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import warnings | |
warnings.filterwarnings('ignore') | |
import subprocess, io, os, sys, time | |
from loguru import logger | |
os.environ["CUDA_VISIBLE_DEVICES"] = "0" | |
if os.environ.get('IS_MY_DEBUG') is None: | |
result = subprocess.run(['pip', 'install', '-e', 'GroundingDINO'], check=True) | |
print(f'pip install GroundingDINO = {result}') | |
result = subprocess.run(['pip', 'list'], check=True) | |
print(f'pip list = {result}') | |
sys.path.insert(0, './GroundingDINO') | |
if not os.path.exists('./sam_vit_h_4b8939.pth'): | |
logger.info(f"get sam_vit_h_4b8939.pth...") | |
result = subprocess.run(['wget', 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth'], check=True) | |
print(f'wget sam_vit_h_4b8939.pth result = {result}') | |
import gradio as gr | |
import argparse | |
import copy | |
import numpy as np | |
import torch | |
from PIL import Image, ImageDraw, ImageFont, ImageOps | |
# 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 | |
import cv2 | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from lama_cleaner.model_manager import ModelManager | |
from lama_cleaner.schema import Config | |
# segment anything | |
from segment_anything import build_sam, SamPredictor, SamAutomaticMaskGenerator | |
# diffusers | |
import PIL | |
import requests | |
import torch | |
from io import BytesIO | |
from diffusers import StableDiffusionInpaintPipeline | |
from huggingface_hub import hf_hub_download | |
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=device) | |
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) | |
font = os.path.join(cv2.__path__[0],'qt','fonts','DejaVuSans.ttf') | |
font_size = 36 | |
new_font = ImageFont.truetype(font, font_size) | |
draw.text((x0+2, y0+2), str(label), font=new_font, fill="white") | |
mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6) | |
return image_pil, mask | |
def load_image(image_path): | |
# # load image | |
if isinstance(image_path, PIL.Image.Image): | |
image_pil = image_path | |
else: | |
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=device) #"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 xywh_to_xyxy(box, sizeW, sizeH): | |
if isinstance(box, list): | |
box = torch.Tensor(box) | |
box = box * torch.Tensor([sizeW, sizeH, sizeW, sizeH]) | |
box[:2] -= box[2:] / 2 | |
box[2:] += box[:2] | |
box = box.numpy() | |
return box | |
def mask_extend(img, box, extend_pixels=10, useRectangle=True): | |
box[0] = int(box[0]) | |
box[1] = int(box[1]) | |
box[2] = int(box[2]) | |
box[3] = int(box[3]) | |
region = img.crop(tuple(box)) | |
new_width = box[2] - box[0] + 2*extend_pixels | |
new_height = box[3] - box[1] + 2*extend_pixels | |
region_BILINEAR = region.resize((int(new_width), int(new_height))) | |
if useRectangle: | |
region_draw = ImageDraw.Draw(region_BILINEAR) | |
region_draw.rectangle((0, 0, new_width, new_height), fill=(255, 255, 255)) | |
img.paste(region_BILINEAR, (int(box[0]-extend_pixels), int(box[1]-extend_pixels))) | |
return img | |
def mix_masks(imgs): | |
re_img = 1 - np.asarray(imgs[0].convert("1")) | |
for i in range(len(imgs)-1): | |
re_img = np.multiply(re_img, 1 - np.asarray(imgs[i+1].convert("1"))) | |
re_img = 1 - re_img | |
return Image.fromarray(np.uint8(255*re_img)) | |
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 = evice = 'cuda' if torch.cuda.is_available() else 'cpu' | |
print(f'device={device}') | |
# make dir | |
os.makedirs(output_dir, exist_ok=True) | |
# initialize groundingdino model | |
logger.info(f"initialize groundingdino model...") | |
groundingdino_model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae) | |
