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import argparse | |
import os | |
from warnings import warn | |
import numpy as np | |
import torch | |
from PIL import Image, ImageDraw, ImageFont | |
# 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 | |
# segment anything | |
from segment_anything import build_sam, SamPredictor | |
import cv2 | |
import numpy as np | |
import matplotlib.pyplot as plt | |
# diffusers | |
import PIL | |
import requests | |
import torch | |
from io import BytesIO | |
from diffusers import StableDiffusionInpaintPipeline | |
# whisper | |
import whisper | |
# ChatGPT | |
import openai | |
def load_image(image_path): | |
# load image | |
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 load_model(model_config_path, model_checkpoint_path, device): | |
args = SLConfig.fromfile(model_config_path) | |
args.device = device | |
model = build_model(args) | |
checkpoint = torch.load(model_checkpoint_path, map_location="cpu") | |
load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False) | |
print(load_res) | |
_ = model.eval() | |
return model | |
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 speech_recognition(speech_file, model): | |
# whisper | |
# load audio and pad/trim it to fit 30 seconds | |
audio = whisper.load_audio(speech_file) | |
audio = whisper.pad_or_trim(audio) | |
# make log-Mel spectrogram and move to the same device as the model | |
mel = whisper.log_mel_spectrogram(audio).to(model.device) | |
# detect the spoken language | |
_, probs = model.detect_language(mel) | |
speech_language = max(probs, key=probs.get) | |
# decode the audio | |
options = whisper.DecodingOptions() | |
result = whisper.decode(model, mel, options) | |
# print the recognized text | |
speech_text = result.text | |
return speech_text, speech_language | |
def filter_prompts_with_chatgpt(caption, max_tokens=100, model="gpt-3.5-turbo"): | |
prompt = [ | |
{ | |
'role': 'system', | |
'content': f"Extract the main object to be replaced and marked it as 'main_object', " + \ | |
f"Extract the remaining part as 'other prompt' " + \ | |
f"Return (main_object, other prompt)" + \ | |
f'Given caption: {caption}.' | |
} | |
] | |
response = openai.ChatCompletion.create(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens) | |
reply = response['choices'][0]['message']['content'] | |
try: | |
det_prompt, inpaint_prompt = reply.split('\n')[0].split(':')[-1].strip(), reply.split('\n')[1].split(':')[-1].strip() | |
except: | |
warn(f"Failed to extract tags from caption") # use caption as det_prompt, inpaint_prompt | |
det_prompt, inpaint_prompt = caption, caption | |
return det_prompt, inpaint_prompt | |
if __name__ == "__main__": | |
parser = argparse.ArgumentParser("Grounded-Segment-Anything Demo", add_help=True) | |
parser.add_argument("--config", type=str, required=True, help="path to config file") | |
parser.add_argument( | |
"--grounded_checkpoint", type=str, required=True, help="path to checkpoint file" | |
) | |
parser.add_argument( | |
"--sam_checkpoint", type=str, required=True, help="path to checkpoint file" | |
) | |
parser.add_argument("--input_image", type=str, required=True, help="path to image file") | |
parser.add_argument( | |
"--output_dir", "-o", type=str, default="outputs", required=True, help="output directory" | |
) | |
parser.add_argument("--det_speech_file", type=str, help="grounding speech file") | |
parser.add_argument("--inpaint_speech_file", type=str, help="inpaint speech file") | |
parser.add_argument("--prompt_speech_file", type=str, help="prompt speech file, no need to provide det_speech_file") | |
parser.add_argument("--enable_chatgpt", action="store_true", help="enable chatgpt") | |
parser.add_argument("--openai_key", type=str, help="key for chatgpt") | |
parser.add_argument("--openai_proxy", default=None, type=str, help="proxy for chatgpt") | |
parser.add_argument("--whisper_model", type=str, default="small", help="whisper model version: tiny, base, small, medium, large") | |
parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold") | |
parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold") | |
parser.add_argument("--inpaint_mode", type=str, default="first", help="inpaint mode") | |
parser.add_argument("--device", type=str, default="cpu", help="running on cpu only!, default=False") | |
parser.add_argument("--prompt_extra", type=str, default=" high resolution, real scene", help="extra prompt for inpaint") | |
args = parser.parse_args() | |
# cfg | |
config_file = args.config # change the path of the model config file | |
grounded_checkpoint = args.grounded_checkpoint # change the path of the model | |
sam_checkpoint = args.sam_checkpoint | |
image_path = args.input_image | |
output_dir = args.output_dir | |
box_threshold = args.box_threshold | |
text_threshold = args.box_threshold | |
inpaint_mode = args.inpaint_mode | |
device = args.device | |
# make dir | |
os.makedirs(output_dir, exist_ok=True) | |
# load image | |
image_pil, image = load_image(image_path) | |
# load model | |
model = load_model(config_file, grounded_checkpoint, device=device) | |
# visualize raw image | |
image_pil.save(os.path.join(output_dir, "raw_image.jpg")) | |
# recognize speech | |
whisper_model = whisper.load_model(args.whisper_model) | |
if args.enable_chatgpt: | |
openai.api_key = args.openai_key | |
if args.openai_proxy: | |
openai.proxy = {"http": args.openai_proxy, "https": args.openai_proxy} | |
speech_text, _ = speech_recognition(args.prompt_speech_file, whisper_model) | |
det_prompt, inpaint_prompt = filter_prompts_with_chatgpt(speech_text) | |
inpaint_prompt += args.prompt_extra | |
print(f"det_prompt: {det_prompt}, inpaint_prompt: {inpaint_prompt}") | |
else: | |
det_prompt, det_speech_language = speech_recognition(args.det_speech_file, whisper_model) | |
inpaint_prompt, inpaint_speech_language = speech_recognition(args.inpaint_speech_file, whisper_model) | |
print(f"det_prompt: {det_prompt}, using language: {det_speech_language}") | |
print(f"inpaint_prompt: {inpaint_prompt}, using language: {inpaint_speech_language}") | |
# run grounding dino model | |
boxes_filt, pred_phrases = get_grounding_output( | |
model, image, det_prompt, box_threshold, text_threshold, device=device | |
) | |
# initialize SAM | |
predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint)) | |
image = cv2.imread(image_path) | |
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
predictor.set_image(image) | |
size = image_pil.size | |
H, W = size[1], size[0] | |
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] | |
boxes_filt = boxes_filt.cpu() | |
transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]) | |
masks, _, _ = predictor.predict_torch( | |
point_coords = None, | |
point_labels = None, | |
boxes = transformed_boxes, | |
multimask_output = False, | |
) | |
# masks: [1, 1, 512, 512] | |
# inpainting pipeline | |
if inpaint_mode == 'merge': | |
masks = torch.sum(masks, dim=0).unsqueeze(0) | |
masks = torch.where(masks > 0, True, False) | |
else: | |
mask = masks[0][0].cpu().numpy() # simply choose the first mask, which will be refine in the future release | |
mask_pil = Image.fromarray(mask) | |
image_pil = Image.fromarray(image) | |
pipe = StableDiffusionInpaintPipeline.from_pretrained( | |
"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16 | |
) | |
pipe = pipe.to("cuda") | |
# prompt = "A sofa, high quality, detailed" | |
image = pipe(prompt=inpaint_prompt, image=image_pil, mask_image=mask_pil).images[0] | |
image.save(os.path.join(output_dir, "grounded_sam_inpainting_output.jpg")) | |
# 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.numpy(), plt.gca(), label) | |
# plt.axis('off') | |
# plt.savefig(os.path.join(output_dir, "grounded_sam_output.jpg"), bbox_inches="tight") | |