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# -*- coding: utf-8 -*-
"""
Created on Fri Oct 6 17:53:27 2023
@author: prarthana.ts
"""
from ultralytics import YOLO
import gradio as gr
import torch
from utils.gradio_tools import fast_process
from utils.tools import format_results, box_prompt, point_prompt, text_prompt
from PIL import ImageDraw
import numpy as np
# Load the pre-trained model
model = YOLO('./weights/FastSAM.pt')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
icon_html = '<i class="fas fa-chart-bar" style="font-weight: bold;"></i>'
title = f"""
<div style="background-color: #f5f1f2; padding: 10px; display: flex; align-items: center; justify-content: center;">
{icon_html} <span style="margin-left: 10px; font-size: 50px;">Fast Segment Anything</span>
</div>
"""
description = f"""
<div style="background-color: #f1f1f5; padding: 10px; display: flex; align-items: center;">
{icon_html}
<span style="margin-left: 10px;">
<p><strong>Pre trained Implementation of fast sam</strong></p>
<p>Note:Please provide the text with respect to what needs to be segmented</p>
</span>
</div>
"""
css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }"
def segment_everything(
input,
input_size=1024,
iou_threshold=0.7,
conf_threshold=0.25,
better_quality=False,
withContours=True,
use_retina=True,
text="",
wider=False,
mask_random_color=True,
):
input_size = int(input_size)
w, h = input.size
scale = input_size / max(w, h)
new_w = int(w * scale)
new_h = int(h * scale)
input = input.resize((new_w, new_h))
results = model(input,
device=device,
retina_masks=True,
iou=iou_threshold,
conf=conf_threshold,
imgsz=input_size,)
if len(text) > 0:
results = format_results(results[0], 0)
annotations, _ = text_prompt(results, text, input, device=device, wider=wider)
annotations = np.array([annotations])
else:
annotations = results[0].masks.data
fig = fast_process(annotations=annotations,
image=input,
device=device,
scale=(1024 // input_size),
better_quality=better_quality,
mask_random_color=mask_random_color,
bbox=None,
use_retina=use_retina,
withContours=withContours,)
return fig
def segment_with_points(
input,
input_size=1024,
iou_threshold=0.7,
conf_threshold=0.25,
better_quality=False,
withContours=True,
use_retina=True,
mask_random_color=True,
):
global global_points
global global_point_label
input_size = int(input_size)
w, h = input.size
scale = input_size / max(w, h)
new_w = int(w * scale)
new_h = int(h * scale)
input = input.resize((new_w, new_h))
scaled_points = [[int(x * scale) for x in point] for point in global_points]
results = model(input,
device=device,
retina_masks=True,
iou=iou_threshold,
conf=conf_threshold,
imgsz=input_size,)
results = format_results(results[0], 0)
annotations, _ = point_prompt(results, scaled_points, global_point_label, new_h, new_w)
annotations = np.array([annotations])
fig = fast_process(annotations=annotations,
image=input,
device=device,
scale=(1024 // input_size),
better_quality=better_quality,
mask_random_color=mask_random_color,
bbox=None,
use_retina=use_retina,
withContours=withContours,)
global_points = []
global_point_label = []
return fig, None
def get_points_with_draw(image, label, evt: gr.SelectData):
global global_points
global global_point_label
x, y = evt.index[0], evt.index[1]
point_radius, point_color = 15, (255, 255, 0) if label == 'Add Mask' else (255, 0, 255)
global_points.append([x, y])
global_point_label.append(1 if label == 'Add Mask' else 0)
print(x, y, label == 'Add Mask')
draw = ImageDraw.Draw(image)
draw.ellipse([(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)], fill=point_color)
return image
cond_img_t = gr.Image(label="Input with text", value="examples/dogs.jpg", type='pil')
segm_img_t = gr.Image(label="Segmented Image with text", interactive=False, type='pil')
global_points = []
global_point_label = []
input_size_slider = gr.components.Slider(minimum=512,
maximum=1024,
value=1024,
step=64,
label='Input_size',
info='The model was trained on a size of 1024')
with gr.Blocks(css=css, title='Fast Segment Anything') as demo:
with gr.Row():
with gr.Column(scale=1):
# Title
gr.Markdown(title)
with gr.Row():
with gr.Column(scale=1):
# Title
gr.Markdown(description)
with gr.Tab("Text mode"):
# Images
with gr.Row(variant="panel"):
with gr.Column(scale=1):
cond_img_t.render()
with gr.Column(scale=1):
segm_img_t.render()
# Submit & Clear
with gr.Row():
with gr.Column():
input_size_slider_t = gr.components.Slider(minimum=512,
maximum=1024,
value=1024,
step=64,
label='Input_size',
info='Our model was trained on a size of 1024')
with gr.Row():
with gr.Column():
contour_check = gr.Checkbox(value=True, label='withContours', info='draw the edges of the masks')
text_box = gr.Textbox(label="text prompt", value="a black dog")
with gr.Column():
segment_btn_t = gr.Button("Segment with text", variant='primary')
clear_btn_t = gr.Button("Clear", variant="secondary")
gr.Markdown("Click on the examples below")
gr.Examples(examples=[["examples/dogs.jpg"],["examples/cat.jpg"],["examples/fruit.jpg"],["examples/flower.jpg"],["examples/boat.jpg"],["examples/fruits.jpg"],],
inputs=[cond_img_t],
examples_per_page=6)
with gr.Column():
with gr.Accordion("Advanced options", open=False):
iou_threshold = gr.Slider(0.1, 0.9, 0.7, step=0.1, label='iou', info='iou threshold for filtering the annotations')
conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf', info='object confidence threshold')
with gr.Row():
mor_check = gr.Checkbox(value=False, label='better_visual_quality', info='better quality using morphologyEx')
retina_check = gr.Checkbox(value=True, label='use_retina', info='draw high-resolution segmentation masks')
wider_check = gr.Checkbox(value=False, label='wider', info='wider result')
segment_btn_t.click(segment_everything,
inputs=[
cond_img_t,
input_size_slider_t,
iou_threshold,
conf_threshold,
mor_check,
contour_check,
retina_check,
text_box,
wider_check,
],
outputs=segm_img_t)
def clear():
return None, None
def clear_text():
return None, None, None
# clear_btn_p.click(clear, outputs=[cond_img_p, segm_img_p])
clear_btn_t.click(clear_text, outputs=[text_box])
demo.queue()
demo.launch()
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