# initialize SAM | |
logger.info(f"initialize SAM model...") | |
sam_model = build_sam(checkpoint=sam_checkpoint).to(device) | |
sam_predictor = SamPredictor(sam_model) | |
sam_mask_generator = SamAutomaticMaskGenerator(sam_model) | |
# initialize stable-diffusion-inpainting | |
logger.info(f"initialize stable-diffusion-inpainting...") | |
sd_pipe = None | |
if os.environ.get('IS_MY_DEBUG') is None: | |
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained( | |
"runwayml/stable-diffusion-inpainting", | |
torch_dtype=torch.float16 | |
) | |
sd_pipe = sd_pipe.to(device) | |
# initialize lama_cleaner | |
logger.info(f"initialize lama_cleaner...") | |
from lama_cleaner.helper import ( | |
load_img, | |
numpy_to_bytes, | |
resize_max_size, | |
) | |
lama_cleaner_model = ModelManager( | |
name='lama', | |
device='cpu', # device, | |
) | |
def lama_cleaner_process(image, mask): | |
ori_image = image | |
if mask.shape[0] == image.shape[1] and mask.shape[1] == image.shape[0] and mask.shape[0] != mask.shape[1]: | |
# rotate image | |
ori_image = np.transpose(image[::-1, ...][:, ::-1], axes=(1, 0, 2))[::-1, ...] | |
image = ori_image | |
original_shape = ori_image.shape | |
interpolation = cv2.INTER_CUBIC | |
size_limit = 1080 | |
if size_limit == "Original": | |
size_limit = max(image.shape) | |
else: | |
size_limit = int(size_limit) | |
config = Config( | |
ldm_steps=25, | |
ldm_sampler='plms', | |
zits_wireframe=True, | |
hd_strategy='Original', | |
hd_strategy_crop_margin=196, | |
hd_strategy_crop_trigger_size=1280, | |
hd_strategy_resize_limit=2048, | |
prompt='', | |
use_croper=False, | |
croper_x=0, | |
croper_y=0, | |
croper_height=512, | |
croper_width=512, | |
sd_mask_blur=5, | |
sd_strength=0.75, | |
sd_steps=50, | |
sd_guidance_scale=7.5, | |
sd_sampler='ddim', | |
sd_seed=42, | |
cv2_flag='INPAINT_NS', | |
cv2_radius=5, | |
) | |
if config.sd_seed == -1: | |
config.sd_seed = random.randint(1, 999999999) | |
# logger.info(f"Origin image shape_0_: {original_shape} / {size_limit}") | |
image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation) | |
# logger.info(f"Resized image shape_1_: {image.shape}") | |
# logger.info(f"mask image shape_0_: {mask.shape} / {type(mask)}") | |
mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation) | |
# logger.info(f"mask image shape_1_: {mask.shape} / {type(mask)}") | |
res_np_img = lama_cleaner_model(image, mask, config) | |
torch.cuda.empty_cache() | |
image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, 'png'))) | |
return image | |
# relate anything | |
from ram_utils import iou, sort_and_deduplicate, relation_classes, MLP, show_anns, show_mask | |
from ram_train_eval import RamModel,RamPredictor | |
from mmengine.config import Config | |
input_size = 512 | |
hidden_size = 256 | |
num_classes = 56 | |
# load ram model | |
model_path = "./checkpoints/ram_epoch12.pth" | |
config = dict( | |
model=dict( | |
pretrained_model_name_or_path='bert-base-uncased', | |
load_pretrained_weights=False, | |
num_transformer_layer=2, | |
input_feature_size=256, | |
output_feature_size=768, | |
cls_feature_size=512, | |
num_relation_classes=56, | |
pred_type='attention', | |
loss_type='multi_label_ce', | |
), | |
load_from=model_path, | |
) | |
config = Config(config) | |
class Ram_Predictor(RamPredictor): | |
def __init__(self, config, device='cpu'): | |
self.config = config | |
self.device = torch.device(device) | |
self._build_model() | |
def _build_model(self): | |
self.model = RamModel(**self.config.model).to(self.device) | |
if self.config.load_from is not None: | |
self.model.load_state_dict(torch.load(self.config.load_from, map_location=self.device)) | |
self.model.train() | |
