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| import warnings | |
| warnings.filterwarnings('ignore') | |
| import subprocess, io, os, sys, time | |
| os.system("pip install gradio==3.50.2") | |
| import gradio as gr | |
| from loguru import logger | |
| os.environ["CUDA_VISIBLE_DEVICES"] = "0" | |
| if os.environ.get('IS_MY_DEBUG') is None: | |
| result = subprocess.run(['pip', 'install', '-e', 'GroundingDINO'], check=True) | |
| print(f'pip install GroundingDINO = {result}') | |
| logger.info(f"Start app...") | |
| # result = subprocess.run(['pip', 'list'], check=True) | |
| # print(f'pip list = {result}') | |
| sys.path.insert(0, './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 | |
| groundingdino_enable = True | |
| sam_enable = True | |
| inpainting_enable = True | |
| ram_enable = False | |
| lama_cleaner_enable = True | |
| kosmos_enable = False | |
| # qwen_enable = True | |
| # from qwen_utils import * | |
| if os.environ.get('IS_MY_DEBUG') is not None: | |
| sam_enable = False | |
| ram_enable = False | |
| inpainting_enable = False | |
| kosmos_enable = False | |
| if lama_cleaner_enable: | |
| try: | |
| from lama_cleaner.model_manager import ModelManager | |
| from lama_cleaner.schema import Config as lama_Config | |
| except Exception as e: | |
| lama_cleaner_enable = False | |
| # segment anything | |
| from segment_anything import build_sam, SamPredictor, SamAutomaticMaskGenerator | |
| # diffusers | |
| import PIL | |
| import requests | |
| import torch | |
| from io import BytesIO | |
| from diffusers import StableDiffusionInpaintPipeline | |
| from huggingface_hub import hf_hub_download | |
| from util_computer import computer_info | |
| # relate anything | |
| from ram_utils import iou, sort_and_deduplicate, relation_classes, MLP, show_anns, ram_show_mask | |
| from ram_train_eval import RamModel, RamPredictor | |
| from mmengine.config import Config as mmengine_Config | |
| if lama_cleaner_enable: | |
| from lama_cleaner.helper import ( | |
| load_img, | |
| numpy_to_bytes, | |
| resize_max_size, | |
| ) | |
| # from transformers import AutoProcessor, AutoModelForVision2Seq | |
| import ast | |
| if kosmos_enable: | |
| os.system("pip install transformers@git+https://github.com/huggingface/transformers.git@main") | |
| # os.system("pip install transformers==4.32.0") | |
| from kosmos_utils import * | |
| from util_tencent import getTextTrans | |
| config_file = 'GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py' | |
| ckpt_repo_id = "ShilongLiu/GroundingDINO" | |
| ckpt_filenmae = "groundingdino_swint_ogc.pth" | |
| sam_checkpoint = './sam_vit_h_4b8939.pth' | |
| output_dir = "outputs" | |
| device = 'cpu' | |
| os.makedirs(output_dir, exist_ok=True) | |
| groundingdino_model = None | |
| sam_device = None | |
| sam_model = None | |
| sam_predictor = None | |
| sam_mask_generator = None | |
| sd_model = None | |
| lama_cleaner_model= None | |
| ram_model = None | |
| kosmos_model = None | |
| kosmos_processor = None | |
| 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)) | |
| def set_device(args): | |
| global device | |
| if os.environ.get('IS_MY_DEBUG') is None: | |
| device = args.cuda if torch.cuda.is_available() else 'cpu' | |
| else: | |
| device = 'cpu' | |
| print(f'device={device}') | |
| def load_groundingdino_model(device): | |
| # initialize groundingdino model | |
| global groundingdino_model | |
| logger.info(f"initialize groundingdino model...") | |
| groundingdino_model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae, device=device) #'cpu') | |
| logger.info(f"initialize groundingdino model...{type(groundingdino_model)}") | |
| def get_sam_vit_h_4b8939(): | |
| if not os.path.exists('./sam_vit_h_4b8939.pth'): | |
| logger.info(f"get sam_vit_h_4b8939.pth...") | |
