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import io
import torch
import numpy as np
import gradio as gr
import matplotlib.pyplot as plt
from transformers import AutoFeatureExtractor, YolosForObjectDetection
from PIL import Image
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],
]
def process_class_list(classes_string: str):
if classes_string == "":
return []
classes_list = classes_string.split(",")
classes_list = [x.strip() for x in classes_list]
return classes_list
def model_inference(img, prob_threshold, classes_to_show):
feature_extractor = AutoFeatureExtractor.from_pretrained(f"hustvl/yolos-small-dwr")
model = YolosForObjectDetection.from_pretrained(f"hustvl/yolos-small-dwr")
img = Image.fromarray(img)
pixel_values = feature_extractor(img, return_tensors="pt").pixel_values
with torch.no_grad():
outputs = model(pixel_values, output_attentions=True)
probas = outputs.logits.softmax(-1)[0, :, :-1]
keep = probas.max(-1).values > prob_threshold
target_sizes = torch.tensor(img.size[::-1]).unsqueeze(0)
postprocessed_outputs = feature_extractor.post_process(outputs, target_sizes)
bboxes_scaled = postprocessed_outputs[0]["boxes"]
classes_list = process_class_list(classes_to_show)
res_img = plot_results(img, probas[keep], bboxes_scaled[keep], model, classes_list)
return res_img
def plot_results(pil_img, prob, boxes, model, classes_list):
plt.figure(figsize=(16, 10))
plt.imshow(pil_img)
ax = plt.gca()
colors = COLORS * 100
for p, (xmin, ymin, xmax, ymax), c in zip(prob, boxes.tolist(), colors):
cl = p.argmax()
object_class = model.config.id2label[cl.item()]
if len(classes_list) > 0:
if object_class not in classes_list:
continue
ax.add_patch(
plt.Rectangle(
(xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=c, linewidth=3
)
)
text = f"{object_class}: {p[cl]:0.2f}"
ax.text(xmin, ymin, text, fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
plt.axis("off")
return fig2img(plt.gcf())
def fig2img(fig):
buf = io.BytesIO()
fig.savefig(buf)
buf.seek(0)
img = Image.open(buf)
return img
description = """Object Detection"""
title = """Object Detection"""
image_in = gr.components.Image(label="Upload an image")
image_out = gr.components.Image()
prob_threshold_slider = gr.components.Slider(
minimum=0, maximum=1.0, step=0.01, value=0.7, label="Probability Threshold"
)
classes_to_show = gr.components.Textbox(
placeholder="e.g. car, dog",
label="Classes to filter (leave empty to detect all classes)",
)
gr.Interface(fn=model_inference,
inputs=[image_in, prob_threshold_slider, classes_to_show],
outputs=image_out,
title=title,
examples=["00_plane.jpg", "01_car.jpg"],
description=description).launch()
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