import string import warnings warnings.filterwarnings('ignore') import subprocess, io, os, sys, time import random # os.environ["XFORMERS_DISABLE_FLASH_ATTN"] = "1" # result = subprocess.run(['pip', 'install', 'xformers'], check=True) from entklei import get_nude from scipy.ndimage import binary_dilation is_production = True install_stuff = True os.environ['CUDA_HOME'] = '/usr/local/cuda-11.7/' if is_production else '/usr/local/cuda-12.1/' run_gradio = False if run_gradio and install_stuff: os.system("pip install gradio==3.50.2") import gradio as gr from loguru import logger os.environ["CUDA_VISIBLE_DEVICES"] = "0" if is_production: os.chdir("/repository") sys.path.insert(0, '/repository') if install_stuff: # result = subprocess.run(['pip', 'install', "-u", 'peft'], check=True) result = subprocess.run(['pip', 'install', '-e', 'GroundingDINO'], check=True) print(f'pip install GroundingDINO = {result}') # result = subprocess.run(['pip', 'list'], check=True) # print(f'pip list = {result}') sys.path.insert(0, '/repository/GroundingDINO' if is_production else "./GroundingDINO") import argparse import copy import numpy as np import torch from PIL import Image, ImageDraw, ImageFont, ImageOps # Grounding DINO import GroundingDINO.groundingdino.datasets.transforms as T from GroundingDINO.groundingdino.models import build_model from GroundingDINO.groundingdino.util import box_ops from GroundingDINO.groundingdino.util.slconfig import SLConfig from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap import cv2 import numpy as np import matplotlib matplotlib.use('AGG') plt = matplotlib.pyplot # import matplotlib.pyplot as plt # <<<<<< AIINFERENCE # >>>>>> AIINFERENCE groundingdino_enable = True sam_enable = True inpainting_enable = True ram_enable = True lama_cleaner_enable = True kosmos_enable = False # qwen_enable = True # from qwen_utils import * # segment anything from segment_anything import build_sam, SamPredictor, SamAutomaticMaskGenerator # diffusers import PIL import requests import torch from io import BytesIO from huggingface_hub import hf_hub_download config_file = 'GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py' ckpt_repo_id = "ShilongLiu/GroundingDINO" ckpt_filenmae = "groundingdino_swint_ogc.pth" sam_checkpoint = './sam_hq_vit_h.pth' output_dir = "outputs" device = 'cpu' sam_device = "cuda" def get_sam_vit_h_4b8939(): url = 'https://huggingface.co/Uminosachi/sam-hq/resolve/main/sam_hq_vit_h.pth' file_path = './sam_hq_vit_h.pth' if not os.path.exists(file_path): logger.info("Downloading sam_vit_h_4b8939.pth...") response = requests.get(url) with open(file_path, 'wb') as f: f.write(response.content) print('Downloaded sam_vit_h_4b8939.pth') logger.info(f"initialize SAM model...") sam_device = "cuda" sd_model = None lama_cleaner_model= None ram_model = None kosmos_model = None kosmos_processor = None get_sam_vit_h_4b8939() sam_model = build_sam(checkpoint=sam_checkpoint).to(sam_device) sam_predictor = SamPredictor(sam_model) sam_mask_generator = SamAutomaticMaskGenerator(sam_model) def load_model_hf(model_config_path, repo_id, filename, device='cpu'): args = SLConfig.fromfile(model_config_path) model = build_model(args) args.device = device cache_file = hf_hub_download(repo_id=repo_id, filename=filename) checkpoint = torch.load(cache_file, map_location=device) log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False) print("Model loaded from {} \n => {}".format(cache_file, log)) _ = model.eval() return model def plot_boxes_to_image(image_pil, tgt): H, W = tgt["size"] boxes = tgt["boxes"] labels = tgt["labels"] assert len(boxes) == len(labels), "boxes and labels must have same length" draw = ImageDraw.Draw(image_pil) mask = Image.new("L", image_pil.size, 0) mask_draw = ImageDraw.Draw(mask) # draw boxes and masks for box, label in zip(boxes, labels): # from 0..1 to 0..W, 0..H box = box * torch.Tensor([W, H, W, H]) # from xywh to xyxy box[:2] -= box[2:] / 2 box[2:] += box[:2] # random color color = tuple(np.random.randint(0, 255, size=3).tolist()) # draw x0, y0, x1, y1 = box x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1) draw.rectangle([x0, y0, x1, y1], outline=color, width=6) # draw.text((x0, y0), str(label), fill=color) font = ImageFont.load_default() if hasattr(font, "getbbox"): bbox = draw.textbbox((x0, y0), str(label), font) else: w, h = draw.textsize(str(label), font) bbox = (x0, y0, w + x0, y0 + h) # bbox = draw.textbbox((x0, y0), str(label)) draw.rectangle(bbox, fill=color) try: font = os.path.join(cv2.