| result = subprocess.run(['wget', '-nv', 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth'], check=True) | |
| print(f'wget sam_vit_h_4b8939.pth result = {result}') | |
| def load_sam_model(device): | |
| # initialize SAM | |
| global sam_model, sam_predictor, sam_mask_generator, sam_device | |
| get_sam_vit_h_4b8939() | |
| logger.info(f"initialize SAM model...") | |
| sam_device = device | |
| sam_model = build_sam(checkpoint=sam_checkpoint).to(sam_device) | |
| sam_predictor = SamPredictor(sam_model) | |
| sam_mask_generator = SamAutomaticMaskGenerator(sam_model) | |
| def load_sd_model(device): | |
| # initialize stable-diffusion-inpainting | |
| global sd_model | |
| logger.info(f"initialize stable-diffusion-inpainting...") | |
| sd_model = None | |
| if os.environ.get('IS_MY_DEBUG') is None: | |
| sd_model = StableDiffusionInpaintPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-inpainting", | |
| revision="fp16", | |
| # "stabilityai/stable-diffusion-2-inpainting", | |
| torch_dtype=torch.float16, | |
| ) | |
| sd_model = sd_model.to(device) | |
| def load_lama_cleaner_model(device): | |
| # initialize lama_cleaner | |
| global lama_cleaner_model | |
| logger.info(f"initialize lama_cleaner...") | |
| lama_cleaner_model = ModelManager( | |
| name='lama', | |
| device=device, | |
| ) | |
| def lama_cleaner_process(image, mask, cleaner_size_limit=1080): | |
| try: | |
| logger.info(f'_______lama_cleaner_process_______1____') | |
| ori_image = image | |
| if mask.shape[0] == image.shape[1] and mask.shape[1] == image.shape[0] and mask.shape[0] != mask.shape[1]: | |
| # rotate image | |
| logger.info(f'_______lama_cleaner_process_______2____') | |
| ori_image = np.transpose(image[::-1, ...][:, ::-1], axes=(1, 0, 2))[::-1, ...] | |
| logger.info(f'_______lama_cleaner_process_______3____') | |
| image = ori_image | |
| logger.info(f'_______lama_cleaner_process_______4____') | |
| original_shape = ori_image.shape | |
| logger.info(f'_______lama_cleaner_process_______5____') | |
| interpolation = cv2.INTER_CUBIC | |
| size_limit = cleaner_size_limit | |
| if size_limit == -1: | |
| logger.info(f'_______lama_cleaner_process_______6____') | |
| size_limit = max(image.shape) | |
| else: | |
| logger.info(f'_______lama_cleaner_process_______7____') | |
| size_limit = int(size_limit) | |
| logger.info(f'_______lama_cleaner_process_______8____') | |
| config = lama_Config( | |
| ldm_steps=25, | |
| ldm_sampler='plms', | |
| zits_wireframe=True, | |
| hd_strategy='Original', | |
| hd_strategy_crop_margin=196, | |
| hd_strategy_crop_trigger_size=1280, | |
| hd_strategy_resize_limit=2048, | |
| prompt='', | |
| use_croper=False, | |
| croper_x=0, | |
| croper_y=0, | |
| croper_height=512, | |
| croper_width=512, | |
| sd_mask_blur=5, | |
| sd_strength=0.75, | |
| sd_steps=50, | |
| sd_guidance_scale=7.5, | |
| sd_sampler='ddim', | |
| sd_seed=42, | |
| cv2_flag='INPAINT_NS', | |
| cv2_radius=5, | |
| ) | |
| logger.info(f'_______lama_cleaner_process_______9____') | |
| if config.sd_seed == -1: | |
| config.sd_seed = random.randint(1, 999999999) | |
| # logger.info(f"Origin image shape_0_: {original_shape} / {size_limit}") | |
| logger.info(f'_______lama_cleaner_process_______10____') | |
| image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation) | |
| # logger.info(f"Resized image shape_1_: {image.shape}") | |
| # logger.info(f"mask image shape_0_: {mask.shape} / {type(mask)}") | |
| logger.info(f'_______lama_cleaner_process_______11____') | |
| mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation) | |
| # logger.info(f"mask image shape_1_: {mask.shape} / {type(mask)}") | |
| logger.info(f'_______lama_cleaner_process_______12____') | |
| res_np_img = lama_cleaner_model(image, mask, config) | |
| logger.info(f'_______lama_cleaner_process_______13____') | |
