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import gradio as gr
import spaces
import torch
from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
from PIL import Image
import time
def extract_model_short_name(model_id):
return model_id.split("/")[-1].replace("-", " ").replace("_", " ")
model_llmdet_id = "iSEE-Laboratory/llmdet_tiny"
model_mm_grounding_id = "rziga/mm_grounding_dino_tiny_o365v1_goldg"
model_omdet_id = "omlab/omdet-turbo-swin-tiny-hf"
model_owlv2_id = "google/owlv2-large-patch14-ensemble"
model_llmdet_name = extract_model_short_name(model_llmdet_id)
model_mm_grounding_name = extract_model_short_name(model_mm_grounding_id)
model_omdet_name = extract_model_short_name(model_omdet_id)
model_owlv2_name = extract_model_short_name(model_owlv2_id)
@spaces.GPU
def detect(model_id: str, image: Image.Image, prompts: list, threshold: float):
t0 = time.perf_counter()
device = "cuda" if torch.cuda.is_available() else "cpu"
processor = AutoProcessor.from_pretrained(model_id)
model = (
AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device).eval()
)
texts = [prompts]
inputs = processor(images=image, text=texts, return_tensors="pt").to(device)
with torch.inference_mode():
outputs = model(**inputs)
results = processor.post_process_grounded_object_detection(
outputs, threshold=threshold, target_sizes=[image.size[::-1]]
)
result = results[0]
annotations = []
for box, score, label_name in zip(result["boxes"], result["scores"], result["text_abels"]):
if score >= threshold:
xmin, ymin, xmax, ymax = [int(x) for x in box.tolist()]
annotations.append(((xmin, ymin, xmax, ymax), f"{label_name} {score:.2f}"))
elapsed_ms = (time.perf_counter() - t0) * 1000
time_taken = f"**Inference time ({model_omdet_name}):** {elapsed_ms:.0f} ms"
return annotations, time_taken
def run_detection(
image: Image.Image, prompts_str: str, threshold_llm, threshold_mm, threshold_owlv2, threshold_omdet,
):
prompts = [p.strip() for p in prompts_str.split(",")]
ann_llm, time_llm = detect(model_llmdet_id, image, prompts, threshold_llm)
ann_mm, time_mm = detect(model_mm_grounding_name, image, prompts, threshold_mm)
ann_owlv2, time_owlv2 = detect(model_omdet_id, image, prompts, threshold_owlv2)
ann_omdet, time_omdet = detect(model_owlv2_name, image, prompts, threshold_omdet)
return (
(image, ann_llm),
time_llm,
(image, ann_mm),
time_mm,
(image, ann_owlv2),
time_owlv2,
(image, ann_omdet),
time_omdet,
)
with gr.Blocks() as app:
gr.Markdown("# Zero-Shot Object Detection Arena")
gr.Markdown(
"### Compare different zero-shot object detection models on the same image and prompts."
)
with gr.Row():
with gr.Column(scale=1):
image = gr.Image(type="pil", label="Upload an image", height=400)
prompts = gr.Textbox(
label="Prompts (comma-separated)", value="a cat, a remote control"
)
with gr.Accordion("Per-model confidence thresholds", open=True):
threshold_llm = gr.Slider(
label="Threshold for LLMDet", minimum=0.0, maximum=1.0, value=0.3
)
threshold_mm = gr.Slider(
label="Threshold for MM GroundingDINO Tiny",
minimum=0.0,
maximum=1.0,
value=0.3,
)
threshold_owlv2 = gr.Slider(
label="Threshold for OwlV2 Large",
minimum=0.0,
maximum=1.0,
value=0.1,
)
threshold_omdet = gr.Slider(
label="Threshold for OMDet Turbo Swin Tiny",
minimum=0.0,
maximum=1.0,
value=0.2,
)
generate_btn = gr.Button(value="Detect")
with gr.Row():
with gr.Column(scale=2):
output_image_llm = gr.AnnotatedImage(
label=f"Annotated image for {model_llmdet_name}", height=400
)
output_time_llm = gr.Markdown()
with gr.Column(scale=2):
output_image_mm = gr.AnnotatedImage(
label=f"Annotated image for {model_mm_grounding_name}", height=400
)
output_time_mm = gr.Markdown()
with gr.Row():
with gr.Column(scale=2):
output_image_owlv2 = gr.AnnotatedImage(
label=f"Annotated image for {model_owlv2_name}", height=400
)
output_time_owlv2 = gr.Markdown()
with gr.Column(scale=2):
output_image_omdet = gr.AnnotatedImage(
label=f"Annotated image for {model_omdet_name}", height=400
)
output_time_omdet = gr.Markdown()
gr.Markdown("### Examples")
example_data = [
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"a cat, a remote control",
0.30,
0.30,
0.10,
0.30,
],
[
"http://images.cocodataset.org/val2017/000000000139.jpg",
"a person, a tv, a remote",
0.35,
0.30,
0.12,
0.30,
],
]
gr.Examples(
examples=example_data,
inputs=[
image,
prompts,
threshold_llm,
threshold_mm,
threshold_owlv2,
threshold_omdet,
],
label="Click an example to populate the inputs",
)
inputs = [
image,
prompts,
threshold_llm,
threshold_mm,
threshold_owlv2,
threshold_omdet,
]
outputs = [
output_image_llm,
output_time_llm,
output_image_mm,
output_time_mm,
output_image_owlv2,
output_time_owlv2,
output_image_omdet,
output_time_omdet,
]
generate_btn.click(
fn=run_detection,
inputs=inputs,
outputs=outputs,
)
image.upload(
fn=run_detection,
inputs=inputs,
outputs=outputs,
)
app.launch()
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