ram_model = Ram_Predictor(config, device) | |
# visualization | |
def draw_selected_mask(mask, draw): | |
color = (255, 0, 0, 153) | |
nonzero_coords = np.transpose(np.nonzero(mask)) | |
for coord in nonzero_coords: | |
draw.point(coord[::-1], fill=color) | |
def draw_object_mask(mask, draw): | |
color = (0, 0, 255, 153) | |
nonzero_coords = np.transpose(np.nonzero(mask)) | |
for coord in nonzero_coords: | |
draw.point(coord[::-1], fill=color) | |
def create_title_image(word1, word2, word3, width, font_path='./assets/OpenSans-Bold.ttf'): | |
# Define the colors to use for each word | |
color_red = (255, 0, 0) | |
color_black = (0, 0, 0) | |
color_blue = (0, 0, 255) | |
# Define the initial font size and spacing between words | |
font_size = 40 | |
# Create a new image with the specified width and white background | |
image = Image.new('RGB', (width, 60), (255, 255, 255)) | |
# Load the specified font | |
font = ImageFont.truetype(font_path, font_size) | |
# Keep increasing the font size until all words fit within the desired width | |
while True: | |
# Create a draw object for the image | |
draw = ImageDraw.Draw(image) | |
word_spacing = font_size / 2 | |
# Draw each word in the appropriate color | |
x_offset = word_spacing | |
draw.text((x_offset, 0), word1, color_red, font=font) | |
x_offset += font.getsize(word1)[0] + word_spacing | |
draw.text((x_offset, 0), word2, color_black, font=font) | |
x_offset += font.getsize(word2)[0] + word_spacing | |
draw.text((x_offset, 0), word3, color_blue, font=font) | |
word_sizes = [font.getsize(word) for word in [word1, word2, word3]] | |
total_width = sum([size[0] for size in word_sizes]) + word_spacing * 3 | |
# Stop increasing font size if the image is within the desired width | |
if total_width <= width: | |
break | |
# Increase font size and reset the draw object | |
font_size -= 1 | |
image = Image.new('RGB', (width, 50), (255, 255, 255)) | |
font = ImageFont.truetype(font_path, font_size) | |
draw = None | |
return image | |
def concatenate_images_vertical(image1, image2): | |
# Get the dimensions of the two images | |
width1, height1 = image1.size | |
width2, height2 = image2.size | |
# Create a new image with the combined height and the maximum width | |
new_image = Image.new('RGBA', (max(width1, width2), height1 + height2)) | |
# Paste the first image at the top of the new image | |
new_image.paste(image1, (0, 0)) | |
# Paste the second image below the first image | |
new_image.paste(image2, (0, height1)) | |
return new_image | |
def relate_anything(input_image, k): | |
w, h = input_image.size | |
max_edge = 1500 | |
if w > max_edge or h > max_edge: | |
ratio = max(w, h) / max_edge | |
new_size = (int(w / ratio), int(h / ratio)) | |
input_image.thumbnail(new_size) | |
# load image | |
pil_image = input_image.convert('RGBA') | |
image = np.array(input_image) | |
sam_masks = sam_mask_generator.generate(image) | |
filtered_masks = sort_and_deduplicate(sam_masks) | |
feat_list = [] | |
for fm in filtered_masks: | |
feat = torch.Tensor(fm['feat']).unsqueeze(0).unsqueeze(0).to(device) | |
feat_list.append(feat) | |
feat = torch.cat(feat_list, dim=1).to(device) | |
matrix_output, rel_triplets = ram_model.predict(feat) | |
pil_image_list = [] | |
for i, rel in enumerate(rel_triplets[:k]): | |
s,o,r = int(rel[0]),int(rel[1]),int(rel[2]) | |
relation = relation_classes[r] | |
mask_image = Image.new('RGBA', pil_image.size, color=(0, 0, 0, 0)) | |
mask_draw = ImageDraw.Draw(mask_image) | |
draw_selected_mask(filtered_masks[s]['segmentation'], mask_draw) | |
draw_object_mask(filtered_masks[o]['segmentation'], mask_draw) | |
current_pil_image = pil_image.copy() | |
