wehub-resource-sync
287 lines
11 KiB
Python
287 lines
11 KiB
Python
import argparse
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import os
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from warnings import warn
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import numpy as np
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import torch
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from PIL import Image, ImageDraw, ImageFont
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import litellm
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# Grounding DINO
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import GroundingDINO.groundingdino.datasets.transforms as T
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from GroundingDINO.groundingdino.models import build_model
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from GroundingDINO.groundingdino.util import box_ops
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from GroundingDINO.groundingdino.util.slconfig import SLConfig
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from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
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# segment anything
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from segment_anything import build_sam, SamPredictor
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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# diffusers
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import PIL
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import requests
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import torch
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from io import BytesIO
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from diffusers import StableDiffusionInpaintPipeline
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# whisper
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import whisper
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# ChatGPT
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import openai
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def load_image(image_path):
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# load image
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image_pil = Image.open(image_path).convert("RGB") # load image
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transform = T.Compose(
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[
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T.RandomResize([800], max_size=1333),
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T.ToTensor(),
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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]
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)
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image, _ = transform(image_pil, None) # 3, h, w
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return image_pil, image
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def load_model(model_config_path, model_checkpoint_path, device):
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args = SLConfig.fromfile(model_config_path)
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args.device = device
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model = build_model(args)
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checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
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load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
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print(load_res)
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_ = model.eval()
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return model
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def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device="cpu"):
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caption = caption.lower()
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caption = caption.strip()
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if not caption.endswith("."):
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caption = caption + "."
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model = model.to(device)
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image = image.to(device)
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with torch.no_grad():
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outputs = model(image[None], captions=[caption])
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logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256)
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boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4)
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logits.shape[0]
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# filter output
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logits_filt = logits.clone()
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boxes_filt = boxes.clone()
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filt_mask = logits_filt.max(dim=1)[0] > box_threshold
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logits_filt = logits_filt[filt_mask] # num_filt, 256
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boxes_filt = boxes_filt[filt_mask] # num_filt, 4
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logits_filt.shape[0]
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# get phrase
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tokenlizer = model.tokenizer
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tokenized = tokenlizer(caption)
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# build pred
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pred_phrases = []
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for logit, box in zip(logits_filt, boxes_filt):
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pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)
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if with_logits:
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pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
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else:
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pred_phrases.append(pred_phrase)
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return boxes_filt, pred_phrases
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def show_mask(mask, ax, random_color=False):
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if random_color:
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color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
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else:
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color = np.array([30/255, 144/255, 255/255, 0.6])
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h, w = mask.shape[-2:]
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mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
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ax.imshow(mask_image)
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def show_box(box, ax, label):
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x0, y0 = box[0], box[1]
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w, h = box[2] - box[0], box[3] - box[1]
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ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))
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ax.text(x0, y0, label)
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def speech_recognition(speech_file, model):
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# whisper
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# load audio and pad/trim it to fit 30 seconds
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audio = whisper.load_audio(speech_file)
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audio = whisper.pad_or_trim(audio)
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# make log-Mel spectrogram and move to the same device as the model
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mel = whisper.log_mel_spectrogram(audio).to(model.device)
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# detect the spoken language
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_, probs = model.detect_language(mel)
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speech_language = max(probs, key=probs.get)
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# decode the audio
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options = whisper.DecodingOptions()
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result = whisper.decode(model, mel, options)
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# print the recognized text
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speech_text = result.text
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return speech_text, speech_language
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def filter_prompts_with_chatgpt(caption, max_tokens=100, model="gpt-3.5-turbo"):
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prompt = [
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{
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'role': 'system',
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'content': f"Extract the main object to be replaced and marked it as 'main_object', " + \
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f"Extract the remaining part as 'other prompt' " + \
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f"Return (main_object, other prompt)" + \
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f'Given caption: {caption}.'
