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import torch
from transformers import pipeline, AutoImageProcessor, SegformerForSemanticSegmentation
from typing import List
from PIL import Image, ImageDraw, ImageFont, ImageChops, ImageMorph
import numpy as np
import datasets
def find_center_of_non_black_pixels(image):
# Get image dimensions
width, height = image.size
# Iterate over the pixels to find the center of the non-black pixels
total_x = 0
total_y = 0
num_non_black_pixels = 0
top, left, bottom, right = height, width, 0, 0
for y in range(height):
for x in range(width):
pixel = image.getpixel((x, y))
if pixel != (255, 255, 255): # Non-black pixel
total_x += x
total_y += y
num_non_black_pixels += 1
top = min(top, y)
left = min(left, x)
bottom = max(bottom, y)
right = max(right, x)
bbox_width = right - left
bbox_height = bottom - top
bbox_size = max(bbox_height, bbox_width)
# Calculate the center of the non-black pixels
if num_non_black_pixels == 0:
return None # No non-black pixels found
center_x = total_x // num_non_black_pixels
center_y = total_y // num_non_black_pixels
return (center_x, center_y), bbox_size
def create_centered_image(image, center, bbox_size):
# Get image dimensions
width, height = image.size
# Calculate the offset to center the non-black pixels in the new image
offset_x = bbox_size // 2 - center[0]
offset_y = bbox_size // 2 - center[1]
# Create a new image with the same size as the original image
new_image = Image.new("RGB", (bbox_size, bbox_size), color=(255, 255, 255))
# Paste the non-black pixels onto the new image
new_image.paste(image, (offset_x, offset_y))
return new_image
def ade_palette():
"""ADE20K palette that maps each class to RGB values."""
return [
[180, 120, 20],
[180, 120, 120],
[6, 230, 230],
[80, 50, 50],
[4, 200, 3],
[120, 120, 80],
[140, 140, 140],
[204, 5, 255],
[230, 230, 230],
[4, 250, 7],
[224, 5, 255],
[235, 255, 7],
[150, 5, 61],
[120, 120, 70],
[8, 255, 51],
[255, 6, 82],
[143, 255, 140],
[204, 255, 4],
[255, 51, 7],
[204, 70, 3],
[0, 102, 200],
[61, 230, 250],
[255, 6, 51],
[11, 102, 255],
[255, 7, 71],
[255, 9, 224],
[9, 7, 230],
[220, 220, 220],
[255, 9, 92],
[112, 9, 255],
[8, 255, 214],
[7, 255, 224],
[255, 184, 6],
[10, 255, 71],
[255, 41, 10],
[7, 255, 255],
[224, 255, 8],
[102, 8, 255],
[255, 61, 6],
[255, 194, 7],
[255, 122, 8],
[0, 255, 20],
[255, 8, 41],
[255, 5, 153],
[6, 51, 255],
[235, 12, 255],
[160, 150, 20],
[0, 163, 255],
[140, 140, 140],
[250, 10, 15],
[20, 255, 0],
[31, 255, 0],
[255, 31, 0],
[255, 224, 0],
[153, 255, 0],
[0, 0, 255],
[255, 71, 0],
[0, 235, 255],
[0, 173, 255],
[31, 0, 255],
[11, 200, 200],
[255, 82, 0],
[0, 255, 245],
[0, 61, 255],
[0, 255, 112],
[0, 255, 133],
[255, 0, 0],
[255, 163, 0],
[255, 102, 0],
[194, 255, 0],
[0, 143, 255],
[51, 255, 0],
[0, 82, 255],
[0, 255, 41],
[0, 255, 173],
[10, 0, 255],
[173, 255, 0],
[0, 255, 153],
[255, 92, 0],
[255, 0, 255],
[255, 0, 245],
[255, 0, 102],
[255, 173, 0],
[255, 0, 20],
[255, 184, 184],
[0, 31, 255],
[0, 255, 61],
[0, 71, 255],
[255, 0, 204],
[0, 255, 194],
[0, 255, 82],
[0, 10, 255],
[0, 112, 255],
[51, 0, 255],
[0, 194, 255],
[0, 122, 255],
[0, 255, 163],
[255, 153, 0],
[0, 255, 10],
[255, 112, 0],
[143, 255, 0],
[82, 0, 255],
[163, 255, 0],
[255, 235, 0],
[8, 184, 170],
[133, 0, 255],
[0, 255, 92],
[184, 0, 255],
[255, 0, 31],
[0, 184, 255],
[0, 214, 255],
[255, 0, 112],
[92, 255, 0],
[0, 224, 255],
[112, 224, 255],
[70, 184, 160],
[163, 0, 255],
[153, 0, 255],
[71, 255, 0],
[255, 0, 163],
[255, 204, 0],
[255, 0, 143],
[0, 255, 235],
[133, 255, 0],
[255, 0, 235],
[245, 0, 255],
[255, 0, 122],
[255, 245, 0],
[10, 190, 212],
[214, 255, 0],
[0, 204, 255],
[20, 0, 255],
[255, 255, 0],
[0, 153, 255],
[0, 41, 255],
[0, 255, 204],
[41, 0, 255],
[41, 255, 0],
[173, 0, 255],
[0, 245, 255],
[71, 0, 255],
[122, 0, 255],
[0, 255, 184],
[0, 92, 255],
[184, 255, 0],
[0, 133, 255],
[255, 214, 0],
[25, 194, 194],
[102, 255, 0],
[92, 0, 255],
]
def label_to_color_image(label, colormap):
if label.ndim != 2:
raise ValueError("Expect 2-D input label")
if np.max(label) >= len(colormap):
raise ValueError("label value too large.")