| torch.cuda.empty_cache() | |
| logger.info(f'_______lama_cleaner_process_______14____') | |
| image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, 'png'))) | |
| logger.info(f'_______lama_cleaner_process_______15____') | |
| except Exception as e: | |
| logger.info(f'lama_cleaner_process[Error]:' + str(e)) | |
| image = None | |
| return image | |
| class Ram_Predictor(RamPredictor): | |
| def __init__(self, config, device='cpu'): | |
| self.config = config | |
| self.device = torch.device(device) | |
| self._build_model() | |
| def _build_model(self): | |
| self.model = RamModel(**self.config.model).to(self.device) | |
| if self.config.load_from is not None: | |
| self.model.load_state_dict(torch.load(self.config.load_from, map_location=self.device)) | |
| self.model.train() | |
| def load_ram_model(device): | |
| # load ram model | |
| global ram_model | |
| if os.environ.get('IS_MY_DEBUG') is not None: | |
| return | |
| model_path = "./checkpoints/ram_epoch12.pth" | |
| ram_config = dict( | |
| model=dict( | |
| pretrained_model_name_or_path='bert-base-uncased', | |
| load_pretrained_weights=False, | |
| num_transformer_layer=2, | |
| input_feature_size=256, | |
| output_feature_size=768, | |
| cls_feature_size=512, | |
| num_relation_classes=56, | |
| pred_type='attention', | |
| loss_type='multi_label_ce', | |
| ), | |
| load_from=model_path, | |
| ) | |
| ram_config = mmengine_Config(ram_config) | |
| ram_model = Ram_Predictor(ram_config, device) | |
| # 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): | |
| text_prompt = getTextTrans(text_prompt, source='zh', target='en') | |
| inpaint_prompt = getTextTrans(inpaint_prompt, source='zh', target='en') | |
| run_task_time = 0 | |
| time_cost_str = '' | |
| run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) | |
| if (task_type == 'Kosmos-2'): | |
| global kosmos_model, kosmos_processor | |
| if isinstance(input_image, dict): | |
| image_pil, image = load_image(input_image['image'].convert("RGB")) | |
| input_img = input_image['image'] | |
| else: | |
| image_pil, image = load_image(input_image.convert("RGB")) | |
| input_img = input_image | |
| kosmos_image, kosmos_text, kosmos_entities = kosmos_generate_predictions(image_pil, kosmos_input, kosmos_model, kosmos_processor) | |
| run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) | |
| return None, None, time_cost_str, kosmos_image, gr.Textbox.update(visible=(time_cost_str !='')), kosmos_text, kosmos_entities | |
| if (task_type == 'relate anything'): | |
| output_images = relate_anything(input_image['image'], num_relation) | |
| run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) | |
| return output_images, gr.Gallery.update(label='relate images'), time_cost_str, gr.Textbox.update(visible=(time_cost_str !='')), None, None, None | |
| text_prompt = text_prompt.strip() | |
| if not ((task_type in ['inpainting', 'outpainting'] 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 = [] | |
| # load image | |
| if mask_source_radio == mask_source_draw: | |
| input_mask_pil = input_image['mask'] | |
| input_mask = np.array(input_mask_pil.convert("L")) | |
| if isinstance(input_image, dict): | |
| image_pil, image = load_image(input_image['image'].convert("RGB")) | |
| input_img = input_image['image'] | |
| output_images.append(input_image['image']) | |
| run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) | |
| else: | |
| image_pil, image = load_image(input_image.convert("RGB")) | |
| input_img = input_image | |
| output_images.append(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 in ['inpainting', 'outpainting'] 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) | |
| pred_dict = { | |
| "boxes": boxes_filt, | |
| "size": [size[1], size[0]], # H,W | |
| "labels": pred_phrases, | |
