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gradio_medsam.py
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import gradio as gr
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import matplotlib.pyplot as plt
import cv2
import torch
import torchvision
import os, sys
import warnings
from scipy import ndimage
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import os, sys
from scipy import ndimage
from fastsam import FastSAM, FastSAMPrompt
device = "cuda" #torch.device("cuda" if torch.cuda.is_available() else "cpu")
import torch
import random
from argparse import Namespace
from segment_anything.predictor_sammed import SammedPredictor
from segment_anything import sam_model_registry
from run_old import *
segment_models = Segment_Serious_Models()
import os
import cv2
import numpy as np
import gradio as gr
# from inference import run_inference
# points color and marker
colors = [(255, 0, 0), (0, 255, 0)]
markers = [1, 5]
# image examples
# in each list, the first element is image path,
# the second is id (used for original_image State),
# the third is an empty list (used for selected_points State)
image_examples = []
for ith, img in enumerate(os.listdir("Dataset_Demo/images/")):
image_examples.append([os.path.join("Dataset_Demo/images/", img), None, [], None])
sam_med2d_b = load_model(256, True, "vit_b", "pretrain_model/sam-med2d_b.pth")
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown(
'''# Segment Anything!🚀
The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. More information can be found in [**Official Project**](https://segment-anything.com/).
[](https://huggingface.co/spaces/AIBoy1993/segment_anything_webui?duplicate=true)
'''
)
with gr.Row():
# select model
model_type = gr.Dropdown(["SAM-Med2d_base"], value='SAM-Med2d_base', label="Select Model")
# select compare model
compare_model_type = gr.Dropdown(["SAM_base", "SAM_large", "FastSAM"], value='SAM_base', label="Select compare Model")
# SAM parameters
# with gr.Accordion(label='Parameters', open=False):
# with gr.Row():
# points_per_side = gr.Number(value=32, label="points_per_side", precision=0,
# info='''The number of points to be sampled along one side of the image. The total
# number of points is points_per_side**2.''')
# pred_iou_thresh = gr.Slider(value=0.88, minimum=0, maximum=1.0, step=0.01, label="pred_iou_thresh",
# info='''A filtering threshold in [0,1], using the model's predicted mask quality.''')
# stability_score_thresh = gr.Slider(value=0.95, minimum=0, maximum=1.0, step=0.01, label="stability_score_thresh",
# info='''A filtering threshold in [0,1], using the stability of the mask under
# changes to the cutoff used to binarize the model's mask predictions.''')
# min_mask_region_area = gr.Number(value=0, label="min_mask_region_area", precision=0,
# info='''If >0, postprocessing will be applied to remove disconnected regions
# and holes in masks with area smaller than min_mask_region_area.''')
# with gr.Row():
# stability_score_offset = gr.Number(value=1, label="stability_score_offset",
# info='''The amount to shift the cutoff when calculated the stability score.''')
# box_nms_thresh = gr.Slider(value=0.7, minimum=0, maximum=1.0, step=0.01, label="box_nms_thresh",
# info='''The box IoU cutoff used by non-maximal ression to filter duplicate masks.''')
# crop_n_layers = gr.Number(value=0, label="crop_n_layers", precision=0,
# info='''If >0, mask prediction will be run again on crops of the image.
# Sets the number of layers to run, where each layer has 2**i_layer number of image crops.''')
# crop_nms_thresh = gr.Slider(value=0.7, minimum=0, maximum=1.0, step=0.01, label="crop_nms_thresh",
# info='''The box IoU cutoff used by non-maximal suppression to filter duplicate
# masks between different crops.''')
# Segment image
with gr.Tab(label='Image'):
with gr.Row().style(equal_height=True):
with gr.Column():
# input image
original_image = gr.State(value=None) # store original image without points, default None
input_image = gr.Image(type="numpy")
# point prompt
with gr.Column():
selected_points = gr.State([]) # store points
last_mask = gr.State(None)
with gr.Row():
gr.Markdown('You can click on the image to select points prompt. Default: foreground_point.')