current_pil_image.alpha_composite(mask_image) | |
title_image = create_title_image('Red', relation, 'Blue', current_pil_image.size[0]) | |
concate_pil_image = concatenate_images_vertical(current_pil_image, title_image) | |
pil_image_list.append(concate_pil_image) | |
yield pil_image_list | |
mask_source_draw = "draw a mask on input image" | |
mask_source_segment = "type what to detect below" | |
def run_anything_task(input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, | |
iou_threshold, inpaint_mode, mask_source_radio, remove_mode, remove_mask_extend, num_relation): | |
if task_type == "relate anything": | |
return relate_anything(input_image['image'], num_relation) | |
text_prompt = text_prompt.strip() | |
if not ((task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_draw): | |
if text_prompt == '': | |
return [], gr.Gallery.update(label='Detection prompt is not found!😂😂😂😂') | |
if input_image is None: | |
return [], gr.Gallery.update(label='Please upload a image!😂😂😂😂') | |
file_temp = int(time.time()) | |
logger.info(f'run_anything_task_[{file_temp}]_{task_type}/{inpaint_mode}/[{mask_source_radio}]/{remove_mode}/{remove_mask_extend}_[{text_prompt}]/[{inpaint_prompt}]___1_') | |
# load image | |
input_mask_pil = input_image['mask'] | |
input_mask = np.array(input_mask_pil.convert("L")) | |
image_pil, image = load_image(input_image['image'].convert("RGB")) | |
# visualize raw image | |
# image_pil.save(os.path.join(output_dir, f"raw_image_{file_temp}.jpg")) | |
size = image_pil.size | |
output_images = [] | |
# run grounding dino model | |
if (task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_draw: | |
pass | |
else: | |
groundingdino_device = 'cpu' | |
if device != 'cpu': | |
try: | |
from groundingdino import _C | |
groundingdino_device = 'cuda:0' | |
except: | |
warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only in groundingdino!") | |
groundingdino_device = 'cpu' | |
boxes_filt, pred_phrases = get_grounding_output( | |
groundingdino_model, image, text_prompt, box_threshold, text_threshold, device=groundingdino_device | |
) | |
if boxes_filt.size(0) == 0: | |
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_[{text_prompt}]_1_[No objects detected, please try others.]_') | |
return [], gr.Gallery.update(label='No objects detected, please try others.😂😂😂😂') | |
boxes_filt_ori = copy.deepcopy(boxes_filt) | |
pred_dict = { | |
"boxes": boxes_filt, | |
"size": [size[1], size[0]], # H,W | |
"labels": pred_phrases, | |
} | |
image_with_box = plot_boxes_to_image(copy.deepcopy(image_pil), pred_dict)[0] | |
image_path = os.path.join(output_dir, f"grounding_dino_output_{file_temp}.jpg") | |
image_with_box.save(image_path) | |
detection_image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) | |
os.remove(image_path) | |
output_images.append(detection_image_result) | |
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_2_') | |
if task_type == 'segment' or ((task_type == 'inpainting' or task_type == 'remove') and mask_source_radio == mask_source_segment): | |
image = np.array(input_image['image']) | |
sam_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() | |
transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]) | |
masks, _, _ = sam_predictor.predict_torch( | |
point_coords = None, | |
point_labels = None, | |
boxes = transformed_boxes, | |
multimask_output = False, | |
) | |
# masks: [9, 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, f"grounding_seg_output_{file_temp}.jpg") | |
plt.savefig(image_path, bbox_inches="tight") | |
segment_image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) | |
os.remove(image_path) | |
output_images.append(segment_image_result) | |