| } | |
| image_with_box = plot_boxes_to_image(copy.deepcopy(image_pil), pred_dict)[0] | |
| output_images.append(image_with_box) | |
| run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) | |
| logger.info(f'run_anything_task_[{file_temp}]_{task_type}_2_') | |
| if task_type == 'segment' or ((task_type in ['inpainting', 'outpainting'] 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') | |
| image_path = os.path.join(output_dir, f"grounding_seg_output_{file_temp}.jpg") | |
| plt.savefig(image_path, bbox_inches="tight") | |
| plt.clf() | |
| plt.close('all') | |
| segment_image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) | |
| os.remove(image_path) | |
| output_images.append(Image.fromarray(segment_image_result)) | |
| run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) | |
| logger.info(f'run_anything_task_[{file_temp}]_{task_type}_3_') | |
| if task_type == 'detection' or task_type == 'segment': | |
| logger.info(f'run_anything_task_[{file_temp}]_{task_type}_9_') | |
| return output_images, gr.Gallery.update(label='result images'), time_cost_str, gr.Textbox.update(visible=(time_cost_str !='')), None, None, None | |
| elif task_type in ['inpainting', 'outpainting'] or task_type == 'remove': | |
| 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")) | |
| run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) | |
| if task_type in ['inpainting', 'outpainting']: | |
| # inpainting pipeline | |
| image_source_for_inpaint = image_pil.resize((512, 512)) | |
| image_mask_for_inpaint = mask_pil.resize((512, 512)) | |
| if task_type in ['outpainting']: | |
| # reverse mask | |
| img_arr = np.array(image_mask_for_inpaint) | |
| img_arr = np.where(img_arr > 0, 1, img_arr) | |
| img_arr = 1 - img_arr | |
| image_mask_for_inpaint = Image.fromarray(255*img_arr.astype('uint8')) | |
| output_images.append(image_mask_for_inpaint.convert("RGB")) | |
| run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) | |
| image_inpainting = sd_model(prompt=inpaint_prompt, image=image_source_for_inpaint, mask_image=image_mask_for_inpaint).images[0] | |
| else: | |
| # remove from mask | |
| if mask_source_radio == mask_source_segment: | |
| mask_imgs = [] | |
| masks_shape = masks_ori.shape | |
| boxes_filt_ori_array = boxes_filt_ori.numpy() | |
| if inpaint_mode == 'merge': | |
| extend_shape_0 = masks_shape[0] | |
| extend_shape_1 = masks_shape[1] | |
| else: | |
| extend_shape_0 = 1 | |
| extend_shape_1 = 1 | |
| for i in range(extend_shape_0): | |
| for j in range(extend_shape_1): | |
| mask = masks_ori[i][j].cpu().numpy() | |
| mask_pil = Image.fromarray(mask) | |
| if remove_mode == 'segment': | |
| useRectangle = False | |
| else: | |
| useRectangle = True | |
| try: | |
| remove_mask_extend = int(remove_mask_extend) | |
| except: | |
| remove_mask_extend = 10 | |
| mask_pil_exp = mask_extend(copy.deepcopy(mask_pil).convert("RGB"), | |
| xywh_to_xyxy(torch.tensor(boxes_filt_ori_array[i]), W, H), | |
| extend_pixels=remove_mask_extend, useRectangle=useRectangle) | |
| mask_imgs.append(mask_pil_exp) | |
| mask_pil = mix_masks(mask_imgs) | |
| output_images.append(mask_pil.convert("RGB")) | |
| run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) | |
| logger.info(f'run_anything_task_[{file_temp}]_{task_type}_6_') | |
| image_inpainting = lama_cleaner_process(np.array(image_pil), np.array(mask_pil.convert("L")), cleaner_size_limit) | |
| if image_inpainting is None: | |
| logger.info(f'run_anything_task_failed_') | |
| return None, None, None, None, None, None, None | |
| # output_images.append(image_inpainting) | |
| # run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) | |
| logger.info(f'run_anything_task_[{file_temp}]_{task_type}_7_') | |
| image_inpainting = image_inpainting.resize((image_pil.size[0], image_pil.size[1])) | |
