Spaces:
Sleeping
Sleeping
File size: 3,869 Bytes
ec63fb5 2545b8c 39c1fef 2545b8c e40c511 749745d e40c511 749745d 0713e59 e40c511 0443d1a e40c511 0443d1a e40c511 7cb4a86 fd60bd4 3c3d474 7cb4a86 e40c511 2a55a2e bbe0905 5732a50 7cb4a86 e40c511 a79ac6f e40c511 ad108ae e40c511 7cb4a86 e40c511 749745d 2a55a2e 749745d e40c511 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
'''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
)
config_file = "configs/pretrain_new/desco_fiber.yaml"
weight_file = "MODEL/desco_fiber_base.pth"
from copy import deepcopy
cfg = deepcopy(cfg)
cfg.merge_from_file(config_file)
cfg.merge_from_list(["MODEL.WEIGHT", weight_file])
cfg.merge_from_list(["MODEL.DEVICE", "cuda"])
fiber_demo = GLIPDemo(
cfg,
min_image_size=800,
confidence_threshold=0.7,
show_mask_heatmaps=False
)
def predict(image, text, specified_tokens=""):
if specified_tokens.strip() == "":
specified_tokens = None
else:
specified_tokens = specified_tokens.strip().split(";")
result, _ = glip_demo.run_on_web_image(deepcopy(image[:, :, [2, 1, 0]]), text, 0.5, specified_tokens)
fiber_result, _ = fiber_demo.run_on_web_image(deepcopy(image[:, :, [2, 1, 0]]), text, 0.5, specified_tokens)
return result[:, :, [2, 1, 0]], fiber_result[:, :, [2, 1, 0]]
image = gr.inputs.Image()
gr.Interface(
description="Object Recognition with DesCo",
fn=predict,
inputs=["image", "text", "text"],
outputs=[
gr.outputs.Image(
type="pil",
# label="grounding results"
),
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 .", ""],
["./1.jpg", "a train besides sidewalk", "train;sidewalk"],
#["./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)
|