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import gradio as gr | |
import json | |
import argparse | |
import os | |
import copy | |
import numpy as np | |
import torch | |
import torchvision | |
from PIL import Image, ImageDraw, ImageFont | |
import openai | |
# 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 | |
from transformers import BlipProcessor, BlipForConditionalGeneration | |
# segment anything | |
from segment_anything import build_sam, SamPredictor | |
from segment_anything.utils.amg import remove_small_regions | |
import cv2 | |
import numpy as np | |
import matplotlib.pyplot as plt | |
# diffusers | |
import PIL | |
import requests | |
import torch | |
from io import BytesIO | |
from huggingface_hub import hf_hub_download | |
from sys import platform | |
#macos | |
if platform == 'darwin': | |
import matplotlib | |
matplotlib.use('agg') | |
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='cpu') | |
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) | |
draw.text((x0, y0), str(label), fill="white") | |
mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6) | |
return image_pil, mask | |
def load_image(image_path): | |
# # load image | |
# image_pil = Image.open(image_path).convert("RGB") # load image | |
image_pil = image_path | |
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) | |
_ = 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 = [] | |
scores = [] | |
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) | |
scores.append(logit.max().item()) | |
return boxes_filt, torch.Tensor(scores), 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 save_mask_data(output_dir, mask_list, box_list, label_list): | |
value = 0 # 0 for background | |
mask_img = torch.zeros(mask_list.shape[-2:]) | |
for idx, mask in enumerate(mask_list): | |
mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1 | |
plt.figure(figsize=(10, 10)) | |
plt.imshow(mask_img.numpy()) | |
plt.axis('off') | |
mask_img_path = os.path.join(output_dir, 'mask.jpg') | |
plt.savefig(mask_img_path, bbox_inches="tight", dpi=300, pad_inches=0.0) | |
json_data = [{ | |
'value': value, | |
'label': 'background' | |
}] | |
for label, box in zip(label_list, box_list): | |
value += 1 | |
name, logit = label.split('(') | |
logit = logit[:-1] # the last is ')' | |
json_data.append({ | |
'value': value, | |
'label': name, | |
'logit': float(logit), | |
'box': box.numpy().tolist(), | |
}) | |
mask_json_path = os.path.join(output_dir, 'mask.json') | |
with open(mask_json_path, 'w') as f: | |
json.dump(json_data, f) | |
return mask_img_path, mask_json_path | |
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) | |
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" | |
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") | |
def generate_caption(raw_image): | |
# unconditional image captioning | |
inputs = processor(raw_image, return_tensors="pt") | |
out = blip_model.generate(**inputs) | |
caption = processor.decode(out[0], skip_special_tokens=True) | |
return caption | |
def generate_tags(caption, split=',', max_tokens=100, model="gpt-3.5-turbo", openai_key=''): | |
openai.api_key = openai_key | |
prompt = [ | |
{ | |
'role': 'system', | |
'content': 'Extract the unique nouns in the caption. Remove all the adjectives. ' + \ | |
f'List the nouns in singular form. Split them by "{split} ". ' + \ | |
f'Caption: {caption}.' | |
} | |
] | |
response = openai.ChatCompletion.create(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens) | |
reply = response['choices'][0]['message']['content'] | |
# sometimes return with "noun: xxx, xxx, xxx" | |
tags = reply.split(':')[-1].strip() | |
return tags | |
def check_caption(caption, pred_phrases, max_tokens=100, model="gpt-3.5-turbo"): | |
object_list = [obj.split('(')[0] for obj in pred_phrases] | |
object_num = [] | |
for obj in set(object_list): | |
object_num.append(f'{object_list.count(obj)} {obj}') | |
object_num = ', '.join(object_num) | |
print(f"Correct object number: {object_num}") | |
prompt = [ | |
{ | |
'role': 'system', | |
'content': 'Revise the number in the caption if it is wrong. ' + \ | |
f'Caption: {caption}. ' + \ | |
