Similarity Index (SSI) between the original and reconstructed images."""
+    original_image = original_image.convert("L")
+    
+    # Resize the original image to match the dimensions of the ASCII art image
+    original_image_resized = original_image.resize(ascii_art_image.size)
+    
+    # Convert both images to NumPy arrays
+    original_array = np.array(original_image_resized)
+    ascii_array = np.array(ascii_art_image)
+
+    # Calculate SSI
+    ssi_value, _ = ssim(original_array, ascii_array, full=True)
+    return ssi_value
+
+
+##### MAIN FUNCTION FOR GRADIO INTERFACE #####
 def process_image(image):
     """Process the input image to generate both an ASCII art image and a downloadable text file."""
-    # Resize and preprocess the image
     resized_image = load_and_preprocess_image(image)
-    # Generate the ASCII art as text
     ascii_art = create_ascii_art(resized_image)
 
-    # Create an image from the ASCII art characters
     output_image = draw_ascii_image(ascii_art, char_width=CHAR_W, char_height=CHAR_H, font_size=10)
-
+    
+    # Convert the ASCII art back into an image for SSI comparison
+    ascii_art_image = ascii_to_image(ascii_art, resized_image.width, resized_image.height)
+    
+    # Calculate SSI between the original and ASCII art image
+    ssi_value = calculate_ssi(image, ascii_art_image)
+    
     # Save the ASCII art as a text file
     ascii_txt_path = "ascii_art.txt"
     with open(ascii_txt_path, "w") as text_file:
         text_file.write(ascii_art)
 
-    return output_image, ascii_txt_path
+    print(f"Structural Similarity Index (SSI): {ssi_value}")
+    
+    return output_image, ascii_txt_path, ssi_value
 
 
 ##### GRADIO INTERFACE #####
 def gradio_interface(image):
-    """Gradio interface function to handle user input and return the ASCII art image and text file."""
-    ascii_image, txt_file = process_image(image)
-    return ascii_image, txt_file
+    ascii_image, txt_file, ssi_value = process_image(image)
+    return ascii_image, txt_file, ssi_value
 
-# Set up the Gradio interface
 demo = gr.Interface(
-    fn=gradio_interface, 
+    fn=gradio_interface,
     inputs=gr.Image(type="pil", label="Upload an Image", height=300), 
     outputs=[
         gr.Image(type="pil", label="ASCII Art Image", height=300), 
         gr.File(label="Download ASCII Art Text File", height=50),
+        gr.Textbox(label="SSI Value")
     ],
-    title="ASCII Art Generator",
-    description="Upload an image, and this tool will generate ASCII art and provide a downloadable text file of the result.",
+    title="ASCII Art Generator with SSI Metric",
+    description="Upload an image, generate ASCII art, and calculate the Structural Similarity Index (SSI).",
     allow_flagging="never",
-    examples=[
+        examples=[
         ['images/building.jpg'],
         ['images/cat.webp'],
         ['images/dog.jpg'],
         ['images/people.jpg'],
-        ['images/Northeastern_seal.png'],
+        ['images/cartoon.png'],
         ['images/einstein.jpg'],
     ],
-
 )
 
+
 demo.launch()

requirements.txt

@@ -1,3 +1,5 @@
 gradio==4.44.0
 pillow==9.3.0
 numpy==1.23.4
+scikit-image==0.19.3
+opencv-python==4.9.0.80