YOLO-World / app.py
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from typing import List
import cv2
import gradio as gr
import numpy as np
import supervision as sv
import torch
from inference.models import YOLOWorld
from utils.efficient_sam import load, inference_with_box
MARKDOWN = """
# YOLO-World + EfficientSAM 🔥
This is a demo of zero-shot instance segmentation using
[YOLO-World](https://github.com/AILab-CVC/YOLO-World) and
[EfficientSAM](https://github.com/yformer/EfficientSAM).
Powered by Roboflow [Inference](https://github.com/roboflow/inference) and
[Supervision](https://github.com/roboflow/supervision).
"""
EXAMPLES = [
['https://media.roboflow.com/dog.jpeg', 'dog, eye, nose, tongue, car', 0.005, 0.1, True, False, False],
]
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
EFFICIENT_SAM_MODEL = load(device=DEVICE)
YOLO_WORLD_MODEL = YOLOWorld(model_id="yolo_world/l")
BOUNDING_BOX_ANNOTATOR = sv.BoundingBoxAnnotator()
MASK_ANNOTATOR = sv.MaskAnnotator()
LABEL_ANNOTATOR = sv.LabelAnnotator()
def process_categories(categories: str) -> List[str]:
return [category.strip() for category in categories.split(',')]
def process_image(
input_image: np.ndarray,
categories: str,
confidence_threshold: float = 0.3,
iou_threshold: float = 0.5,
with_segmentation: bool = True,
with_confidence: bool = False,
with_class_agnostic_nms: bool = False,
) -> np.ndarray:
categories = process_categories(categories)
YOLO_WORLD_MODEL.set_classes(categories)
results = YOLO_WORLD_MODEL.infer(input_image, confidence=confidence_threshold)
detections = sv.Detections.from_inference(results)
detections = detections.with_nms(
class_agnostic=with_class_agnostic_nms,
threshold=iou_threshold)
if with_segmentation:
masks = []
for [x_min, y_min, x_max, y_max] in detections.xyxy:
box = np.array([[x_min, y_min], [x_max, y_max]])
mask = inference_with_box(input_image, box, EFFICIENT_SAM_MODEL, DEVICE)
masks.append(mask)
detections.mask = np.array(masks)
labels = [
(
f"{categories[class_id]}: {confidence:.2f}"
if with_confidence
else f"{categories[class_id]}"
)
for class_id, confidence in
zip(detections.class_id, detections.confidence)
]
output_image = input_image.copy()
output_image = cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)
output_image = MASK_ANNOTATOR.annotate(output_image, detections)
output_image = BOUNDING_BOX_ANNOTATOR.annotate(output_image, detections)
output_image = LABEL_ANNOTATOR.annotate(output_image, detections, labels=labels)
output_image = cv2.cvtColor(output_image, cv2.COLOR_BGR2RGB)
return output_image
confidence_threshold_component = gr.Slider(
minimum=0,
maximum=1.0,
value=0.3,
step=0.01,
label="Confidence Threshold",
info=(
"The confidence threshold for the YOLO-World model. Lower the threshold to "
"reduce false negatives, enhancing the model's sensitivity to detect "
"sought-after objects. Conversely, increase the threshold to minimize false "
"positives, preventing the model from identifying objects it shouldn't."
))
iou_threshold_component = gr.Slider(
minimum=0,
maximum=1.0,
value=0.5,
step=0.01,
label="IoU Threshold",
info=(
"The Intersection over Union (IoU) threshold for non-maximum suppression. "
"Decrease the value to lessen the occurrence of overlapping bounding boxes, "
"making the detection process stricter. On the other hand, increase the value "
"to allow more overlapping bounding boxes, accommodating a broader range of "
"detections."
))
with_segmentation_component = gr.Checkbox(
value=True,
label="With Segmentation",
info=(
"Whether to run EfficientSAM for instance segmentation."
)
)
with_confidence_component = gr.Checkbox(
value=False,
label="Display Confidence",
info=(
"Whether to display the confidence of the detected objects."
)
)
with_class_agnostic_nms_component = gr.Checkbox(
value=False,
label="Use Class-Agnostic NMS",
info=(
"Suppress overlapping bounding boxes across all classes."
)
)
with gr.Blocks() as demo:
gr.Markdown(MARKDOWN)
with gr.Accordion("Configuration", open=False):
confidence_threshold_component.render()
iou_threshold_component.render()
with gr.Row():
with_segmentation_component.render()
with_confidence_component.render()
with_class_agnostic_nms_component.render()
with gr.Row():
input_image_component = gr.Image(
type='numpy',
label='Input Image'
)
output_image_component = gr.Image(
type='numpy',
label='Output Image'
)
with gr.Row():
categories_text_component = gr.Textbox(
label='Categories',
placeholder='comma separated list of categories',
scale=7
)
submit_button_component = gr.Button(
value='Submit',
scale=1,
variant='primary'
)
gr.Examples(
fn=process_image,
examples=EXAMPLES,
inputs=[
input_image_component,
categories_text_component,
confidence_threshold_component,
iou_threshold_component,
with_segmentation_component,
with_confidence_component,
with_class_agnostic_nms_component
],
outputs=output_image_component
)
submit_button_component.click(
fn=process_image,
inputs=[
input_image_component,
categories_text_component,
confidence_threshold_component,
iou_threshold_component,
with_segmentation_component,
with_confidence_component,
with_class_agnostic_nms_component
],
outputs=output_image_component
)
demo.launch(debug=False, show_error=True)