try:
import detectron2
except:
import os
os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
# os.system('git clone https://github.com/facebookresearch/detectron2.git')
# os.system('python -m pip install -e detectron2')
import gradio as gr
import numpy as np
import cv2
import torch
from detectron2.config import get_cfg
from GLEE.glee.models.glee_model import GLEE_Model
from GLEE.glee.config_deeplab import add_deeplab_config
from GLEE.glee.config import add_glee_config
import torch.nn.functional as F
import torchvision
import math
from obj365_name import categories as OBJ365_CATEGORIESV2
print(f"Is CUDA available: {torch.cuda.is_available()}")
# True
if torch.cuda.is_available():
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
# Tesla T4
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
(x_c + 0.5 * w), (y_c + 0.5 * h)]
return torch.stack(b, dim=-1)
def scribble2box(img):
if img.max()==0:
return None, None
rows = np.any(img, axis=1)
cols = np.any(img, axis=0)
all = np.any(img,axis=2)
R,G,B,A = img[np.where(all)[0][0],np.where(all)[1][0]].tolist() # get color
ymin, ymax = np.where(rows)[0][[0, -1]]
xmin, xmax = np.where(cols)[0][[0, -1]]
return np.array([ xmin,ymin, xmax,ymax]), (R,G,B)
def LSJ_box_postprocess( out_bbox, padding_size, crop_size, img_h, img_w):
# postprocess box height and width
boxes = box_cxcywh_to_xyxy(out_bbox)
lsj_sclae = torch.tensor([padding_size[1], padding_size[0], padding_size[1], padding_size[0]]).to(out_bbox)
crop_scale = torch.tensor([crop_size[1], crop_size[0], crop_size[1], crop_size[0]]).to(out_bbox)
boxes = boxes * lsj_sclae
boxes = boxes / crop_scale
boxes = torch.clamp(boxes,0,1)
scale_fct = torch.tensor([img_w, img_h, img_w, img_h])
scale_fct = scale_fct.to(out_bbox)
boxes = boxes * scale_fct
return boxes
COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125],
[0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933],
[0.494, 0.000, 0.556], [0.494, 0.000, 0.000], [0.000, 0.745, 0.000],
[0.700, 0.300, 0.600],[0.000, 0.447, 0.741], [0.850, 0.325, 0.098]]
def load_smallmodel():
cfg_r50 = get_cfg()
add_deeplab_config(cfg_r50)
add_glee_config(cfg_r50)
conf_files_r50 = 'GLEE/configs/R50.yaml'
checkpoints_r50 = torch.load('GLEE_R50_Scaleup10m.pth')
cfg_r50.merge_from_file(conf_files_r50)
GLEEmodel_r50 = GLEE_Model(cfg_r50, None, device, None, True).to(device)
GLEEmodel_r50.load_state_dict(checkpoints_r50, strict=False)
GLEEmodel_r50.eval()
return GLEEmodel_r50
def load_bigmodel():
cfg_swin = get_cfg()
add_deeplab_config(cfg_swin)
add_glee_config(cfg_swin)
conf_files_swin = 'GLEE/configs/SwinL.yaml'
checkpoints_swin = torch.load('GLEE_SwinL_Scaleup10m.pth')
cfg_swin.merge_from_file(conf_files_swin)
GLEEmodel_swin = GLEE_Model(cfg_swin, None, device, None, True).to(device)
GLEEmodel_swin.load_state_dict(checkpoints_swin, strict=False)
GLEEmodel_swin.eval()
return GLEEmodel_swin
GLEEmodel_swin = load_bigmodel()
GLEEmodel_r50 = load_smallmodel()
coco_class_name = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
OBJ365_class_names = [cat['name'] for cat in OBJ365_CATEGORIESV2]
class_agnostic_name = ['object']
if torch.cuda.is_available():
print('use cuda')
device = 'cuda'
else:
print('use cpu')
device='cpu'
pixel_mean = torch.Tensor( [123.675, 116.28, 103.53]).to(device).view(3, 1, 1)
pixel_std = torch.Tensor([58.395, 57.12, 57.375]).to(device).view(3, 1, 1)
normalizer = lambda x: (x - pixel_mean) / pixel_std
inference_size = 800
inference_type = 'resize_shot' # or LSJ
size_divisibility = 32
FONT_SCALE = 1.5e-3
THICKNESS_SCALE = 1e-3
TEXT_Y_OFFSET_SCALE = 1e-2
if inference_type != 'LSJ':
resizer = torchvision.transforms.Resize(inference_size)