return colormap[label]
labels_list = []
with open(r'labels.txt', 'r') as fp:
for line in fp:
labels_list.append(line[:-1])
colormap = np.asarray(ade_palette())
LABEL_NAMES = np.asarray(labels_list)
LABEL_TO_INDEX = {label: i for i, label in enumerate(labels_list)}
FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP, colormap)
FONT = ImageFont.truetype("Arial.ttf", 1000)
def lift_black_value(image, lift_amount):
"""
Increase the black values of an image by a specified amount.
Parameters:
image (PIL.Image): The image to adjust.
lift_amount (int): The amount to increase the brightness of the darker pixels.
Returns:
PIL.Image: The adjusted image with lifted black values.
"""
# Ensure that we don't go out of the 0-255 range for any pixel value
def adjust_value(value):
return min(255, max(0, value + lift_amount))
# Apply the point function to each channel
return image.point(adjust_value)
torch.set_grad_enabled(False)
DEVICE = 'cuda' if torch.cuda.is_available() else "cpu"
# MIN_AREA_THRESHOLD = 0.01
pipe = pipeline("image-segmentation", model="nvidia/segformer-b5-finetuned-ade-640-640")
def segmentation_inference(
image_rgb_pil: Image.Image,
savepath: str
):
outputs = pipe(image_rgb_pil, points_per_batch=32)
for i, prediction in enumerate(outputs):
label = prediction['label']
if (label == "floor") | (label == "wall") | (label == "ceiling"):
mask = prediction['mask']
## Save mask
label_savepath = savepath + label + str(i) + '.png'
fill_image = Image.new("RGB", image_rgb_pil.size, color=(255,255,255))
cutout_image = Image.composite(image_rgb_pil, fill_image, mask)
# Crop mask
center, bbox_size = find_center_of_non_black_pixels(cutout_image)
if center is not None:
centered_image = create_centered_image(cutout_image, center, bbox_size)
centered_image.save(label_savepath)
## Inspect masks
# inverted_mask = ImageChops.invert(mask)
# mask_adjusted = lift_black_value(inverted_mask, 100)
# color_index = LABEL_TO_INDEX[label]
# color = tuple(FULL_COLOR_MAP[color_index][0])
# fill_image = Image.new("RGB", image_rgb_pil.size, color=color)
# image_rgb_pil = Image.composite(image_rgb_pil, fill_image, mask_adjusted)
# Display the final image
# image_rgb_pil.show()
def online_segmentation_inference(
image_rgb_pil: Image.Image
):
outputs = pipe(image_rgb_pil, points_per_batch=32)
# Create an image dictionary
image_dict = {"image": [], "label":[]}
for i, prediction in enumerate(outputs):
label = prediction['label']
if (label == "floor") | (label == "wall") | (label == "ceiling"):
mask = prediction['mask']
fill_image = Image.new("RGB", image_rgb_pil.size, color=(255,255,255))
cutout_image = Image.composite(image_rgb_pil, fill_image, mask)
# Crop mask
center, bbox_size = find_center_of_non_black_pixels(cutout_image)
if center is not None:
centered_image = create_centered_image(cutout_image, center, bbox_size)
# Add image to image dictionary
image_dict["image"].append(centered_image)
image_dict["label"].append(label)
segmented_ds = datasets.Dataset.from_dict(image_dict).cast_column("image", datasets.Image())
return segmented_ds
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