__path__[0],'qt','fonts','DejaVuSans.ttf') font_size = 36 new_font = ImageFont.truetype(font, font_size) draw.text((x0+2, y0+2), str(label), font=new_font, fill="white") except Exception as e: pass mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6) return image_pil, mask def load_image(image_path): # # load image if isinstance(image_path, PIL.Image.Image): image_pil = image_path else: image_pil = Image.open(image_path).convert("RGB") # load image transform = T.Compose( [ T.RandomResize([800], max_size=1333), T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ] ) image, _ = transform(image_pil, None) # 3, h, w return image_pil, image def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device="cpu"): caption = caption.lower() caption = caption.strip() if not caption.endswith("."): caption = caption + "." model = model.to(device) image = image.to(device) with torch.no_grad(): outputs = model(image[None], captions=[caption]) logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256) boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4) logits.shape[0] # filter output logits_filt = logits.clone() boxes_filt = boxes.clone() filt_mask = logits_filt.max(dim=1)[0] > box_threshold logits_filt = logits_filt[filt_mask] # num_filt, 256 boxes_filt = boxes_filt[filt_mask] # num_filt, 4 logits_filt.shape[0] # get phrase tokenlizer = model.tokenizer tokenized = tokenlizer(caption) # build pred pred_phrases = [] for logit, box in zip(logits_filt, boxes_filt): pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer) if with_logits: pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})") else: pred_phrases.append(pred_phrase) return boxes_filt, pred_phrases def show_mask(mask, ax, random_color=False): if random_color: color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) else: color = np.array([30/255, 144/255, 255/255, 0.6]) h, w = mask.shape[-2:] mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) ax.imshow(mask_image) def show_box(box, ax, label): x0, y0 = box[0], box[1] w, h = box[2] - box[0], box[3] - box[1] ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) ax.text(x0, y0, label) def xywh_to_xyxy(box, sizeW, sizeH): if isinstance(box, list): box = torch.Tensor(box) box = box * torch.Tensor([sizeW, sizeH, sizeW, sizeH]) box[:2] -= box[2:] / 2 box[2:] += box[:2] box = box.numpy() return box def mask_extend(img, box, extend_pixels=10, useRectangle=True): box[0] = int(box[0]) box[1] = int(box[1]) box[2] = int(box[2]) box[3] = int(box[3]) region = img.crop(tuple(box)) new_width = box[2] - box[0] + 2*extend_pixels new_height = box[3] - box[1] + 2*extend_pixels region_BILINEAR = region.resize((int(new_width), int(new_height))) if useRectangle: region_draw = ImageDraw.Draw(region_BILINEAR) region_draw.rectangle((0, 0, new_width, new_height), fill=(255, 255, 255)) img.paste(region_BILINEAR, (int(box[0]-extend_pixels), int(box[1]-extend_pixels))) return img def mix_masks(imgs): re_img = 1 - np.asarray(imgs[0].convert("1")) for i in range(len(imgs)-1): re_img = np.multiply(re_img, 1 - np.asarray(imgs[i+1].convert("1"))) re_img = 1 - re_img return Image.fromarray(np.uint8(255*re_img)) # visualization def draw_selected_mask(mask, draw): color = (255, 0, 0, 153) nonzero_coords = np.transpose(np.nonzero(mask)) for coord in nonzero_coords: draw.point(coord[::-1], fill=color) def draw_object_mask(mask, draw): color = (0, 0, 255, 153) nonzero_coords = np.transpose(np.nonzero(mask)) for coord in nonzero_coords: draw.point(coord[::-1], fill=color) def create_title_image(word1, word2, word3, width, font_path='./assets/OpenSans-Bold.ttf'): # Define the colors to use for each word color_red = (255, 0, 0) color_black = (0, 