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import io | |
import gradio as gr | |
import matplotlib.pyplot as plt | |
import requests, validators | |
import torch | |
import pathlib | |
from PIL import Image | |
import cv2 as cv | |
import numpy as np | |
from transformers import DetrImageProcessor, DetrForSegmentation, MaskFormerImageProcessor, MaskFormerForInstanceSegmentation | |
from transformers.image_transforms import id_to_rgb | |
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 contour_map(map_to_use, label_id): | |
mask = (map_to_use.cpu().numpy() == label_id) | |
visual_mask = (mask * 255).astype(np.uint8) | |
contours, hierarchy = cv.findContours(visual_mask, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE) | |
return contours, hierarchy | |
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 = MaskFormerImageProcessor.from_pretrained(model_name) | |
# print(type(processor)) | |
model = MaskFormerForInstanceSegmentation.from_pretrained(model_name) | |
inputs = processor(images=image, return_tensors="pt") | |
outputs = model(**inputs) | |
results = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] | |
return_string = "" | |
for r in results["segments_info"]: | |
contour_list, hierarchy = contour_map(results["segmentation"], r["id"]) | |
label_name = model.config.id2label[r["label_id"]] | |
return_string += f"ID: {r['id']}\t Contour Count: {len(contour_list)}\t Score: {r['score']}\t Label Name: {label_name},\n" | |
r_shape = results["segmentation"].shape | |
new_image = np.zeros((r_shape[0], r_shape[1], 3), dtype=np.uint8) | |
new_image[:, :, 0] = (results["segmentation"].numpy()[:, :] * 2) % 256 | |
new_image[:, :, 1] = (new_image[:, :, 0] * 3) %256 | |
new_image[:, :, 2] = (new_image[:, :, 0] * 4) %256 | |
new_image = Image.fromarray(new_image) | |
return new_image, return_string | |
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 = """<h1 id="title">Image Segmentation with Various Models</h1>""" | |
description = """ | |
Links to HuggingFace Models: | |
- [facebook/detr-resnet-50-panoptic](https://huggingface.co/facebook/detr-resnet-50-panoptic) (Not implemented YET) | |
- [facebook/detr-resnet-101-panoptic](https://huggingface.co/facebook/detr-resnet-101-panoptic) (Not implemented YET) | |
- [facebook/maskformer-swin-large-coco](https://huggingface.co/facebook/maskformer-swin-large-coco) | |
Warning: On the free tier, MaskFormer takes a long time. | |
""" | |
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) |