entklei.py

from io import BytesIO
import io
import random

import requests
import string
import time
from PIL import Image, ImageFilter
import numpy as np
import torch
from dw_pose.main import dwpose
from scipy.ndimage import binary_dilation
from transformers import ViTFeatureExtractor, ViTForImageClassification
import torch.nn.functional as F
import transformers
from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, DDIMScheduler
import os
import pydash as _
import boto3

is_production = True

age_detection_model = ViTForImageClassification.from_pretrained(
    'nateraw/vit-age-classifier')
age_detection_transforms = ViTFeatureExtractor.from_pretrained(
    'nateraw/vit-age-classifier')

REPLICATE_API_KEY = ""

S3_REGION = "fra1"
S3_ACCESS_ID = "0RN7BZXS59HYSBD3VB79"
S3_ACCESS_SECRET = "hfSPgBlWl5jsGHa2xuByVkSpancgVeA2CVQf2EMp"
S3_ENDPOINT_URL = "https://s3.solarcom.ch"
S3_BUCKET_NAME = "pissnelke"

s3_session = boto3.session.Session()
s3 = s3_session.client(
    service_name="s3",
    region_name=S3_REGION,
    aws_access_key_id=S3_ACCESS_ID,
    aws_secret_access_key=S3_ACCESS_SECRET,
    endpoint_url=S3_ENDPOINT_URL,
)

def find_bounding_box(pil_image):
    image_np = np.array(pil_image.convert('L'))
    white_pixels = np.argwhere(image_np == 255)
    x_min, y_min = np.min(white_pixels, axis=0)
    x_max, y_max = np.max(white_pixels, axis=0)
    return (y_min, x_min), (y_max, x_max)

def getSizeFromCoords(top_left, bottom_right):
    """
    Calculate the width and height of a bounding box.

    Parameters:
    bounding_box (tuple): A tuple containing two tuples, 
                          the first is the top-left corner (x_min, y_min) 
                          and the second is the bottom-right corner (x_max, y_max).

    Returns:
    tuple: A tuple containing the width and height of the bounding box.
    """
    (x_min, y_min), (x_max, y_max) = top_left, bottom_right
    width = x_max - x_min
    height = y_max - y_min
    return {"width": width, "height": height}


def crop_to_coords(coords1, coords2, pil_image):
    top_left_x, top_left_y = coords1
    bottom_right_x, bottom_right_y = coords2
    cropped_image = pil_image.crop(
        (top_left_x, top_left_y, bottom_right_x, bottom_right_y))
    return cropped_image


def paste_image_at_coords(dest_image, src_image, coords):
    dest_image.paste(src_image, coords)
    return dest_image


def resize(width, height, maxStretch):
    new_width = width * (maxStretch / max(width, height))
    new_height = height * (maxStretch / max(width, height))

    return {"width": new_width, "height": new_height}


def get_is_underage(input_pil):
    input_pil = input_pil.convert("RGB")

    inputs = age_detection_transforms(input_pil, return_tensors='pt')
    output = age_detection_model(**inputs)

    # Apply softmax to the logits to get probabilities
    probabilities = F.softmax(output['logits'], dim=1)

    # Get the class with the highest probability
    predicted_class = probabilities.argmax().item()

    map = {
        "0": "0-2",
        "1": "3-9",
        "2": "10-19",
        "3": "20-29",
        "4": "30-39",
        "5": "40-49",
        "6": "50-59",
        "7": "60-69",
        "8": "more than 70"
    }

    print("Age:", map[str(predicted_class)], "years old")

    if predicted_class < 3:
        return True

    return False


controlnet = ControlNetModel.from_pretrained(
    "lllyasviel/control_v11p_sd15_openpose", torch_dtype=torch.float16
)
base_pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
    "redstonehero/epicrealism_pureevolutionv5-inpainting", controlnet=controlnet, torch_dtype=torch.float16
)
base_pipe.scheduler = DDIMScheduler.from_config(base_pipe.scheduler.config)
base_pipe = base_pipe.to("cuda")
base_pipe.enable_model_cpu_offload()
base_pipe.safety_checker = None
#base_pipe.enable_xformers_memory_efficient_attention()

pipe_with_tit_slider = _.clone_deep(base_pipe)

pipe_with_tit_slider.load_lora_weights(os.path.join("/repository" if is_production else ".", "models", "breastsizeslideroffset.safetensors"), weight_name="breastsizeslideroffset.safetensors", adapter_name="breastsizeslideroffset")

# pipe_with_small_tits = _.clone_deep(pipe_with_tit_slider)
# pipe_with_small_tits.set_adapters("breastsizeslideroffset", adapter_weights=[-0.8])