| output_images.append(image_inpainting) | |
| run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) | |
| logger.info(f'run_anything_task_[{file_temp}]_{task_type}_9_') | |
| return output_images, gr.Gallery.update(label='result images'), time_cost_str, gr.Textbox.update(visible=(time_cost_str !='')), None, None, None | |
| else: | |
| logger.info(f"task_type:{task_type} error!") | |
| logger.info(f'run_anything_task_[{file_temp}]_9_9_') | |
| return output_images, gr.Gallery.update(label='result images'), time_cost_str, gr.Textbox.update(visible=(time_cost_str !='')), None, None, None | |
| 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 in ['inpainting', 'outpainting']: | |
| inpaint_prompt_visible = True | |
| if task_type in ['inpainting', 'outpainting'] 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' | |
| def main_gradio(args): | |
| block = gr.Blocks( | |
| title="SAM and others", | |
| theme="shivi/calm_seafoam@>=0.0.1,<1.0.0", | |
| ) | |
| with block: | |
| with gr.Row(): | |
| with gr.Column(): | |
| task_types = ["detection"] | |
| if sam_enable: | |
| task_types.append("segment") | |
| if inpainting_enable: | |
| task_types.append("inpainting") | |
| task_types.append("outpainting") | |
| if lama_cleaner_enable: | |
| task_types.append("remove") | |
| if ram_enable: | |
| task_types.append("relate anything") | |
| if kosmos_enable: | |
| task_types.append("Kosmos-2") | |
| input_image = gr.Image(source='upload', elem_id="image_upload", tool='sketch', type='pil', label="Upload", | |
| height=512, brush_color='#00FFFF', mask_opacity=0.6) | |
| task_type = gr.Radio(task_types, value="detection", | |
| label='Task type', visible=True) | |
| mask_source_radio = gr.Radio([mask_source_draw, mask_source_segment], | |
| value=mask_source_segment, label="Mask from", | |
| visible=False) | |
| text_prompt = gr.Textbox(label="Detection Prompt[To detect multiple objects, seperating each with '.', like this: cat . dog . chair ]", placeholder="Cannot be empty") | |
| inpaint_prompt = gr.Textbox(label="Inpaint/Outpaint Prompt (if this is empty, then remove)", visible=False) | |
| num_relation = gr.Slider(label="How many relations do you want to see", minimum=1, maximum=20, value=5, step=1, visible=False) | |
| kosmos_input = gr.Radio(["Brief", "Detailed"], label="Kosmos Description Type", value="Brief", visible=False) | |
| run_button = gr.Button(label="Run", visible=True) | |
| with gr.Accordion("Advanced options", open=False) as advanced_options: | |
| box_threshold = gr.Slider( | |
| label="Box Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.001 | |
| ) | |
| text_threshold = gr.Slider( | |
| label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001 | |
| ) | |
| iou_threshold = gr.Slider( | |
| label="IOU Threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.001 | |
| ) | |
| inpaint_mode = gr.Radio(["merge", "first"], value="merge", label="inpaint_mode") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| remove_mode = gr.Radio(["segment", "rectangle"], value="segment", label='remove mode') | |
| with gr.Column(scale=1): | |
| remove_mask_extend = gr.Textbox(label="remove_mask_extend", value='10') | |
| with gr.Column(): | |
| image_gallery = gr.Gallery(label="result images", show_label=True, elem_id="gallery", height=512, visible=True | |
| ).style(preview=True, columns=[5], object_fit="scale-down", height="auto") | |
| time_cost = gr.Textbox(label="Time cost by step (ms):", visible=False, interactive=False) | |
| kosmos_output = gr.Image(type="pil", label="result images", visible=False) | |
| kosmos_text_output = gr.HighlightedText( | |
| label="Generated Description", | |
| combine_adjacent=False, | |
| show_legend=True, | |
| visible=False, | |
| ).style(color_map=color_map) | |
