File size: 9,850 Bytes
6ed23ea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 |
"""
Created Date: 09-26-2024
Updated Date: -
Author: Chaitanya Chadha
"""
import streamlit as st
from transformers import AutoImageProcessor, ResNetForImageClassification
import torch
from PIL import Image, ImageDraw, ImageFont
import math
import os
import io
from datetime import datetime
# Set page configuration at the very beginning
st.set_page_config(page_title="π¨ Colored ASCII Art Generator", layout="wide")
# Initialize model and processor once to improve performance
@st.cache_resource
def load_model_and_processor():
processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
return processor, model
processor, model = load_model_and_processor()
def Classify_Image(image):
"""
Classifies the image using a pre-trained ResNet-50 model.
Returns the predicted label.
"""
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
return model.config.id2label[predicted_label]
def resize_image(image, new_width=100):
width, height = image.size
aspect_ratio = height / width
# Adjusting height based on the aspect ratio and a scaling factor
new_height = int(aspect_ratio * new_width * 0.55)
resized_image = image.resize((new_width, new_height))
return resized_image
def grayify(image):
return image.convert("L")
def calculate_image_entropy(image):
# Calculates the entropy of the grayscale image to determine its complexity.
# Higher entropy indicates a more complex image with more details.
histogram = image.histogram()
histogram_length = sum(histogram)
samples_probability = [float(h) / histogram_length for h in histogram if h != 0]
entropy = -sum([p * math.log(p, 2) for p in samples_probability])
return entropy
def select_character_set(entropy, art_gen_choice, classification):
ASCII_CHARS_SETS = {
"standard": [
'@', '%', '#', '*', '+', '=', '-', ':', '.', ' '
],
"detailed": [
'$', '@', 'B', '%', '8', '&', 'W', 'M', '#', '*', 'o', 'a', 'h', 'k', 'b',
'd', 'p', 'q', 'w', 'm', 'Z', 'O', '0', 'Q', 'L', 'C', 'J', 'U', 'Y',
'X', 'z', 'c', 'v', 'u', 'n', 'x', 'r', 'j', 'f', 't', '/', '\\', '|',
'(', ')', '1', '{', '}', '[', ']', '?', '-', '_', '+', '~', '<', '>',
'i', '!', 'l', 'I', ';', ':', ',', '"', '^', '', "'", '.', ' '
],
"simple": [
'#', '*', '+', '=', '-', ':', '.', ' '
],
"custom": [
"!", "~", "@", "#", "$", "%", "Β¨", "&", "*", "(", ")", "_", "+", "-",
"=", "{", "}", "[", "]", "|", "\\", "/", ":", ";", "'", "\"", ",", "<",
".", ">", "?", " " ] + list(set(list(str(classification))))
}
if art_gen_choice.lower() == "custom":
selected_set = "custom"
return ASCII_CHARS_SETS[selected_set]
else:
# Define entropy thresholds (these values can be adjusted based on experimentation)
if entropy < 4.0:
selected_set = "simple"
elif 4.0 <= entropy < 5.5:
selected_set = "standard"
else:
selected_set = "detailed"
return ASCII_CHARS_SETS[selected_set]
def determine_optimal_width(image, max_width=120):
# Determines the optimal width for the ASCII art based on the image's original dimensions.
original_width, original_height = image.size
if original_width > max_width:
return max_width
else:
return original_width
def render_ascii_to_image(ascii_chars, img_width, img_height, font_path="fonts/DejaVuSansMono.ttf", font_size=10):
# Renders the ASCII characters onto an image with their corresponding colors.
if not os.path.isfile(font_path):
st.error(f"Font file not found at {font_path}. Please provide a valid font path.")
return None
# Create a new image with white background
try:
font = ImageFont.truetype(font_path, font_size)
except Exception as e:
st.error(f"Error loading font: {e}")
return None
# Get character dimensions
# Using getbbox which is compatible with Pillow >= 8.0
left, top, right, bottom = font.getbbox('A')
char_width = right - left
char_height = bottom - top
image_width = char_width * img_width
image_height = char_height * img_height
new_image = Image.new("RGB", (image_width, image_height), "white")
draw = ImageDraw.Draw(new_image)
for i, (char, color) in enumerate(ascii_chars):
x = (i % img_width) * char_width
y = (i // img_width) * char_height
draw.text((x, y), char, fill=color, font=font)
return new_image
def pixels_to_colored_ascii(grayscale_image, color_image, chars):
