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import gradio as gr
import pandas as pd
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from PIL import Image, ImageDraw
import torch
image_processor = AutoImageProcessor.from_pretrained('hustvl/yolos-small')
model = AutoModelForObjectDetection.from_pretrained('hustvl/yolos-small')
colors = ["red",
"orange",
"yellow",
"green",
"blue",
"indigo",
"violet",
"brown",
"black",
"slategray",
]
def detect(image):
inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
# convert outputs to COCO API
target_sizes = torch.tensor([image.size[::-1]])
results = image_processor.post_process_object_detection(outputs,
threshold=0.9,
target_sizes=target_sizes)[0]
draw = ImageDraw.Draw(image)
# label and the count
counts = {}
for score, label in zip(results["scores"], results["labels"]):
label_name = model.config.id2label[label.item()]
if label_name not in counts:
counts[label_name] = 0
counts[label_name] += 1
count_results = {k: v for k, v in (sorted(counts.items(), key=lambda item: item[1], reverse=True)[:10])}
label2color = {}
for idx, label in enumerate(count_results):
label2color[label] = colors[idx]
for label, box in zip(results["labels"], results["boxes"]):
label_name = model.config.id2label[label.item()]
if label_name in count_results:
box = [round(i, 4) for i in box.tolist()]
x1, y1, x2, y2 = tuple(box)
draw.rectangle((x1, y1, x2, y2), outline=label2color[label_name], width=2)
draw.text((x1, y1), label_name, fill="white")
df = pd.DataFrame({
'label': [label for label in count_results],
'counts': [counts[label] for label in count_results]
})
return image, df, count_results
demo = gr.Interface(
fn=detect,
inputs=[gr.inputs.Image(label="Input image", type="pil")],
outputs=[gr.Image(label="Output image"), gr.BarPlot(show_label=False, x="label", y="counts", x_title="Labels", y_title="Counts", vertical=False), gr.Textbox(show_label=False)],
title="YOLO Object Detection"
)
demo.launch() |