0, 0) color_blue = (0, 0, 255) # Define the initial font size and spacing between words font_size = 40 # Create a new image with the specified width and white background image = Image.new('RGB', (width, 60), (255, 255, 255)) try: # Load the specified font font = ImageFont.truetype(font_path, font_size) # Keep increasing the font size until all words fit within the desired width while True: # Create a draw object for the image draw = ImageDraw.Draw(image) word_spacing = font_size / 2 # Draw each word in the appropriate color x_offset = word_spacing draw.text((x_offset, 0), word1, color_red, font=font) x_offset += font.getsize(word1)[0] + word_spacing draw.text((x_offset, 0), word2, color_black, font=font) x_offset += font.getsize(word2)[0] + word_spacing draw.text((x_offset, 0), word3, color_blue, font=font) word_sizes = [font.getsize(word) for word in [word1, word2, word3]] total_width = sum([size[0] for size in word_sizes]) + word_spacing * 3 # Stop increasing font size if the image is within the desired width if total_width <= width: break # Increase font size and reset the draw object font_size -= 1 image = Image.new('RGB', (width, 50), (255, 255, 255)) font = ImageFont.truetype(font_path, font_size) draw = None except Exception as e: pass return image def concatenate_images_vertical(image1, image2): # Get the dimensions of the two images width1, height1 = image1.size width2, height2 = image2.size # Create a new image with the combined height and the maximum width new_image = Image.new('RGBA', (max(width1, width2), height1 + height2)) # Paste the first image at the top of the new image new_image.paste(image1, (0, 0)) # Paste the second image below the first image new_image.paste(image2, (0, height1)) return new_image def relate_anything(input_image, k): logger.info(f'relate_anything_1_{input_image.size}_') w, h = input_image.size max_edge = 1500 if w > max_edge or h > max_edge: ratio = max(w, h) / max_edge new_size = (int(w / ratio), int(h / ratio)) input_image.thumbnail(new_size) logger.info(f'relate_anything_2_') # load image pil_image = input_image.convert('RGBA') image = np.array(input_image) sam_masks = sam_mask_generator.generate(image) filtered_masks = sort_and_deduplicate(sam_masks) logger.info(f'relate_anything_3_') feat_list = [] for fm in filtered_masks: feat = torch.Tensor(fm['feat']).unsqueeze(0).unsqueeze(0).to(device) feat_list.append(feat) feat = torch.cat(feat_list, dim=1).to(device) matrix_output, rel_triplets = ram_model.predict(feat) logger.info(f'relate_anything_4_') pil_image_list = [] for i, rel in enumerate(rel_triplets[:k]): s,o,r = int(rel[0]),int(rel[1]),int(rel[2]) relation = relation_classes[r] mask_image = Image.new('RGBA', pil_image.size, color=(0, 0, 0, 0)) mask_draw = ImageDraw.Draw(mask_image) draw_selected_mask(filtered_masks[s]['segmentation'], mask_draw) draw_object_mask(filtered_masks[o]['segmentation'], mask_draw) current_pil_image = pil_image.copy() current_pil_image.alpha_composite(mask_image) title_image = create_title_image('Red', relation, 'Blue', current_pil_image.size[0]) concate_pil_image = concatenate_images_vertical(current_pil_image, title_image) pil_image_list.append(concate_pil_image) logger.info(f'relate_anything_5_{len(pil_image_list)}') return pil_image_list mask_source_draw = "draw a mask on input image" mask_source_segment = "type what to detect below" def get_time_cost(run_task_time, time_cost_str): now_time = int(time.time()*1000) if run_task_time == 0: time_cost_str = 'start' else: if time_cost_str != '': time_cost_str += f'-->' time_cost_str += f'{now_time - run_task_time}' run_task_time = now_time return run_task_time, time_cost_str def run_anything_task(input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, iou_threshold, inpaint_mode, mask_source_radio, remove_mode, remove_mask_extend, num_relation, kosmos_input, cleaner_size_limit=1080): run_task_time = 0 time_cost_str = '' run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) text_prompt = text_prompt.strip() if not ((task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_draw): if text_prompt == '': return [], gr.Gallery.update(label='Detection prompt is not found!