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'''import gradio as gr | |
def update(name): | |
return f"Welcome to Gradio, {name}!" | |
with gr.Blocks() as demo: | |
gr.Markdown("Start typing below and then click **Run** to see the output.") | |
with gr.Row(): | |
inp = gr.Textbox(placeholder="What is your name?") | |
out = gr.Textbox() | |
btn = gr.Button("Run") | |
btn.click(fn=update, inputs=inp, outputs=out) | |
demo.launch() | |
exit()#''' | |
import requests | |
import os | |
from io import BytesIO | |
from PIL import Image | |
import numpy as np | |
from pathlib import Path | |
import gradio as gr | |
import warnings | |
warnings.filterwarnings("ignore") | |
# os.system( | |
# "pip install einops shapely timm yacs tensorboardX ftfy prettytable pymongo click opencv-python inflect nltk scipy scikit-learn pycocotools") | |
# os.system("pip install transformers") | |
os.system("python setup.py build develop --user") | |
from maskrcnn_benchmark.config import cfg | |
from maskrcnn_benchmark.engine.predictor_glip import GLIPDemo | |
# Use this command for evaluate the GLIP-T model | |
#config_file = "configs/pretrain/glip_Swin_T_O365_GoldG.yaml" | |
#weight_file = "MODEL/glip_tiny_model_o365_goldg_cc_sbu.pth" | |
config_file = "configs/pretrain_new/desco_glip.yaml" | |
weight_file = "MODEL/desco_glip_tiny.pth" | |
# Use this command if you want to try the GLIP-L model | |
# ! wget https://penzhanwu2bbs.blob.core.windows.net/data/GLIPv1_Open/models/glip_large_model.pth -O MODEL/glip_large_model.pth | |
# config_file = "configs/pretrain/glip_Swin_L.yaml" | |
# weight_file = "MODEL/glip_large_model.pth" | |
# update the config options with the config file | |
# manual override some options | |
cfg.local_rank = 0 | |
cfg.num_gpus = 1 | |
cfg.merge_from_file(config_file) | |
cfg.merge_from_list(["MODEL.WEIGHT", weight_file]) | |
cfg.merge_from_list(["MODEL.DEVICE", "cuda"]) | |
glip_demo = GLIPDemo( | |
cfg, | |
min_image_size=800, | |
confidence_threshold=0.7, | |
show_mask_heatmaps=False | |
) | |
def predict(image, text): | |
result, _ = glip_demo.run_on_web_image(image[:, :, [2, 1, 0]], text, 0.5) | |
return result[:, :, [2, 1, 0]] | |
image = gr.inputs.Image() | |
gr.Interface( | |
description="Object Detection in the Wild through GLIP (https://github.com/microsoft/GLIP).", | |
fn=predict, | |
inputs=["image", "text"], | |
outputs=[ | |
gr.outputs.Image( | |
type="pil", | |
# label="grounding results" | |
), | |
], | |
examples=[ | |
#["./flickr_9472793441.jpg", "bobble heads on top of the shelf ."], | |
#["./flickr_9472793441.jpg", "sofa . remote . dog . person . car . sky . plane ."], | |
["./coco_000000281759.jpg", "A green umbrella. A pink striped umbrella. A plain white umbrella."], | |
["./coco_000000281759.jpg", "a flowery top. A blue dress. An orange shirt ."], | |
["./coco_000000281759.jpg", "a car . An electricity box ."], | |
#["./flickr_7520721.jpg", "A woman figure skater in a blue costume holds her leg by the blade of her skate ."] | |
], | |
article=Path("docs/intro.md").read_text() | |
).launch() | |
# ).launch(server_name="0.0.0.0", server_port=7000, share=True) | |