import io
import gradio as gr
import matplotlib.pyplot as plt
import requests, validators
import torch
import pathlib
from PIL import Image
from transformers import DetrFeatureExtractor, DetrForSegmentation, MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
from transformers.models.detr.feature_extraction_detr import rgb_to_id
import os
# colors for visualization
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]
]
YOLOV8_LABELS = ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
def make_prediction(img, feature_extractor, model):
inputs = feature_extractor(img, return_tensors="pt")
outputs = model(**inputs)
img_size = torch.tensor([tuple(reversed(img.size))])
processed_outputs = feature_extractor.post_process(outputs, img_size)
return processed_outputs
def fig2img(fig):
buf = io.BytesIO()
fig.savefig(buf, bbox_inches="tight")
buf.seek(0)
img = Image.open(buf)
return img
def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
keep = output_dict["scores"] > threshold
boxes = output_dict["boxes"][keep].tolist()
scores = output_dict["scores"][keep].tolist()
labels = output_dict["labels"][keep].tolist()
if id2label is not None:
labels = [id2label[x] for x in labels]
# print("Labels " + str(labels))
plt.figure(figsize=(16, 10))
plt.imshow(pil_img)
ax = plt.gca()
colors = COLORS * 100
for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors):
ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3))
ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
plt.axis("off")
return fig2img(plt.gcf())
def segment_images(model_name,url_input,image_input,threshold):
####
# Get Image Object
if validators.url(url_input):
image = Image.open(requests.get(url_input, stream=True).raw)
elif image_input:
image = image_input
####
if "detr" in model_name:
pass
elif "maskformer" in model_name.lower():
# Load the processor and model
processor = MaskFormerFeatureExtractor.from_pretrained(model_name)
print(type(processor))
model = MaskFormerForInstanceSegmentation.from_pretrained(model_name)
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
print(type(outputs))
print(outputs)
pass
else:
raise NameError("Model is not implemented")
def set_example_image(example: list) -> dict:
return gr.Image.update(value=example[0])
def set_example_url(example: list) -> dict:
return gr.Textbox.update(value=example[0])
title = """
Image Segmentation with Various Models
"""
description = """
Links to HuggingFace Models:
- [facebook/detr-resnet-50-panoptic](https://huggingface.co/facebook/detr-resnet-50-panoptic)
- [facebook/detr-resnet-101-panoptic](https://huggingface.co/facebook/detr-resnet-101-panoptic)
- [facebook/maskformer-swin-large-coco](https://huggingface.co/facebook/maskformer-swin-large-coco)
"""
models = ["facebook/detr-resnet-50-panoptic","facebook/detr-resnet-101-panoptic","facebook/maskformer-swin-large-coco"]
urls = ["https://c8.alamy.com/comp/J2AB4K/the-new-york-stock-exchange-on-the-wall-street-in-new-york-J2AB4K.jpg"]
# twitter_link = """
# [![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi)
# """
css = '''
h1#title {
text-align: center;
}
'''
demo = gr.Blocks(css=css)
def changing():
# https://discuss.huggingface.co/t/how-to-programmatically-enable-or-disable-components/52350/4
return gr.Button.update(interactive=True), gr.Button.update(interactive=True)
with demo:
gr.Markdown(title)
gr.Markdown(description)
# gr.Markdown(twitter_link)
options = gr.Dropdown(choices=models,label='Select Image Segmentation Model',show_label=True)
slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.7,label='Prediction Threshold')
with gr.Tabs():
with gr.TabItem('Image URL'):
with gr.Row():
url_input = gr.Textbox(lines=2,label='Enter valid image URL here..')
img_output_from_url = gr.Image(shape=(650,650))
with gr.Row():
example_url = gr.Dataset(components=[url_input],samples=[[str(url)] for url in urls])
url_but = gr.Button('Detect', interactive=False)
with gr.TabItem('Image Upload'):
with gr.Row():
img_input = gr.Image(type='pil')
img_output_from_upload= gr.Image(shape=(650,650))
with gr.Row():
example_images = gr.Dataset(components=[img_input],
samples=[[path.as_posix()]
for path in sorted(pathlib.Path('images').rglob('*.JPG'))]) # Can't get case_sensitive to work
img_but = gr.Button('Detect', interactive=False)
# output_text1 = gr.outputs.Textbox(label="Confidence Values")
output_text1 = gr.components.Textbox(label="Confidence Values")
# https://huggingface.co/spaces/vishnun/CLIPnCROP/blob/main/app.py -- Got .outputs. from this
options.change(fn=changing, inputs=[], outputs=[img_but, url_but])
url_but.click(segment_images,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, output_text1],queue=True)
img_but.click(segment_images,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, output_text1],queue=True)
# url_but.click(segment_images,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, _],queue=True)
# img_but.click(segment_images,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, _],queue=True)
# url_but.click(segment_images,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_url,queue=True)
# img_but.click(segment_images,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_upload,queue=True)
example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])
example_url.click(fn=set_example_url,inputs=[example_url],outputs=[url_input])
# gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-object-detection-with-detr-and-yolos)")
# demo.launch(enable_queue=True)
demo.launch() #removed (share=True)