😂😂😂😂'), time_cost_str, gr.Textbox.update(visible=(time_cost_str !='')), None, None, None if input_image is None: return [], gr.Gallery.update(label='Please upload a image!😂😂😂😂'), time_cost_str, gr.Textbox.update(visible=(time_cost_str !='')), None, None, None file_temp = int(time.time()) logger.info(f'run_anything_task_002/{device}_[{file_temp}]_{task_type}/{inpaint_mode}/[{mask_source_radio}]/{remove_mode}/{remove_mask_extend}_[{text_prompt}]/[{inpaint_prompt}]___1_') output_images = [] image_pil, image = load_image(input_image.convert("RGB")) input_img = input_image run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) size = image_pil.size H, W = size[1], size[0] # run grounding dino model if (task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_draw: pass else: groundingdino_device = 'cpu' if device != 'cpu': try: from groundingdino import _C groundingdino_device = 'cuda:0' except: warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only in groundingdino!") boxes_filt, pred_phrases = get_grounding_output( groundingdino_model, image, text_prompt, box_threshold, text_threshold, device=groundingdino_device ) if boxes_filt.size(0) == 0: logger.info(f'run_anything_task_[{file_temp}]_{task_type}_[{text_prompt}]_1___{groundingdino_device}/[No objects detected, please try others.]_') return [], gr.Gallery.update(label='No objects detected, please try others.😂😂😂😂'), time_cost_str, gr.Textbox.update(visible=(time_cost_str !='')), None, None, None boxes_filt_ori = copy.deepcopy(boxes_filt) logger.info(f'run_anything_task_[{file_temp}]_{task_type}_2_') if task_type == 'segment' or ((task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_segment): image = np.array(input_img) if sam_predictor: sam_predictor.set_image(image) for i in range(boxes_filt.size(0)): boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H]) boxes_filt[i][:2] -= boxes_filt[i][2:] / 2 boxes_filt[i][2:] += boxes_filt[i][:2] if sam_predictor: boxes_filt = boxes_filt.to(sam_device) transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]) masks, _, _, _ = sam_predictor.predict_torch( point_coords = None, point_labels = None, boxes = transformed_boxes, multimask_output = False, ) # masks: [9, 1, 512, 512] assert sam_checkpoint, 'sam_checkpoint is not found!' else: masks = torch.zeros(len(boxes_filt), 1, H, W) mask_count = 0 for box in boxes_filt: masks[mask_count, 0, int(box[1]):int(box[3]), int(box[0]):int(box[2])] = 1 mask_count += 1 masks = torch.where(masks > 0, True, False) run_mode = "rectangle" # draw output image plt.figure(figsize=(10, 10)) plt.imshow(image) for mask in masks: show_mask(mask.cpu().numpy(), plt.gca(), random_color=True) for box, label in zip(boxes_filt, pred_phrases): show_box(box.cpu().numpy(), plt.gca(), label) plt.axis('off') # Save the plot to a BytesIO object in memory buf = io.BytesIO() plt.savefig(buf, format='jpeg', bbox_inches='tight') buf.seek(0) # Convert the image in memory to a PIL Image segment_image_result = Image.open(buf).convert('RGB') output_images.append(segment_image_result) # Clearing memory buf.close() plt.clf() plt.close('all') print(sam_predictor) if inpaint_prompt.strip() == '' and mask_source_radio == mask_source_segment: task_type = 'remove' logger.info(f'run_anything_task_[{file_temp}]_{task_type}_4_') if mask_source_radio == mask_source_draw: mask_pil = input_mask_pil mask = input_mask else: masks_ori = copy.deepcopy(masks) if inpaint_mode == 'merge': masks = torch.sum(masks, dim=0).unsqueeze(0) masks = torch.where(masks > 0, True, False) mask = masks[0][0].cpu().numpy() mask_pil = Image.fromarray(mask) output_images.append(mask_pil.convert("RGB")) return mask_pil def change_radio_display(task_type, mask_source_radio): text_prompt_visible = True inpaint_prompt_visible = False mask_source_radio_visible = False num_relation_visible = False image_gallery_visible = True kosmos_input_visible = False kosmos_output_visible = False kosmos_text_output_visible = False if task_type == "Kosmos-2": if kosmos_enable: text_prompt_visible = False