wehub-resource-sync
324 lines
12 KiB
Python
324 lines
12 KiB
Python
import argparse
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import os
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import copy
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import numpy as np
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import json
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import torch
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import torchvision
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from PIL import Image, ImageDraw, ImageFont
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import nltk
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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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# BLIP
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from transformers import BlipProcessor, BlipForConditionalGeneration
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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 generate_caption(raw_image, device):
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# unconditional image captioning
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if device == "cuda":
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inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
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else:
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inputs = processor(raw_image, return_tensors="pt")
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out = blip_model.generate(**inputs)
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caption = processor.decode(out[0], skip_special_tokens=True)
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return caption
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def generate_tags(caption, split=',', max_tokens=100, model="gpt-3.5-turbo"):
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lemma = nltk.wordnet.WordNetLemmatizer()
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if openai_key:
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prompt = [
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{
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'role': 'system',
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'content': 'Extract the unique nouns in the caption. Remove all the adjectives. ' + \
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f'List the nouns in singular form. Split them by "{split} ". ' + \
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f'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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# sometimes return with "noun: xxx, xxx, xxx"
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tags = reply.split(':')[-1].strip()
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else:
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nltk.download(['punkt', 'averaged_perceptron_tagger', 'wordnet'])
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tags_list = [word for (word, pos) in nltk.pos_tag(nltk.word_tokenize(caption)) if pos[0] == 'N']
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tags_lemma = [lemma.lemmatize(w) for w in tags_list]
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tags = ', '.join(map(str, tags_lemma))
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return tags
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def check_caption(caption, pred_phrases, max_tokens=100, model="gpt-3.5-turbo"):
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object_list = [obj.split('(')[0] for obj in pred_phrases]
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object_num = []
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for obj in set(object_list):
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object_num.append(f'{object_list.count(obj)} {obj}')
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object_num = ', '.join(object_num)
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print(f"Correct object number: {object_num}")
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if openai_key:
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prompt = [
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{
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'role': 'system',
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'content': 'Revise the number in the caption if it is wrong. ' + \
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f'Caption: {caption}. ' + \
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f'True object number: {object_num}. ' + \
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'Only give the revised 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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# sometimes return with "Caption: xxx, xxx, xxx"
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caption = reply.split(':')[-1].strip()
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return caption
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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,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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scores = []
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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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pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
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scores.append(logit.max().item())
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return boxes_filt, torch.Tensor(scores), 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 save_mask_data(output_dir, caption, mask_list, box_list, label_list):
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value = 0 # 0 for background
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mask_img = torch.zeros(mask_list.shape[-2:])
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for idx, mask in enumerate(mask_list):
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mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1
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plt.figure(figsize=(10, 10))
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plt.imshow(mask_img.numpy())
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plt.axis('off')
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plt.savefig(os.path.join(output_dir, 'mask.jpg'), bbox_inches="tight", dpi=300, pad_inches=0.0)
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json_data = {
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'caption': caption,
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'mask':[{
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'value': value,
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'label': 'background'
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}]
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}
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for label, box in zip(label_list, box_list):
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value += 1
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name, logit = label.split('(')
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logit = logit[:-1] # the last is ')'
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json_data['mask'].append({
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'value': value,
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'label': name,
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'logit': float(logit),
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'box': box.numpy().tolist(),
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})
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with open(os.path.join(output_dir, 'label.json'), 'w') as f:
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json.dump(json_data, f)
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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("--split", default=",", type=str, help="split for text prompt")
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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(
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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("--box_threshold", type=float, default=0.25, help="box threshold")
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parser.add_argument("--text_threshold", type=float, default=0.2, help="text threshold")
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parser.add_argument("--iou_threshold", type=float, default=0.5, help="iou threshold")
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parser.add_argument("--device", type=str, default="cpu", help="running on cpu only!, default=False")
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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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split = args.split
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openai_key = args.openai_key
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openai_proxy = args.openai_proxy
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output_dir = args.output_dir
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box_threshold = args.box_threshold
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text_threshold = args.text_threshold
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iou_threshold = args.iou_threshold
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device = args.device
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openai.api_key = openai_key
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if openai_proxy:
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openai.proxy = {"http": openai_proxy, "https": openai_proxy}
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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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# generate caption and tags
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# use Tag2Text can generate better captions
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# https://huggingface.co/spaces/xinyu1205/Tag2Text
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# but there are some bugs...
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processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
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if device == "cuda":
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blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda")
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else:
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blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
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caption = generate_caption(image_pil, device=device)
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# Currently ", " is better for detecting single tags
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# while ". " is a little worse in some case
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text_prompt = generate_tags(caption, split=split)
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print(f"Caption: {caption}")
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print(f"Tags: {text_prompt}")
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# run grounding dino model
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boxes_filt, scores, pred_phrases = get_grounding_output(
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model, image, text_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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# use NMS to handle overlapped boxes
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print(f"Before NMS: {boxes_filt.shape[0]} boxes")
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nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist()
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boxes_filt = boxes_filt[nms_idx]
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pred_phrases = [pred_phrases[idx] for idx in nms_idx]
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print(f"After NMS: {boxes_filt.shape[0]} boxes")
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caption = check_caption(caption, pred_phrases)
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print(f"Revise caption with number: {caption}")
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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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# 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.title(caption)
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plt.axis('off')
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plt.savefig(
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os.path.join(output_dir, "automatic_label_output.jpg"),
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bbox_inches="tight", dpi=300, pad_inches=0.0
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)
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save_mask_data(output_dir, caption, masks, boxes_filt, pred_phrases)
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