def segment_image(img,prompt_mode, categoryname, custom_category, expressiong, results_select, num_inst_select, threshold_select, mask_image_mix_ration, model_selection):
if model_selection == 'GLEE-Plus (SwinL)':
GLEEmodel = GLEEmodel_swin
print('use GLEE-Plus')
else:
GLEEmodel = GLEEmodel_r50
print('use GLEE-Lite')
copyed_img = img['background'][:,:,:3].copy()
ori_image = torch.as_tensor(np.ascontiguousarray( copyed_img.transpose(2, 0, 1)))
ori_image = normalizer(ori_image.to(device))[None,]
_,_, ori_height, ori_width = ori_image.shape
if inference_type == 'LSJ':
infer_image = torch.zeros(1,3,1024,1024).to(ori_image)
infer_image[:,:,:inference_size,:inference_size] = ori_image
else:
resize_image = resizer(ori_image)
image_size = torch.as_tensor((resize_image.shape[-2],resize_image.shape[-1]))
re_size = resize_image.shape[-2:]
if size_divisibility > 1:
stride = size_divisibility
# the last two dims are H,W, both subject to divisibility requirement
padding_size = ((image_size + (stride - 1)).div(stride, rounding_mode="floor") * stride).tolist()
infer_image = torch.zeros(1,3,padding_size[0],padding_size[1]).to(resize_image)
infer_image[0,:,:image_size[0],:image_size[1]] = resize_image
# reversed_image = infer_image*pixel_std + pixel_mean
# reversed_image = torch.clip(reversed_image,min=0,max=255)
# reversed_image = reversed_image[0].permute(1,2,0)
# reversed_image = reversed_image.int().cpu().numpy().copy()
# cv2.imwrite('test.png',reversed_image[:,:,::-1])
if prompt_mode == 'categories' or prompt_mode == 'expression':
if len(results_select)==0:
results_select=['box']
if prompt_mode == 'categories':
if categoryname =="COCO-80":
batch_category_name = coco_class_name
elif categoryname =="OBJ365":
batch_category_name = OBJ365_class_names
elif categoryname =="Custom-List":
batch_category_name = custom_category.split(',')
else:
batch_category_name = class_agnostic_name
# mask_ori = torch.from_numpy(np.load('03_moto_mask.npy'))[None,]
# mask_ori = (F.interpolate(mask_ori, (height, width), mode='bilinear') > 0).to(device)
# prompt_list = [mask_ori[0]]
prompt_list = []
with torch.no_grad():
(outputs,_) = GLEEmodel(infer_image, prompt_list, task="coco", batch_name_list=batch_category_name, is_train=False)
topK_instance = max(num_inst_select,1)
else:
topK_instance = 1
prompt_list = {'grounding':[expressiong]}
with torch.no_grad():
(outputs,_) = GLEEmodel(infer_image, prompt_list, task="grounding", batch_name_list=[], is_train=False)
mask_pred = outputs['pred_masks'][0]
mask_cls = outputs['pred_logits'][0]
boxes_pred = outputs['pred_boxes'][0]
scores = mask_cls.sigmoid().max(-1)[0]
scores_per_image, topk_indices = scores.topk(topK_instance, sorted=True)
if prompt_mode == 'categories':
valid = scores_per_image>threshold_select
topk_indices = topk_indices[valid]
scores_per_image = scores_per_image[valid]
pred_class = mask_cls[topk_indices].max(-1)[1].tolist()
pred_boxes = boxes_pred[topk_indices]
boxes = LSJ_box_postprocess(pred_boxes,padding_size,re_size, ori_height,ori_width)
mask_pred = mask_pred[topk_indices]
pred_masks = F.interpolate( mask_pred[None,], size=(padding_size[0], padding_size[1]), mode="bilinear", align_corners=False )
pred_masks = pred_masks[:,:,:re_size[0],:re_size[1]]
pred_masks = F.interpolate( pred_masks, size=(ori_height,ori_width), mode="bilinear", align_corners=False )
pred_masks = (pred_masks>0).detach().cpu().numpy()[0]
if 'mask' in results_select:
zero_mask = np.zeros_like(copyed_img)
for nn, mask in enumerate(pred_masks):
# mask = mask.numpy()
mask = mask.reshape(mask.shape[0], mask.shape[1], 1)