| # record which text span (label) is selected | |
| selected = gr.Number(-1, show_label=False, placeholder="Selected", visible=False) | |
| # record the current `entities` | |
| entity_output = gr.Textbox(visible=False) | |
| # get the current selected span label | |
| def get_text_span_label(evt: gr.SelectData): | |
| if evt.value[-1] is None: | |
| return -1 | |
| return int(evt.value[-1]) | |
| # and set this information to `selected` | |
| kosmos_text_output.select(get_text_span_label, None, selected) | |
| # update output image when we change the span (enity) selection | |
| def update_output_image(img_input, image_output, entities, idx): | |
| entities = ast.literal_eval(entities) | |
| updated_image = draw_entity_boxes_on_image(img_input, entities, entity_index=idx) | |
| return updated_image | |
| selected.change(update_output_image, [kosmos_output, kosmos_output, entity_output, selected], [kosmos_output]) | |
| run_button.click(fn=run_anything_task, inputs=[ | |
| 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], | |
| outputs=[image_gallery, image_gallery, time_cost, time_cost, kosmos_output, kosmos_text_output, entity_output], show_progress=True, queue=True) | |
| mask_source_radio.change(fn=change_radio_display, inputs=[task_type, mask_source_radio], | |
| outputs=[text_prompt, inpaint_prompt, mask_source_radio, num_relation]) | |
| task_type.change(fn=change_radio_display, inputs=[task_type, mask_source_radio], | |
| outputs=[text_prompt, inpaint_prompt, mask_source_radio, num_relation, | |
| image_gallery, kosmos_input, kosmos_output, kosmos_text_output | |
| ]) | |
| DESCRIPTION = f'### This demo from [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything). <br>' | |
| if lama_cleaner_enable: | |
| DESCRIPTION += f'Remove(cleaner) from [lama-cleaner](https://github.com/Sanster/lama-cleaner). <br>' | |
| if kosmos_enable: | |
| DESCRIPTION += f'Kosmos-2 from [Kosmos-2](https://github.com/microsoft/unilm/tree/master/kosmos-2). <br>' | |
| if ram_enable: | |
| DESCRIPTION += f'RAM from [RelateAnything](https://github.com/Luodian/RelateAnything). <br>' | |
| DESCRIPTION += f'Thanks for their excellent work.' | |
| DESCRIPTION += f'<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. \ | |
| <a href="https://huggingface.co/spaces/yizhangliu/Grounded-Segment-Anything?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>' | |
| gr.Markdown(DESCRIPTION) | |
| print(f'device = {device}') | |
| print(f'torch.cuda.is_available = {torch.cuda.is_available()}') | |
| computer_info() | |
| block.queue(max_size=10, api_open=False) | |
| block.launch(server_name='0.0.0.0', server_port=args.port, debug=args.debug, share=args.share) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser("Grounded SAM demo", add_help=True) | |
| parser.add_argument("--debug", action="store_true", help="using debug mode") | |
| parser.add_argument("--share", action="store_true", help="share the app") | |
| parser.add_argument("--port", "-p", type=int, default=7860, help="port") | |
| parser.add_argument("--cuda", "-c", type=str, default='cuda:0', help="cuda") | |
| args, _ = parser.parse_known_args() | |
| print(f'args = {args}') | |
| if os.environ.get('IS_MY_DEBUG') is None: | |
| os.system("pip list") | |
| set_device(args) | |
| if device == 'cpu': | |
| kosmos_enable = False | |
| if kosmos_enable: | |
| kosmos_model, kosmos_processor = load_kosmos_model(device) | |
| if groundingdino_enable: | |
| load_groundingdino_model('cpu') | |
| if sam_enable: | |
| load_sam_model(device) | |
| if inpainting_enable: | |
| load_sd_model(device) | |
| if lama_cleaner_enable: | |
| load_lama_cleaner_model(device) | |
| if ram_enable: | |
| load_ram_model(device) | |
| if os.environ.get('IS_MY_DEBUG') is None: | |
| os.system("pip list") | |
| main_gradio(args) | |