image_gallery_visible = False kosmos_input_visible = True kosmos_output_visible = True kosmos_text_output_visible = True if task_type == "inpainting": inpaint_prompt_visible = True if task_type == "inpainting" or task_type == "remove": mask_source_radio_visible = True if mask_source_radio == mask_source_draw: text_prompt_visible = False if task_type == "relate anything": text_prompt_visible = False num_relation_visible = True return (gr.Textbox.update(visible=text_prompt_visible), gr.Textbox.update(visible=inpaint_prompt_visible), gr.Radio.update(visible=mask_source_radio_visible), gr.Slider.update(visible=num_relation_visible), gr.Gallery.update(visible=image_gallery_visible), gr.Radio.update(visible=kosmos_input_visible), gr.Image.update(visible=kosmos_output_visible), gr.HighlightedText.update(visible=kosmos_text_output_visible)) def get_model_device(module): try: if module is None: return 'None' if isinstance(module, torch.nn.DataParallel): module = module.module for submodule in module.children(): if hasattr(submodule, "_parameters"): parameters = submodule._parameters if "weight" in parameters: return parameters["weight"].device return 'UnKnown' except Exception as e: return 'Error' import signal import json from datetime import date, datetime, timedelta from gevent import pywsgi import base64 def get_groundingdino_model(device): # initialize groundingdino model logger.info(f"initialize groundingdino model...") model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae, device=device) return model groundingdino_model = get_groundingdino_model("cuda") def expand_white_pixels(input_pil, expand_by=1): # Convert the input image to grayscale grayscale = input_pil.convert('L') # Create a binary mask where white pixels are represented by 1 binary_mask = np.array(grayscale) > 245 # Apply the dilation operation to the binary mask dilated_mask = binary_dilation(binary_mask, iterations=expand_by) # Create a new PIL image from the dilated mask expanded_image = Image.fromarray(np.uint8(dilated_mask * 255)) return expanded_image def just_fucking_get_sd_mask(input_pil, prompt, expand_by=10): raw_mask = run_anything_task(input_pil, prompt, "inpainting", "", 0.3, 0.25, 0.8, "merge", "type what to detect below", "segment", "10", 5, "Brief") expanded_mask = expand_white_pixels(raw_mask, expand_by=expand_by) return expanded_mask S3_REGION = "fra1" S3_ACCESS_ID = "0RN7BZXS59HYSBD3VB79" S3_ACCESS_SECRET = "hfSPgBlWl5jsGHa2xuByVkSpancgVeA2CVQf2EMp" S3_ENDPOINT_URL = "https://s3.solarcom.ch" S3_BUCKET_NAME = "pissnelke" import boto3 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, ) class EndpointHandler(): def __init__(self, path=""): # get_nude(Image.open("girl.png")) os.environ['path'] = path print("running apt-get update && apt-get install ffmpeg libsm6 libxext6 -y") command = "apt-get update && apt-get install ffmpeg libsm6 libxext6 -y" process = subprocess.Popen( command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = process.communicate() print("ran apt-get update && apt-get install ffmpeg libsm6 libxext6 -y") print("path", path) def __call__(self, data): original_image_res = requests.get(data.get("original_link")) original_pil = Image.open(BytesIO(original_image_res.content)) with_small_tits = data.get("with_small_tits", False) with_big_tits = data.get("with_big_tits", False) nude_pils = get_nude(get_mask_function=just_fucking_get_sd_mask, cfg_scale=data.get("cfg_scale"), generate_max_size=data.get("generate_max_size"), original_max_size=data.get( "original_max_size"), original_pil=original_pil, positive_prompt=data.get("positive_prompt"), steps=data.get("steps"), with_small_tits=with_small_tits, with_big_tits=with_big_tits) filenames = [] for image in nude_pils: byte_arr = io.BytesIO() image.save(byte_arr, format='PNG') byte_arr = byte_arr.getvalue() random_string = ''.join(random.choice( string.ascii_letters + string.digits) for i in range(20)) image_filename = random_string + ".jpeg" s3.put_object(Body=byte_arr, Bucket=S3_BUCKET_NAME, Key=image_filename) filenames.append(image_filename) return { "filenames": filenames }