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import os
import gradio as gr
from demo.sam_inference import SAM_Inference
from demo.seagull_inference import Seagull
class Main_ui():
def __init__(self, args) -> None:
self.args = args
self.seagull = Seagull(model_path=args.model)
self.example_list = self.load_example()
self.sam = SAM_Inference()
def load_example(self):
examples = []
for file in sorted(os.listdir(self.args.example_path)):
examples.append([os.path.join(self.args.example_path, file)])
return examples
def load_demo(self):
with gr.Blocks() as demo:
preprocessed_img = gr.State(value=None)
binary_mask = gr.State(value=None)
with gr.Row():
gr.Markdown("""
<img src="https://github.com/chencn2020/SEAGULL/raw/main/imgs/Logo/logo.png" alt="SEAGULL" style="height: auto; width: 100%; margin-bottom: 3%;">
## 🔔 Usage
Firstly, you need to upload an image and choose the analyse types **(quality score, importance score and distortion analysis)**.
Then you can click **(points)** or pull a frame **(bbox)** on the image to indicate the region of interest (ROIs).
After that, this demo process the following steps:
> 1. SAM extracts the mask-based ROIs based on your clicked points or frame.
> 2. Based on the uploaded image and mask-based ROIs, SEAGULL analyses the quality of the ROIs.
""")
with gr.TabItem("Mask-based ROIs (Points)"):
with gr.Row():
input_image_ponit = gr.Image(type="numpy", label='Input image', height=512) # input image
output_mask_ponit = gr.Image(label='Mask-based ROI', height=512) # output binary mask
with gr.Row():
output_mask_point_on_img = gr.Image(label='Mask on image', height=512) # mask on image for better view
with gr.Column():
radio_point = gr.Radio(label='Analysis type', choices=['Quality Score', 'Importance Score', 'Distortion Analysis'], value='Quality Score')
output_text_point = gr.Textbox(label='Analysis Results')
point_seg_button = gr.Button('Analysis')
point_example = gr.Dataset(label='Examples', components=[input_image_ponit], samples=self.example_list)
with gr.TabItem("Mask-based ROIs (BBox)"):
with gr.Row():
input_image_BBOX = gr.Image(tool='boxes', type="numpy", label='Input image', height=512)
output_mask_BBOX = gr.Image(label='Mask-based ROI', height=512)
with gr.Row():
output_BBOX_mask_on_img = gr.Image(label='Mask on image', height=512)
with gr.Column():
radio_BBOX = gr.Radio(label='Analysis type', choices=['Quality Score', 'Importance Score', 'Distortion Analysis'], value='Quality Score')
output_text_BBOX = gr.Textbox(label='ROI Quality Analysis')
box_seg_button = gr.Button('Generate mask and analysis')
box_analyse_button = gr.Button('Analysis')
BBOX_example = gr.Dataset(label='Examples', components=[input_image_BBOX], samples=self.example_list)
with gr.TabItem("Mask-based ROIs (BBox with Points)"):
with gr.Row():
input_image_BBOX_Points = gr.Image(tool='boxes', type="numpy", label='Input image', height=512)
output_mask_BBOX_Points = gr.Image(label='Mask-based ROI', height=512)
with gr.Row():
output_BBOX_Points_mask_on_img = gr.Image(label='Mask on image', height=512)
with gr.Column():
radio_BBOX_Points = gr.Radio(label='Analysis type', choices=['Quality Score', 'Importance Score', 'Distortion Analysis'], value='Quality Score')
output_text_BBOX_Points = gr.Textbox(label='ROI Quality Analysis')
box_seg_button_with_point = gr.Button('Generate mask and analysis')
box_analyse_button_with_point = gr.Button('Analysis')
BBOX_Points_example = gr.Dataset(label='Examples', components=[input_image_BBOX_Points], samples=self.example_list)
# 1. click point
input_image_ponit.upload(
self.seagull.init_image,
[input_image_ponit],
[preprocessed_img, input_image_ponit, input_image_BBOX, input_image_BBOX_Points]
)
point_example.click(
self.seagull.init_image,
[point_example],
[preprocessed_img, input_image_ponit, input_image_BBOX, input_image_BBOX_Points]
)
# after clicking on the image
input_image_ponit.select(
self.sam.img_select_point,
[preprocessed_img],
[input_image_ponit, output_mask_ponit, output_mask_point_on_img, binary_mask]
).then(
self.seagull.seagull_predict,
[preprocessed_img, binary_mask, radio_point],
[output_text_point]
)
point_seg_button.click(
self.seagull.seagull_predict,
[preprocessed_img, binary_mask, radio_point],
[output_text_point]
)
# 2. draw frame and use SAM to indicate the rois
input_image_BBOX.upload(
self.seagull.init_image,
[input_image_BBOX],
[preprocessed_img, input_image_ponit, input_image_BBOX, input_image_BBOX_Points]
)
BBOX_example.click(
self.seagull.init_image,
[BBOX_example],
[preprocessed_img, input_image_ponit, input_image_BBOX, input_image_BBOX_Points]
)
# after drawing a frame on the image
input_image_BBOX.select(
self.sam.gen_box_seg,
[input_image_BBOX],
[output_mask_BBOX, output_BBOX_mask_on_img, binary_mask]
)
box_seg_button.click(
self.sam.gen_box_seg,
[input_image_BBOX],
[output_mask_BBOX, output_BBOX_mask_on_img, binary_mask]
).then(
self.seagull.seagull_predict,
[preprocessed_img, binary_mask, radio_BBOX],
[output_text_BBOX]
)
box_analyse_button.click(
self.seagull.seagull_predict,
[preprocessed_img, binary_mask, radio_BBOX],
[output_text_BBOX]
)
# 3. draw frame and use the points
input_image_BBOX.upload(
self.seagull.init_image,
[input_image_BBOX],
[preprocessed_img, input_image_ponit, input_image_BBOX, input_image_BBOX_Points]
)
BBOX_Points_example.click(
self.seagull.init_image,
[BBOX_Points_example],
[preprocessed_img, input_image_ponit, input_image_BBOX, input_image_BBOX_Points]
)
# after drawing a frame on the image
input_image_BBOX_Points.select(
self.sam.gen_box_point,
[input_image_BBOX_Points],
[output_mask_BBOX_Points, output_BBOX_Points_mask_on_img, binary_mask]
)
box_seg_button_with_point.click(
self.sam.gen_box_point,
[input_image_BBOX_Points],
[output_mask_BBOX_Points, output_BBOX_Points_mask_on_img, binary_mask]
).then(
self.seagull.seagull_predict,
[preprocessed_img, binary_mask, radio_BBOX_Points],
[output_text_BBOX_Points]
)
box_analyse_button_with_point.click(
self.seagull.seagull_predict,
[preprocessed_img, binary_mask, radio_BBOX_Points],
[output_text_BBOX_Points]
)
return demo |