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}
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]
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response = litellm.completion(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens)
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reply = response['choices'][0]['message']['content']
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try:
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det_prompt, inpaint_prompt = reply.split('\n')[0].split(':')[-1].strip(), reply.split('\n')[1].split(':')[-1].strip()
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except:
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warn(f"Failed to extract tags from caption") # use caption as det_prompt, inpaint_prompt
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det_prompt, inpaint_prompt = caption, caption
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return det_prompt, inpaint_prompt
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Grounded-Segment-Anything Demo", add_help=True)
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parser.add_argument("--config", type=str, required=True, help="path to config file")
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parser.add_argument(
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"--grounded_checkpoint", type=str, required=True, help="path to checkpoint file"
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)
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parser.add_argument(
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"--sam_checkpoint", type=str, required=True, help="path to checkpoint file"
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)
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parser.add_argument("--input_image", type=str, required=True, help="path to image file")
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parser.add_argument(
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"--output_dir", "-o", type=str, default="outputs", required=True, help="output directory"
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)
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parser.add_argument("--cache_dir", type=str, default=None, help="save your huggingface large model cache")
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parser.add_argument("--det_speech_file", type=str, help="grounding speech file")
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parser.add_argument("--inpaint_speech_file", type=str, help="inpaint speech file")
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parser.add_argument("--prompt_speech_file", type=str, help="prompt speech file, no need to provide det_speech_file")
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parser.add_argument("--enable_chatgpt", action="store_true", help="enable chatgpt")
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parser.add_argument("--openai_key", type=str, help="key for chatgpt")
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parser.add_argument("--openai_proxy", default=None, type=str, help="proxy for chatgpt")
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parser.add_argument("--whisper_model", type=str, default="small", help="whisper model version: tiny, base, small, medium, large")
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parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold")
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parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold")
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parser.add_argument("--inpaint_mode", type=str, default="first", help="inpaint mode")
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parser.add_argument("--device", type=str, default="cpu", help="running on cpu only!, default=False")
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parser.add_argument("--prompt_extra", type=str, default=" high resolution, real scene", help="extra prompt for inpaint")
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args = parser.parse_args()
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# cfg
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config_file = args.config # change the path of the model config file
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grounded_checkpoint = args.grounded_checkpoint # change the path of the model
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sam_checkpoint = args.sam_checkpoint
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image_path = args.input_image
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output_dir = args.output_dir
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cache_dir=args.cache_dir
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# if not os.path.exists(cache_dir):
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# print(f"create your cache dir:{cache_dir}")
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# os.mkdir(cache_dir)
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box_threshold = args.box_threshold
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text_threshold = args.text_threshold
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inpaint_mode = args.inpaint_mode
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device = args.device
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# make dir
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os.makedirs(output_dir, exist_ok=True)
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# load image
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image_pil, image = load_image(image_path)
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# load model
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model = load_model(config_file, grounded_checkpoint, device=device)
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# visualize raw image
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image_pil.save(os.path.join(output_dir, "raw_image.jpg"))
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# recognize speech
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whisper_model = whisper.load_model(args.whisper_model)
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if args.enable_chatgpt:
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openai.api_key = args.openai_key
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if args.openai_proxy:
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openai.proxy = {"http": args.openai_proxy, "https": args.openai_proxy}
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speech_text, _ = speech_recognition(args.prompt_speech_file, whisper_model)
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det_prompt, inpaint_prompt = filter_prompts_with_chatgpt(speech_text)
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inpaint_prompt += args.prompt_extra
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print(f"det_prompt: {det_prompt}, inpaint_prompt: {inpaint_prompt}")
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else:
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det_prompt, det_speech_language = speech_recognition(args.det_speech_file, whisper_model)
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inpaint_prompt, inpaint_speech_language = speech_recognition(args.inpaint_speech_file, whisper_model)
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print(f"det_prompt: {det_prompt}, using language: {det_speech_language}")
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print(f"inpaint_prompt: {inpaint_prompt}, using language: {inpaint_speech_language}")
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# run grounding dino model
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boxes_filt, pred_phrases = get_grounding_output(
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model, image, det_prompt, box_threshold, text_threshold, device=device
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)
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# initialize SAM
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predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device))
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image = cv2.imread(image_path)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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predictor.set_image(image)
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size = image_pil.size
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H, W = size[1], size[0]
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for i in range(boxes_filt.size(0)):
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boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])
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boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
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boxes_filt[i][2:] += boxes_filt[i][:2]
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boxes_filt = boxes_filt.cpu()
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transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)
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masks, _, _ = predictor.predict_torch(
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point_coords = None,
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point_labels = None,
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boxes = transformed_boxes.to(device),
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multimask_output = False,
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)
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# masks: [1, 1, 512, 512]
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# inpainting pipeline
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if inpaint_mode == 'merge':
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masks = torch.sum(masks, dim=0).unsqueeze(0)
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masks = torch.where(masks > 0, True, False)
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mask = masks[0][0].cpu().numpy() # simply choose the first mask, which will be refine in the future release
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mask_pil = Image.fromarray(mask)
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image_pil = Image.fromarray(image)
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pipe = StableDiffusionInpaintPipeline.from_pretrained(
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"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16,cache_dir=cache_dir
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)
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pipe = pipe.to("cuda")
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# prompt = "A sofa, high quality, detailed"
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image = pipe(prompt=inpaint_prompt, image=image_pil, mask_image=mask_pil).images[0]
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image.save(os.path.join(output_dir, "grounded_sam_inpainting_output.jpg"))
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# draw output image
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# plt.figure(figsize=(10, 10))
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# plt.imshow(image)
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# for mask in masks:
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# show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)
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# for box, label in zip(boxes_filt, pred_phrases):
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# show_box(box.numpy(), plt.gca(), label)
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# plt.axis('off')
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# plt.savefig(os.path.join(output_dir, "grounded_sam_output.jpg"), bbox_inches="tight")
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