undo_button = gr.Button('Undo point')
radio = gr.Radio(['foreground_point', 'background_point'], label='point labels')
# text prompt to generate box prompt
# text = gr.Textbox(label='Text prompt(optional)', info=
# 'If you type words, the OWL-ViT model will be used to detect the objects in the image, '
# 'and the boxes will be feed into SAM model to predict mask. Please use English.',
# placeholder='Multiple words are separated by commas')
# owl_vit_threshold = gr.Slider(value=0.1, minimum=0, maximum=1.0, step=0.01, label="OWL ViT Object Detection threshold",
# info='''A small threshold will generate more objects, but may causing OOM.
# A big threshold may not detect objects, resulting in an error ''')
# run button
button = gr.Button("Auto!")
# show the image with mask
gallery_sammed = gr.Gallery(
label="SAMMED Generated images", show_label=True, elem_id="gallery_sammed").style(preview=True, grid=2,object_fit="scale-down")
# with gr.Tab(label='Image+Mask'):
# output_image = gr.Image(type='numpy')
# # show only mask
# with gr.Tab(label='Mask'):
# output_mask = gr.Image(type='numpy')
def process_example(img, ori_img, sel_p, last_mask):
return img, []
# example = gr.Examples(
# examples=image_examples,
# inputs=[input_image, original_image, selected_points],
# outputs=[original_image, selected_points],
# fn=process_example,
# run_on_click=True
# )
with gr.Row():
demo_image_root_path= "Dataset_Demo/images/"
input_examples = [os.path.join(demo_image_root_path, img) for img in os.listdir(demo_image_root_path)]
with gr.Column():
# gr.Examples(input_examples, inputs=input_image)
gr.Examples(examples=image_examples, inputs=[input_image, original_image, selected_points, last_mask], outputs=[original_image, selected_points], fn=process_example, run_on_click=True)
# once user upload an image, the original image is stored in `original_image`
def store_img(img):
return img, [], None # when new image is uploaded, `selected_points` should be empty
input_image.upload(
store_img,
[input_image],
[original_image, selected_points, last_mask]
)
# user click the image to get points, and show the points on the image
def get_point(img, sel_pix, point_type, evt: gr.SelectData):
if point_type == 'foreground_point':
sel_pix.append((evt.index, 1)) # append the foreground_point
elif point_type == 'background_point':
sel_pix.append((evt.index, 0)) # append the background_point
else:
sel_pix.append((evt.index, 1)) # default foreground_point
# draw points
for point, label in sel_pix:
cv2.drawMarker(img, point, colors[label], markerType=markers[label], markerSize=20, thickness=5)
if img[..., 0][0, 0] == img[..., 2][0, 0]: # BGR to RGB
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return img if isinstance(img, np.ndarray) else np.array(img)
input_image.select(
get_point,
[input_image, selected_points, radio],
[input_image],
)
# undo the selected point
def undo_points(orig_img, sel_pix):
if isinstance(orig_img, int): # if orig_img is int, the image if select from examples
temp = cv2.imread(image_examples[orig_img][0])
temp = cv2.cvtColor(temp, cv2.COLOR_BGR2RGB)
else:
temp = orig_img.copy()
# draw points
if len(sel_pix) != 0:
sel_pix.pop()
for point, label in sel_pix:
cv2.drawMarker(temp, point, colors[label], markerType=markers[label], markerSize=20, thickness=5)
if temp[..., 0][0, 0] == temp[..., 2][0, 0]: # BGR to RGB
temp = cv2.cvtColor(temp, cv2.COLOR_BGR2RGB)
return temp if isinstance(temp, np.ndarray) else np.array(temp)
undo_button.click(
undo_points,
[original_image, selected_points],
[input_image]
)
# else:
# print("task_type:{} error!".format(task_type))
# return None, None
# button image
button.click(segment_models.run_sammed, inputs=[model_type, original_image, selected_points, last_mask],
outputs=[gallery_sammed, last_mask])
demo.queue().launch(debug=True, server_name='0.0.0.0', server_port=7860)