f'True object number: {object_num}. ' + \ | |
'Only give the revised caption: ' | |
} | |
] | |
response = openai.ChatCompletion.create(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens) | |
reply = response['choices'][0]['message']['content'] | |
# sometimes return with "Caption: xxx, xxx, xxx" | |
caption = reply.split(':')[-1].strip() | |
return caption | |
def run_grounded_sam(image_path, openai_key, box_threshold, text_threshold, iou_threshold, area_threshold): | |
assert openai_key, 'Openai key is not found!' | |
# make dir | |
os.makedirs(output_dir, exist_ok=True) | |
# load image | |
image_pil, image = load_image(image_path.convert("RGB")) | |
# load model | |
model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae) | |
# visualize raw image | |
image_pil.save(os.path.join(output_dir, "raw_image.jpg")) | |
caption = generate_caption(image_pil) | |
# Currently ", " is better for detecting single tags | |
# while ". " is a little worse in some case | |
split = ',' | |
tags = generate_tags(caption, split=split, openai_key=openai_key) | |
# run grounding dino model | |
boxes_filt, scores, pred_phrases = get_grounding_output( | |
model, image, tags, box_threshold, text_threshold, device=device | |
) | |
size = image_pil.size | |
# initialize SAM | |
predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint)) | |
image = np.array(image_path) | |
predictor.set_image(image) | |
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() | |
# use NMS to handle overlapped boxes | |
print(f"Before NMS: {boxes_filt.shape[0]} boxes") | |
nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist() | |
boxes_filt = boxes_filt[nms_idx] | |
pred_phrases = [pred_phrases[idx] for idx in nms_idx] | |
print(f"After NMS: {boxes_filt.shape[0]} boxes") | |
caption = check_caption(caption, pred_phrases) | |
print(f"Revise caption with number: {caption}") | |
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, | |
) | |
# area threshold: remove the mask when area < area_thresh (in pixels) | |
new_masks = [] | |
for mask in masks: | |
# reshape to be used in remove_small_regions() | |
mask = mask.cpu().numpy().squeeze() | |
mask, _ = remove_small_regions(mask, area_threshold, mode="holes") | |
mask, _ = remove_small_regions(mask, area_threshold, mode="islands") | |
new_masks.append(torch.as_tensor(mask).unsqueeze(0)) | |
masks = torch.stack(new_masks, dim=0) | |
# masks: [1, 1, 512, 512] | |
assert sam_checkpoint, 'sam_checkpoint is not found!' | |
# 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') | |
image_path = os.path.join(output_dir, "grounding_dino_output.jpg") | |
plt.savefig(image_path, bbox_inches="tight") | |
image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) | |
mask_img_path, _ = save_mask_data('./outputs', masks, boxes_filt, pred_phrases) | |
mask_img = cv2.cvtColor(cv2.imread(mask_img_path), cv2.COLOR_BGR2RGB) | |
return image_result, mask_img, caption, tags | |
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") | |
args = parser.parse_args() | |
block = gr.Blocks().queue() | |
with block: | |
with gr.Row(): | |
with gr.Column(): | |
input_image = gr.Image(source='upload', type="pil") | |
openai_key = gr.Textbox(label="OpenAI key") | |
run_button = gr.Button(label="Run") | |
with gr.Accordion("Advanced options", open=False): | |
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=0.5, step=0.001 | |
) | |
area_threshold = gr.Slider( | |
label="Area Threshold", minimum=0.0, maximum=2500, value=100, step=10 | |
) | |
with gr.Column(): | |
image_caption = gr.Textbox(label="Image Caption") | |
identified_labels = gr.Textbox(label="Key objects extracted by ChatGPT") | |
gallery = gr.outputs.Image( | |
type="pil", | |
).style(full_width=True, full_height=True) | |
mask_gallary = gr.outputs.Image( | |
type="pil", | |
).style(full_width=True, full_height=True) | |
run_button.click(fn=run_grounded_sam, inputs=[ | |
input_image, openai_key, box_threshold, text_threshold, iou_threshold, area_threshold], | |
outputs=[gallery, mask_gallary, image_caption, identified_labels]) | |
block.launch(server_name='0.0.0.0', server_port=7589, debug=args.debug, share=args.share) |