lar = np.concatenate((mask*COLORS[nn%12][2], mask*COLORS[nn%12][1], mask*COLORS[nn%12][0]), axis = 2)
zero_mask = zero_mask+ lar
lar_valid = zero_mask>0
masked_image = lar_valid*copyed_img
img_n = masked_image*mask_image_mix_ration + np.clip(zero_mask,0,1)*255*(1-mask_image_mix_ration)
max_p = img_n.max()
img_n = 255*img_n/max_p
ret = (~lar_valid*copyed_img)*mask_image_mix_ration + img_n
ret = ret.astype('uint8')
else:
ret = copyed_img
if 'box' in results_select:
line_width = max(ret.shape) /200
for nn,(classid, box) in enumerate(zip(pred_class,boxes)):
x1,y1,x2,y2 = box.long().tolist()
RGB = (COLORS[nn%12][2]*255,COLORS[nn%12][1]*255,COLORS[nn%12][0]*255)
cv2.rectangle(ret, (x1,y1), (x2,y2), RGB, math.ceil(line_width) )
if prompt_mode == 'categories' or (prompt_mode == 'expression' and 'expression' in results_select ):
if prompt_mode == 'categories':
label = ''
if 'name' in results_select:
label += batch_category_name[classid]
if 'score' in results_select:
label += str(scores_per_image[nn].item())[:4]
else:
label = expressiong
if len(label)==0:
continue
height, width, _ = ret.shape
FONT = cv2.FONT_HERSHEY_COMPLEX
label_width, label_height = cv2.getTextSize(label, FONT, min(width, height) * FONT_SCALE, math.ceil(min(width, height) * THICKNESS_SCALE))[0]
cv2.rectangle(ret, (x1,y1), (x1+label_width,(y1 -label_height) - int(height * TEXT_Y_OFFSET_SCALE)), RGB, -1)
cv2.putText(
ret,
label,
(x1, y1 - int(height * TEXT_Y_OFFSET_SCALE)),
fontFace=FONT,
fontScale=min(width, height) * FONT_SCALE,
thickness=math.ceil(min(width, height) * THICKNESS_SCALE),
color=(255,255,255),
)
ret = ret.astype('uint8')
return ret
else: #visual prompt
topK_instance = 1
copyed_img = img['background'][:,:,:3].copy()
# get bbox from scribbles in layers
bbox_list = [scribble2box(layer) for layer in img['layers'] ]
visual_prompt_list = []
visual_prompt_RGB_list = []
for mask, (box,RGB) in zip(img['layers'], bbox_list):
if box is None:
continue
if prompt_mode=='box':
fakemask = np.zeros_like(copyed_img[:,:,0])
x1 ,y1 ,x2, y2 = box
fakemask[ y1:y2, x1:x2 ] = 1
fakemask = fakemask>0
elif prompt_mode=='point':
fakemask = np.zeros_like(copyed_img[:,:,0])
H,W = fakemask.shape
x1 ,y1 ,x2, y2 = box
center_x, center_y = (x1+x2)//2, (y1+y2)//2
fakemask[ center_y-H//40:center_y+H//40, center_x-W//40:center_x+W//40 ] = 1
fakemask = fakemask>0
elif prompt_mode=='scribble':
fakemask = mask[:,:,-1]
fakemask = fakemask>0
fakemask = torch.from_numpy(fakemask).unsqueeze(0).to(ori_image)
if inference_type == 'LSJ':
infer_visual_prompt = torch.zeros(1,1024,1024).to(ori_image)
infer_visual_prompt[:,:inference_size,:inference_size] = fakemask
else:
resize_fakemask = resizer(fakemask)
if size_divisibility > 1:
# the last two dims are H,W, both subject to divisibility requirement
infer_visual_prompt = torch.zeros(1,padding_size[0],padding_size[1]).to(resize_fakemask)
infer_visual_prompt[:,:image_size[0],:image_size[1]] = resize_fakemask
visual_prompt_list.append( infer_visual_prompt>0 )
visual_prompt_RGB_list.append(RGB)
mask_results_list = []
for visual_prompt in visual_prompt_list:
prompt_list = {'spatial':[visual_prompt]}
with torch.no_grad():
(outputs,_) = GLEEmodel(infer_image, prompt_list, task="coco", batch_name_list=['object'], is_train=False, visual_prompt_type=prompt_mode )
mask_pred = outputs['pred_masks'][0]
mask_cls = outputs['pred_logits'][0]
boxes_pred = outputs['pred_boxes'][0]
scores = mask_cls.sigmoid().max(-1)[0]
scores_per_image, topk_indices = scores.topk(topK_instance, sorted=True)
pred_class = mask_cls[topk_indices].max(-1)[1].tolist()
pred_boxes = boxes_pred[topk_indices]
boxes = LSJ_box_postprocess(pred_boxes,padding_size,re_size, ori_height,ori_width)