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_3_') | |
if task_type == 'detection' or task_type == 'segment': | |
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_9_') | |
return output_images, gr.Gallery.update(label='result images') | |
elif task_type == 'inpainting' or task_type == 'remove': | |
if inpaint_prompt.strip() == '' and mask_source_radio == mask_source_segment: | |
task_type = 'remove' | |
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_4_') | |
if mask_source_radio == mask_source_draw: | |
mask_pil = input_mask_pil | |
mask = input_mask | |
else: | |
masks_ori = copy.deepcopy(masks) | |
if inpaint_mode == 'merge': | |
masks = torch.sum(masks, dim=0).unsqueeze(0) | |
masks = torch.where(masks > 0, True, False) | |
mask = masks[0][0].cpu().numpy() | |
mask_pil = Image.fromarray(mask) | |
image_path = os.path.join(output_dir, f"image_mask_{file_temp}.jpg") | |
# if reverse_mask: | |
# mask_pil = mask_pil.point(lambda _: 255-_) | |
mask_pil.convert("RGB").save(image_path) | |
image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) | |
os.remove(image_path) | |
output_images.append(image_result) | |
if task_type == 'inpainting': | |
# inpainting pipeline | |
image_source_for_inpaint = image_pil.resize((512, 512)) | |
image_mask_for_inpaint = mask_pil.resize((512, 512)) | |
image_inpainting = sd_pipe(prompt=inpaint_prompt, image=image_source_for_inpaint, mask_image=image_mask_for_inpaint).images[0] | |
else: | |
# remove from mask | |
if mask_source_radio == mask_source_segment: | |
mask_imgs = [] | |
masks_shape = masks_ori.shape | |
boxes_filt_ori_array = boxes_filt_ori.numpy() | |
if inpaint_mode == 'merge': | |
extend_shape_0 = masks_shape[0] | |
extend_shape_1 = masks_shape[1] | |
else: | |
extend_shape_0 = 1 | |
extend_shape_1 = 1 | |
for i in range(extend_shape_0): | |
for j in range(extend_shape_1): | |
mask = masks_ori[i][j].cpu().numpy() | |
mask_pil = Image.fromarray(mask) | |
if remove_mode == 'segment': | |
useRectangle = False | |
else: | |
useRectangle = True | |
try: | |
remove_mask_extend = int(remove_mask_extend) | |
except: | |
remove_mask_extend = 10 | |
mask_pil_exp = mask_extend(copy.deepcopy(mask_pil).convert("RGB"), | |
xywh_to_xyxy(torch.tensor(boxes_filt_ori_array[i]), size[0], size[1]), | |
extend_pixels=remove_mask_extend, useRectangle=useRectangle) | |
mask_imgs.append(mask_pil_exp) | |
mask_pil = mix_masks(mask_imgs) | |
image_path = os.path.join(output_dir, f"image_mask_{file_temp}.jpg") | |
# if reverse_mask: | |
# mask_pil = mask_pil.point(lambda _: 255-_) | |
mask_pil.convert("RGB").save(image_path) | |
image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) | |
os.remove(image_path) | |
output_images.append(image_result) | |
image_inpainting = lama_cleaner_process(np.array(image_pil), np.array(mask_pil.convert("L"))) | |
image_inpainting = image_inpainting.resize((image_pil.size[0], image_pil.size[1])) | |
image_path = os.path.join(output_dir, f"grounded_sam_inpainting_output_{file_temp}.jpg") | |
image_inpainting.save(image_path) | |
image_result = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB) | |
os.remove(image_path) | |
logger.info(f'run_anything_task_[{file_temp}]_{task_type}_9_') | |
output_images.append(image_result) | |
return output_images, gr.Gallery.update(label='result images') | |
else: | |
logger.info(f"task_type:{task_type} error!") | |
logger.info(f'run_anything_task_[{file_temp}]_9_9_') | |
return output_images, gr.Gallery.update(label='result images') | |
def change_radio_display(task_type, mask_source_radio, num_relation): #, gsa_gallery, ram_gallery): | |
text_prompt_visible = True | |
inpaint_prompt_visible = False | |