# Maps each pixel to an ASCII character from the selected character set.
# Returns a list of tuples: (character, (R, G, B))
grayscale_pixels = grayscale_image.getdata()
color_pixels = color_image.getdata()
ascii_chars = []
for grayscale_pixel, color_pixel in zip(grayscale_pixels, color_pixels):
# Scale grayscale pixel value to the range of the character set
index = grayscale_pixel * (len(chars) - 1) // 255
ascii_char = chars[index]
# Extract RGB values; ignore alpha if present
if len(color_pixel) == 4:
r, g, b, _ = color_pixel
else:
r, g, b = color_pixel
ascii_chars.append((ascii_char, (r, g, b)))
return ascii_chars
def generate_ascii_art(image, font_path="fonts/DejaVuSansMono.ttf", font_size=12):
# image: PIL Image object
# Classify the image
image_class = Classify_Image(image)
st.write(f"**Image Classification:** {image_class}")
# Convert to grayscale and calculate entropy
grayscale_image = grayify(image)
entropy = calculate_image_entropy(grayscale_image)
st.write(f"**Image Entropy:** {entropy:.2f}")
# Select character set
art_gen_choice = st.session_state.get('art_gen_choice', 'custom')
selected_chars = select_character_set(entropy, art_gen_choice, image_class)
st.write(f"**Selected Character Set:** '{art_gen_choice}' with {len(selected_chars)} characters.")
# Determine optimal width
optimal_width = determine_optimal_width(image)
st.write(f"**Selected Width:** {optimal_width}")
# Resize images
resized_image = resize_image(image, optimal_width)
grayscale_resized_image = grayify(resized_image)
color_resized_image = resized_image.convert("RGB") # Ensure image is in RGB mode
# Convert pixels to ASCII
ascii_chars = pixels_to_colored_ascii(grayscale_resized_image, color_resized_image, selected_chars)
# Create ASCII string
ascii_str = ''.join([char for char, color in ascii_chars])
ascii_lines = [ascii_str[index: index + optimal_width] for index in range(0, len(ascii_str), optimal_width)]
ascii_art = "\n".join(ascii_lines)
return ascii_art, ascii_chars, resized_image.size
def main():
# Title and Description are already set after set_page_config
st.title("π¨ Colored ASCII Art Generator")
st.markdown("""
Upload an image, and this app will convert it into colored ASCII art.
You can view the classification, entropy, and download or copy the ASCII art.
""")
# Sidebar for options
st.sidebar.header("Options")
art_gen_choice = st.sidebar.selectbox(
"Character Set Choice",
options=["custom", "standard", "detailed", "simple"],
help="Select the character set to use for ASCII art generation."
)
st.session_state['art_gen_choice'] = art_gen_choice
# File uploader
uploaded_file = st.file_uploader("Upload an Image", type=["png", "jpg", "jpeg", "bmp", "gif"])
if uploaded_file is not None:
try:
# Read the image
image = Image.open(uploaded_file).convert("RGB")
st.image(image, caption='Uploaded Image', use_column_width=True)
# Generate ASCII Art
with st.spinner("Generating ASCII Art..."):
ascii_art, ascii_chars, resized_size = generate_ascii_art(image)
# Display ASCII Art as Image
ascii_image = render_ascii_to_image(
ascii_chars,
img_width=resized_size[0],
img_height=resized_size[1],
font_path="fonts/DejaVuSansMono.ttf",
font_size=12
)
if ascii_image:
st.image(ascii_image, caption='Colored ASCII Art', use_column_width=True)
# Provide Download Button for ASCII Image
img_byte_arr = io.BytesIO()
ascii_image.save(img_byte_arr, format='PNG')
img_byte_arr = img_byte_arr.getvalue()
st.download_button(
label="π₯ Download ASCII Art Image",
data=img_byte_arr,
file_name=f"ascii_art_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png",
mime="image/png"
)
# Display ASCII Art as Text with Copy Option
st.text_area("ASCII Art", ascii_art, height=300)
# Provide a download button for the ASCII text
st.download_button(
label="π Download ASCII Art Text",
data=ascii_art,
file_name=f"ascii_art_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt",
mime="text/plain"
)
except Exception as e:
st.error(f"An error occurred: {e}")
else:
st.info("Please upload an image to get started.")
if __name__ == "__main__":
main()
|