mask_pred = mask_pred[topk_indices]
pred_masks = F.interpolate( mask_pred[None,], size=(padding_size[0], padding_size[1]), mode="bilinear", align_corners=False )
pred_masks = pred_masks[:,:,:re_size[0],:re_size[1]]
pred_masks = F.interpolate( pred_masks, size=(ori_height,ori_width), mode="bilinear", align_corners=False )
pred_masks = (pred_masks>0).detach().cpu().numpy()[0]
mask_results_list.append(pred_masks)
zero_mask = np.zeros_like(copyed_img)
for mask,RGB in zip(mask_results_list,visual_prompt_RGB_list):
mask = mask.reshape(mask.shape[-2], mask.shape[-1], 1)
lar = np.concatenate((mask*RGB[0], mask*RGB[1],mask*RGB[2]), axis = 2)
zero_mask = zero_mask+ lar
lar_valid = zero_mask>0
masked_image = lar_valid*copyed_img
img_n = masked_image*mask_image_mix_ration + np.clip(zero_mask,0,255)*(1-mask_image_mix_ration)
max_p = img_n.max()
img_n = 255*img_n/max_p
ret = (~lar_valid*copyed_img)*mask_image_mix_ration + img_n
ret = ret.astype('uint8')
# cv2.imwrite('00020_inst.jpg', cv2.cvtColor(ret, cv2.COLOR_BGR2RGB))
return ret
# def get_select_coordinates(img):
# # img{'background': (H,W,3)
# # 'layers': list[ (H,W,4(RGBA)) ], draw map
# # 'composite': (H,W,4(RGBA))} ori_img concat drow
# ori_img = img['background'][:,:,:3].copy()
# # get bbox from scribbles in layers
# bbox_list = [scribble2box(layer) for layer in img['layers'] ]
# for mask, (box,RGB) in zip(img['layers'], bbox_list):
# if box is None:
# continue
# cv2.rectangle(ori_img, (box[0],box[1]), (box[2],box[3]),RGB, 3)
# return ori_img
def visual_prompt_preview(img, prompt_mode):
copyed_img = img['background'][:,:,:3].copy()
# get bbox from scribbles in layers
bbox_list = [scribble2box(layer) for layer in img['layers'] ]
zero_mask = np.zeros_like(copyed_img)
for mask, (box,RGB) in zip(img['layers'], bbox_list):
if box is None:
continue
if prompt_mode=='box':
fakemask = np.zeros_like(copyed_img[:,:,0])
x1 ,y1 ,x2, y2 = box
fakemask[ y1:y2, x1:x2 ] = 1
fakemask = fakemask>0
elif prompt_mode=='point':
fakemask = np.zeros_like(copyed_img[:,:,0])
H,W = fakemask.shape
x1 ,y1 ,x2, y2 = box
center_x, center_y = (x1+x2)//2, (y1+y2)//2
fakemask[ center_y-H//40:center_y+H//40, center_x-W//40:center_x+W//40 ] = 1
fakemask = fakemask>0
else:
fakemask = mask[:,:,-1]
fakemask = fakemask>0
mask = fakemask.reshape(fakemask.shape[0], fakemask.shape[1], 1)
lar = np.concatenate((mask*RGB[0], mask*RGB[1],mask*RGB[2]), axis = 2)
zero_mask = zero_mask+ lar
img_n = copyed_img + np.clip(zero_mask,0,255)
max_p = img_n.max()
ret = 255*img_n/max_p
ret = ret.astype('uint8')
return ret
with gr.Blocks() as demo:
gr.Markdown('# GLEE: General Object Foundation Model for Images and Videos at Scale')
gr.Markdown('## [Paper](https://arxiv.org/abs/2312.09158) - [Project Page](https://glee-vision.github.io) - [Code](https://github.com/FoundationVision/GLEE) ')
gr.Markdown(
'**The functionality demonstration demo app of GLEE. Select a Tab for image or video tasks. Image tasks includes arbitrary vocabulary object detection&segmentation, any form of object name or object caption detection, referring expression comprehension, and interactive segmentation. Video tasks add object tracking functionality based on image tasks.**'
)
with gr.Tab("Image task"):
with gr.Row():
with gr.Column():
img_input = gr.ImageEditor()
model_select = gr.Dropdown(
["GLEE-Lite (R50)", "GLEE-Plus (SwinL)"], value = "GLEE-Lite (R50)" , multiselect=False, label="Model",
)
with gr.Row():
with gr.Column():
prompt_mode_select = gr.Radio(["point", "scribble", "box", "categories", "expression"], label="Prompt", value= "categories" , info="What kind of prompt do you want to use?")
