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from typing import Tuple
import gradio as gr
import numpy as np
import supervision as sv
import torch
import time
from PIL import Image
from torchvision.transforms import ToTensor
# from transformers import SamModel, SamProcessor
from efficient_sam.build_efficient_sam import build_efficient_sam_vits
from efficientvit.models.efficientvit.sam import EfficientViTSamPredictor
from efficientvit.sam_model_zoo import create_sam_model
MARKDOWN = """
# EfficientViT-SAM vs EfficientSAM vs SAM
Paper source：
[EfficientViT-SAM](https://arxiv.org/abs/2402.05008) and [EfficientSAM](https://arxiv.org/abs/2312.00863) and
[SAM](https://arxiv.org/abs/2304.02643)
\n
Github Source Code: [Link](https://github.com/pg56714/Segment-Anything-Arena)
\n
The SAM model takes one minute to run to completion, which slow down other models. Currently, EfficientViT-SAM and EfficientSAM are displayed first.
The source code for all three models is available, but the SAM is commented out.
"""
BOX_EXAMPLES = [
["https://media.roboflow.com/efficient-sam/corgi.jpg", 801, 510, 1782, 993],
]
PROMPT_COLOR = sv.Color.from_hex("#D3D3D3")
MASK_COLOR = sv.Color.from_hex("#FF0000")
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# SAM_MODEL = SamModel.from_pretrained("facebook/sam-vit-huge").to(DEVICE).eval()
# SAM_PROCESSOR = SamProcessor.from_pretrained("facebook/sam-vit-huge")
EFFICIENT_SAM_MODEL = build_efficient_sam_vits().to(DEVICE).eval()
MASK_ANNOTATOR = sv.MaskAnnotator(color=MASK_COLOR, color_lookup=sv.ColorLookup.INDEX)
EFFICIENTVITSAM = EfficientViTSamPredictor(
create_sam_model(name="xl1", weight_url="./weights/xl1.pt").to(DEVICE).eval()
)
def annotate_image_with_box_prompt_result(
image: np.ndarray,
detections: sv.Detections,
x_min: int,
y_min: int,
x_max: int,
y_max: int,
) -> np.ndarray:
h, w, _ = image.shape
bgr_image = image[:, :, ::-1]
annotated_bgr_image = MASK_ANNOTATOR.annotate(
scene=bgr_image.copy(), detections=detections
)
annotated_bgr_image = sv.draw_rectangle(
scene=annotated_bgr_image,
rect=sv.Rect(
x=x_min,
y=y_min,
width=int(x_max - x_min),
height=int(y_max - y_min),
),
color=PROMPT_COLOR,
thickness=sv.calculate_optimal_line_thickness(resolution_wh=(w, h)),
)
return annotated_bgr_image[:, :, ::-1]
def efficientvit_sam_box_inference(
image: np.ndarray, x_min: int, y_min: int, x_max: int, y_max: int
) -> np.ndarray:
t1 = time.time()
box = np.array([[x_min, y_min, x_max, y_max]])
EFFICIENTVITSAM.set_image(image)
mask = EFFICIENTVITSAM.predict(box=box, multimask_output=False)
mask = mask[0]
detections = sv.Detections(xyxy=sv.mask_to_xyxy(masks=mask), mask=mask)
result = annotate_image_with_box_prompt_result(
image=image,
detections=detections,
x_max=x_max,
x_min=x_min,
y_max=y_max,
y_min=y_min,
)
t2 = time.time()
print(f"timecost: {t2-t1}")
return result
def inference_with_box(
image: np.ndarray,
box: np.ndarray,
model: torch.jit.ScriptModule,
device: torch.device,
) -> np.ndarray:
bbox = torch.reshape(torch.tensor(box), [1, 1, 2, 2])
bbox_labels = torch.reshape(torch.tensor([2, 3]), [1, 1, 2])
img_tensor = ToTensor()(image)
predicted_logits, predicted_iou = model(
img_tensor[None, ...].to(device),
bbox.to(device),
bbox_labels.to(device),
)
predicted_logits = predicted_logits.cpu()
all_masks = torch.ge(torch.sigmoid(predicted_logits[0, 0, :, :, :]), 0.5).numpy()
predicted_iou = predicted_iou[0, 0, ...].cpu().detach().numpy()
max_predicted_iou = -1
selected_mask_using_predicted_iou = None
for m in range(all_masks.shape[0]):
curr_predicted_iou = predicted_iou[m]
if (
curr_predicted_iou > max_predicted_iou
or selected_mask_using_predicted_iou is None
):
max_predicted_iou = curr_predicted_iou
selected_mask_using_predicted_iou = all_masks[m]
return selected_mask_using_predicted_iou
def efficient_sam_box_inference(
image: np.ndarray, x_min: int, y_min: int, x_max: int, y_max: int
) -> np.ndarray:
t1 = time.time()
box = np.array([[x_min, y_min], [x_max, y_max]])
mask = inference_with_box(image, box, EFFICIENT_SAM_MODEL, DEVICE)
mask = mask[np.newaxis, ...]
