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from transformers import AutoFeatureExtractor, YolosForObjectDetection
import gradio as gr
from PIL import Image
import torch
import matplotlib.pyplot as plt
import io
import numpy as np
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 get_class_list_from_input(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 infer(img, model_name: str, prob_threshold: int, classes_to_show = str):
feature_extractor = AutoFeatureExtractor.from_pretrained(f"hustvl/{model_name}")
model = YolosForObjectDetection.from_pretrained(f"hustvl/{model_name}")
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 = get_class_list_from_input(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 with YOLOS. Choose https://github.com/amikelive/coco-labels/blob/master/coco-labels-2014_2017.txtyour model and you're good to go.
You can adapt the minimum probability threshold with the slider.
Additionally you can restrict the classes that will be shown by putting in a comma separated list of
[COCO classes](https://github.com/amikelive/coco-labels/blob/master/coco-labels-2014_2017.txt).
Leaving the field empty will show all classes"""
image_in = gr.components.Image()
image_out = gr.components.Image()
model_choice = gr.components.Dropdown(["yolos-tiny", "yolos-small", "yolos-base", "yolos-small-300", "yolos-small-dwr"], value="yolos-small", label="YOLOS Model")
prob_threshold_slider = gr.components.Slider(minimum=0, maximum=1.0, step=0.01, value=0.9, label="Probability Threshold")
classes_to_show = gr.components.Textbox(placeholder="e.g. person, boat", label="Classes to use (empty means all classes)")
Iface = gr.Interface(
fn=infer,
inputs=[image_in,model_choice, prob_threshold_slider, classes_to_show],
outputs=image_out,
#examples=[["examples/10_People_Marching_People_Marching_2_120.jpg"], ["examples/12_Group_Group_12_Group_Group_12_26.jpg"], ["examples/43_Row_Boat_Canoe_43_247.jpg"]],
title="Object Detection with YOLOS",
description=description,
).launch() |