category_select = gr.Dropdown(
["COCO-80", "OBJ365", "Custom-List", "Class-Agnostic"], value = "COCO-80" , multiselect=False, label="Categories", info="Choose an existing category list or class-agnostic"
)
custom_category = gr.Textbox(
label="Custom Category",
info="Input custom category list, seperate by ',' ",
lines=1,
value="dog, cat, car, person",
)
input_expressiong = gr.Textbox(
label="Expression",
info="Input any description of an object in the image ",
lines=2,
value="the red car",
)
# with gr.Column():
with gr.Group():
with gr.Accordion("Interactive segmentation usage",open=False):
gr.Markdown(
'For interactive segmentation:
\
1.Draw points, boxes, or scribbles on the canvas for multiclass segmentation; use separate layers for different objects, adding layers with a "+" sign.
\
2.Point mode accepts a single point only; multiple points default to the centroid, so use boxes or scribbles for larger objects.
\
3.After drawing, click green "√" on the right side of the image to preview the prompt visualization; the segmentation mask follows the chosen prompt colors.'
)
with gr.Accordion("Text based detection usage",open=False):
gr.Markdown(
'GLEE supports three kind of object perception methods: category list, textual description, and class-agnostic.
\
1.Select an existing category list from the "Categories" dropdown, like COCO or OBJ365, or customize your own list.
\
2.Enter arbitrary object name in "Custom Category", or choose the expression model and describe the object in "Expression Textbox" for single object detection only.
\
3.For class-agnostic mode, choose "Class-Agnostic" from the "Categories" dropdown.'
)
img_showbox = gr.Image(label="visual prompt area preview")
with gr.Column():
image_segment = gr.Image(label="detection and segmentation results")
with gr.Accordion("Try More Visualization Options"):
results_select = gr.CheckboxGroup(["box", "mask", "name", "score", "expression"], value=["box", "mask", "name", "score"], label="Shown Results", info="The results shown on image")
num_inst_select = gr.Slider(1, 50, value=15, step=1, label="Num of topK instances for category based detection", info="Choose between 1 and 50 for better visualization")
threshold_select = gr.Slider(0, 1, value=0.2, label="Confidence Threshold", info="Choose threshold ")
mask_image_mix_ration = gr.Slider(0, 1, value=0.45, label="Image Brightness Ratio", info="Brightness between image and colored masks ")
image_button = gr.Button("Detect & Segment")
img_input.change(visual_prompt_preview, inputs = [img_input,prompt_mode_select] , outputs = img_showbox)
image_button.click(segment_image, inputs=[img_input, prompt_mode_select, category_select, custom_category,input_expressiong, results_select, num_inst_select, threshold_select, mask_image_mix_ration,model_select], outputs=image_segment)
with gr.Tab("Video task"):
with gr.Row():
gr.Markdown(
'# Due to computational resource limitations, support for video tasks is being processed and is expected to be available within a week.'
)
video_input = gr.Image()
video_button = gr.Button("Segment&Track")
if __name__ == '__main__':
demo.launch(inbrowser=True,share=True)