detections = sv.Detections(xyxy=sv.mask_to_xyxy(masks=mask), mask=mask)
result = annotate_image_with_box_prompt_result(
image=image,
detections=detections,
x_max=x_max,
x_min=x_min,
y_max=y_max,
y_min=y_min,
)
t2 = time.time()
print(f"timecost: {t2-t1}")
return result
# def sam_box_inference(
# image: np.ndarray, x_min: int, y_min: int, x_max: int, y_max: int
# ) -> np.ndarray:
# t1 = time.time()
# input_boxes = [[[x_min, y_min, x_max, y_max]]]
# inputs = SAM_PROCESSOR(
# Image.fromarray(image), input_boxes=[input_boxes], return_tensors="pt"
# ).to(DEVICE)
# with torch.no_grad():
# outputs = SAM_MODEL(**inputs)
# mask = SAM_PROCESSOR.image_processor.post_process_masks(
# outputs.pred_masks.cpu(),
# inputs["original_sizes"].cpu(),
# inputs["reshaped_input_sizes"].cpu(),
# )[0][0][0].numpy()
# mask = mask[np.newaxis, ...]
# detections = sv.Detections(xyxy=sv.mask_to_xyxy(masks=mask), mask=mask)
# result = annotate_image_with_box_prompt_result(
# image=image,
# detections=detections,
# x_max=x_max,
# x_min=x_min,
# y_max=y_max,
# y_min=y_min,
# )
# t2 = time.time()
# print(f"timecost: {t2-t1}")
# return result
def box_inference(
image: np.ndarray, x_min: int, y_min: int, x_max: int, y_max: int
) -> Tuple[np.ndarray, np.ndarray]:
return (
efficientvit_sam_box_inference(image, x_min, y_min, x_max, y_max),
efficient_sam_box_inference(image, x_min, y_min, x_max, y_max),
# sam_box_inference(image, x_min, y_min, x_max, y_max),
)
# def clear(_: np.ndarray) -> Tuple[None, None, None]:
# return None, None, None
def clear(_: np.ndarray) -> Tuple[None, None]:
return None, None
box_input_image = gr.Image()
x_min_number = gr.Number(label="x_min")
y_min_number = gr.Number(label="y_min")
x_max_number = gr.Number(label="x_max")
y_max_number = gr.Number(label="y_max")
box_inputs = [box_input_image, x_min_number, y_min_number, x_max_number, y_max_number]
with gr.Blocks() as demo:
gr.Markdown(MARKDOWN)
with gr.Row():
box_input_image.render()
efficientvit_sam_box_output_image = gr.Image(label="EfficientVit-SAM")
efficient_sam_box_output_image = gr.Image(label="EfficientSAM")
# sam_box_output_image = gr.Image(label="SAM")
with gr.Row():
x_min_number.render()
y_min_number.render()
x_max_number.render()
y_max_number.render()
submit_box_inference_button = gr.Button(
value="Submit", scale=1, variant="primary"
)
gr.Examples(
# fn=box_inference,
examples=BOX_EXAMPLES,
inputs=box_inputs,
outputs=[
efficientvit_sam_box_output_image,
efficient_sam_box_output_image,
# sam_box_output_image,
],
)
submit_box_inference_button.click(
efficientvit_sam_box_inference,
inputs=box_inputs,
outputs=efficientvit_sam_box_output_image,
)
submit_box_inference_button.click(
efficient_sam_box_inference,
inputs=box_inputs,
outputs=efficient_sam_box_output_image,
)
# submit_box_inference_button.click(
# sam_box_inference, inputs=box_inputs, outputs=sam_box_output_image
# )
box_input_image.change(
clear,
inputs=box_input_image,
outputs=[
efficientvit_sam_box_output_image,
efficient_sam_box_output_image,
# sam_box_output_image,
],
)
demo.launch(debug=False, show_error=True)
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