mask_source_radio_visible = False | |
num_relation_visible = False | |
# gsa_gallery_visible = True | |
# ram_gallery_visible = False | |
if task_type == "inpainting": | |
inpaint_prompt_visible = True | |
if task_type == "inpainting" or task_type == "remove": | |
mask_source_radio_visible = True | |
if mask_source_radio == mask_source_draw: | |
text_prompt_visible = False | |
if task_type == "relate anything": | |
text_prompt_visible = False | |
num_relation_visible = True | |
# gsa_gallery_visible = False | |
# ram_gallery_visible = True | |
return gr.Textbox.update(visible=text_prompt_visible), gr.Textbox.update(visible=inpaint_prompt_visible), gr.Radio.update(visible=mask_source_radio_visible), gr.Slider.update(visible=num_relation_visible) | |
# gr.Gallery.update(visible=gsa_gallery_visible), gr.Gallery.update(visible=ram_gallery_visible) | |
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() | |
print(f'args = {args}') | |
block = gr.Blocks().queue() | |
with block: | |
with gr.Row(): | |
with gr.Column(): | |
input_image = gr.Image(source='upload', elem_id="image_upload", tool='sketch', type='pil', label="Upload") | |
task_type = gr.Radio(["detection", "segment", "inpainting", "remove", "relate anything"], value="detection", | |
label='Task type', visible=True) | |
mask_source_radio = gr.Radio([mask_source_draw, mask_source_segment], | |
value=mask_source_segment, label="Mask from", | |
visible=False) | |
text_prompt = gr.Textbox(label="Detection Prompt[To detect multiple objects, seperating each name with '.', like this: cat . dog . chair ]", placeholder="Cannot be empty") | |
inpaint_prompt = gr.Textbox(label="Inpaint Prompt (if this is empty, then remove)", visible=False) | |
num_relation = gr.Slider(label="How many relations do you want to see", minimum=1, maximum=20, value=5, step=1, visible=False) | |
run_button = gr.Button(label="Run") | |
with gr.Accordion("Advanced options", open=False) as advanced_options: | |
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=0.8, step=0.001 | |
) | |
inpaint_mode = gr.Radio(["merge", "first"], value="merge", label="inpaint_mode") | |
with gr.Row(): | |
with gr.Column(scale=1): | |
remove_mode = gr.Radio(["segment", "rectangle"], value="segment", label='remove mode') | |
with gr.Column(scale=1): | |
remove_mask_extend = gr.Textbox(label="remove_mask_extend", value='10') | |
with gr.Column(): | |
# gsa_gallery = gr.Gallery( | |
# label="result images", show_label=True, elem_id="gsa_gallery" | |
# ).style(grid=[2], full_width=True, full_height=True) | |
gallery = gr.Gallery(label="Your Result", show_label=True, elem_id="gallery").style(preview=True, columns=5, object_fit="scale-down") | |
run_button.click(fn=run_anything_task, inputs=[ | |
input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, iou_threshold, inpaint_mode, mask_source_radio, remove_mode, remove_mask_extend, num_relation], outputs=[gallery, gallery]) | |
task_type.change(fn=change_radio_display, inputs=[task_type, mask_source_radio], outputs=[text_prompt, inpaint_prompt, mask_source_radio, num_relation]) | |
mask_source_radio.change(fn=change_radio_display, inputs=[task_type, mask_source_radio], outputs=[text_prompt, inpaint_prompt, mask_source_radio, num_relation]) | |
DESCRIPTION = '### This demo from [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything). Thanks for their excellent work.' | |
DESCRIPTION += f'<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/yizhangliu/Grounded-Segment-Anything?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>' | |
gr.Markdown(DESCRIPTION) | |
block.launch(server_name='0.0.0.0', debug=args.debug, share=args.share) | |