# Edit Anything trained with Stable Diffusion + ControlNet + SAM + BLIP2 # pip install mmcv from torchvision.utils import save_image from PIL import Image import subprocess from collections import OrderedDict import numpy as np import cv2 import textwrap import torch import os from annotator.util import resize_image, HWC3 import mmcv import random # device = "cuda" if torch.cuda.is_available() else "cpu" # > 15GB GPU memory required device = "cpu" use_blip = True use_gradio = True if device == 'cpu': data_type = torch.float32 else: data_type = torch.float16 # Diffusion init using diffusers. # diffusers==0.14.0 required. from diffusers.utils import load_image base_model_path = "stabilityai/stable-diffusion-2-inpainting" config_dict = OrderedDict([('SAM Pretrained(v0-1): Good Natural Sense', 'shgao/edit-anything-v0-1-1'), ('LAION Pretrained(v0-3): Good Face', 'shgao/edit-anything-v0-3'), ('SD Inpainting: Not keep position', 'stabilityai/stable-diffusion-2-inpainting') ]) # Segment-Anything init. # pip install git+https://github.com/facebookresearch/segment-anything.git try: from segment_anything import sam_model_registry, SamAutomaticMaskGenerator except ImportError: print('segment_anything not installed') result = subprocess.run(['pip', 'install', 'git+https://github.com/facebookresearch/segment-anything.git'], check=True) print(f'Install segment_anything {result}') from segment_anything import sam_model_registry, SamAutomaticMaskGenerator if not os.path.exists('./models/sam_vit_h_4b8939.pth'): result = subprocess.run(['wget', 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth', '-P', 'models'], check=True) print(f'Download sam_vit_h_4b8939.pth {result}') sam_checkpoint = "models/sam_vit_h_4b8939.pth" model_type = "default" sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) sam.to(device=device) mask_generator = SamAutomaticMaskGenerator(sam) # BLIP2 init. if use_blip: # need the latest transformers # pip install git+https://github.com/huggingface/transformers.git from transformers import AutoProcessor, Blip2ForConditionalGeneration processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b") blip_model = Blip2ForConditionalGeneration.from_pretrained( "Salesforce/blip2-opt-2.7b", torch_dtype=data_type) def region_classify_w_blip2(image): inputs = processor(image, return_tensors="pt").to(device, data_type) generated_ids = blip_model.generate(**inputs, max_new_tokens=15) generated_text = processor.batch_decode( generated_ids, skip_special_tokens=True)[0].strip() return generated_text def region_level_semantic_api(image, topk=5): """ rank regions by area, and classify each region with blip2 Args: image: numpy array topk: int Returns: topk_region_w_class_label: list of dict with key 'class_label' """ topk_region_w_class_label = [] anns = mask_generator.generate(image) if len(anns) == 0: return [] sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True) for i in range(min(topk, len(sorted_anns))): ann = anns[i] m = ann['segmentation'] m_3c = m[:,:, np.newaxis] m_3c = np.concatenate((m_3c,m_3c,m_3c), axis=2) bbox = ann['bbox'] region = mmcv.imcrop(image*m_3c, np.array([bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]), scale=1) region_class_label = region_classify_w_blip2(region) ann['class_label'] = region_class_label print(ann['class_label'], str(bbox)) topk_region_w_class_label.append(ann) return topk_region_w_class_label def show_semantic_image_label(anns): """ show semantic image label for each region Args: anns: list of dict with key 'class_label' Returns: full_img: numpy array """ full_img = None # generate mask image for i in range(len(anns)): m = anns[i]['segmentation'] if full_img is None: full_img = np.zeros((m.shape[0], m.shape[1], 3)) color_mask = np.random.random((1, 3)).tolist()[0] full_img[m != 0] = color_mask full_img = full_img*255 # add text on this mask image for i in range(len(anns)): m = anns[i]['segmentation'] class_label = anns[i]['class_label'] # add text to region # Calculate the centroid of the region to place the text y, x = np.where(m != 0) x_center, y_center = int(np.mean(x)), int(np.mean(y)) # Split the text into multiple lines max_width = 20 # Adjust this value based on your preferred maximum width wrapped_text = textwrap.wrap(class_label, width=max_width) # Add text to region font = cv2.FONT_HERSHEY_SIMPLEX font_scale = 1.2 font_thickness = 2 font_color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) # red line_spacing = 40 # Adjust this value based on your preferred line for idx, line in enumerate(wrapped_text): y_offset = y_center - (len(wrapped_text) - 1) * line_spacing // 2 + idx * line_spacing text_size = cv2.getTextSize(line, font, font_scale, font_thickness)[0] x_offset = x_center - text_size[0] // 2 # Draw the text multiple times with small offsets to create a bolder appearance offsets = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)] for off_x, off_y in offsets: cv2.putText(full_img, line, (x_offset + off_x, y_offset + off_y), font, font_scale, font_color, font_thickness, cv2.LINE_AA) return full_img image_path = "images/sa_224577.jpg" input_image = Image.open(image_path) detect_resolution=1024 input_image = resize_image(np.array(input_image, dtype=np.uint8), detect_resolution) region_level_annots = region_level_semantic_api(input_image, topk=5) output = show_semantic_image_label(region_level_annots) image_list = [] input_image = resize_image(input_image, 512) output = resize_image(output, 512) input_image = np.array(input_image, dtype=np.uint8) output = np.array(output, dtype=np.uint8) image_list.append(torch.tensor(input_image).float()) image_list.append(torch.tensor(output).float()) for each in image_list: print(each.shape, type(each)) print(each.max(), each.min()) image_list = torch.stack(image_list).permute(0, 3, 1, 2) print(image_list.shape) save_image(image_list, "images/sample_semantic.jpg", nrow=2, normalize=True)