# pipe_with_medium_tits = _.clone_deep(base_pipe)

# pipe_with_big_tits = _.clone_deep(pipe_with_tit_slider)
# pipe_with_big_tits.set_adapters("breastsizeslideroffset", adapter_weights=[0.7])

def get_nude(original_pil, original_max_size=2000, generate_max_size=768, positive_prompt="nude girl, pussy, tits", negative_prompt="ugly", steps=20, cfg_scale=7, get_mask_function=None, with_small_tits=False, with_big_tits=False):
    try:
        exif_data = original_pil._getexif()
        orientation_tag = 274  # The Exif tag for orientation
        if exif_data is not None and orientation_tag in exif_data:
            orientation = exif_data[orientation_tag]
            if orientation == 3:
                original_pil = original_pil.rotate(180, expand=True)
            elif orientation == 6:
                original_pil = original_pil.rotate(270, expand=True)
            elif orientation == 8:
                original_pil = original_pil.rotate(90, expand=True)
    except (AttributeError, KeyError, IndexError):
        # In case the Exif data is missing or corrupt, continue without rotating
        pass

    original_max_size = original_max_size or 2000
    generate_max_size = generate_max_size or 768
    positive_prompt = positive_prompt or "nude girl, pussy, tits"
    negative_prompt = negative_prompt or "ugly"
    steps = steps or 20
    cfg_scale = cfg_scale or 7

    small_original_image = original_pil.copy()

    small_original_image = small_original_image.convert("RGB")  # new

    small_original_image.thumbnail((original_max_size, original_max_size))

    start_time = time.time()
    is_underage = get_is_underage(small_original_image)
    print("get_is_underage", time.time() - start_time, "seconds")

    if is_underage:
        raise Exception("Underage")

    person_mask_pil_expanded = get_mask_function(
        small_original_image, "person", expand_by=20)

    person_coords1, person_coords2 = find_bounding_box(
        person_mask_pil_expanded)

    size = getSizeFromCoords(person_coords1, person_coords2)

    there_height = size["height"]
    there_width = size["width"]

    # Determine if the image is portrait or landscape
    if there_height >= there_width:
        # Portrait
        there_height_to_width = there_width / there_height
        then_height = 768
        then_atleast_width = 768 * there_height_to_width
    else:
        # Landscape
        there_width_to_height = there_height / there_width
        then_width = 768
        then_atleast_height = 768 * there_width_to_height

    # Ensure dimensions are multiples of 8
    if there_height >= there_width:
        then_width = then_atleast_width - (then_atleast_width % 8) + 8
        crop_width = there_height * then_width / then_height
        crop_height = there_height
    else:
        then_height = then_atleast_height - (then_atleast_height % 8) + 8
        crop_height = there_width * then_height / then_width
        crop_width = there_width

    # Calculate cropping coordinates
    crop_coord_1 = (
        person_coords1[0] - (crop_width - size["width"]), person_coords1[1])
    crop_coord_2 = person_coords2

    if (crop_coord_1[0] < 0):
        crop_coord_1 = person_coords1
        crop_coord_2 = (
            person_coords2[0] + (crop_width - size["width"]), person_coords2[1])

    person_cropped_pil = crop_to_coords(
        crop_coord_1, crop_coord_2, small_original_image)

    expanded_mask_image = get_mask_function(
        person_cropped_pil, "bra . blouse . skirt . dress", expand_by=10)

    person_cropped_width, person_cropped_height = person_cropped_pil.size
    new_size = resize(crop_width, crop_height, generate_max_size)

    dwpose_pil = dwpose(person_cropped_pil, 512)

    expanded_mask_image_width, expanded_mask_image_height = expanded_mask_image.size

    dwpose_pil_resized = dwpose_pil.resize(
        (int(expanded_mask_image_width), int(expanded_mask_image_height)))

    end_result_images = pipe_with_tit_slider(
        positive_prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=steps,
        guidance_scale=cfg_scale,
        eta=1.0,
        image=person_cropped_pil,
        mask_image=expanded_mask_image,
        control_image=dwpose_pil_resized,
        num_images_per_prompt=2,
        height=round(new_size["height"]),
        width=round(new_size["width"]),
        cross_attention_kwargs={"scale": -0.8} if with_small_tits else {"scale": 0.7} if with_big_tits else { "scale": 0 }
    ).images

    # Function to create a mask for blurring edges
    def create_blurred_edge_mask(image, blur_radius):
        mask = Image.new("L", image.size, 0)
        mask.paste(255, [blur_radius, blur_radius, mask.width -
                   blur_radius, mask.height - blur_radius])
        return mask.filter(ImageFilter.GaussianBlur(blur_radius))

    output_pils = []

    # Your existing code
    for image in end_result_images:
        fit_into_group_image = image.resize(
            (person_cropped_width, person_cropped_height))

        # Create a mask for the resized image with blurred edges
        blur_radius = 10  # You can adjust the radius as needed
        mask = create_blurred_edge_mask(fit_into_group_image, blur_radius)

        # Paste using the mask for a smoother transition
        small_original_image.paste(
            fit_into_group_image, (int(crop_coord_1[0]), crop_coord_1[1]), mask)

        output_pils.append(small_original_image)

    return output_pils

# get all files in ./dataset and get nude