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from ultralytics import YOLO
import numpy as np
import matplotlib.pyplot as plt
import gradio as gr
import cv2
import torch
# import queue
# import threading
from PIL import Image
model = YOLO('checkpoints/FastSAM.pt') # load a custom model
def fast_process(annotations, image, high_quality, device):
if isinstance(annotations[0],dict):
annotations = [annotation['segmentation'] for annotation in annotations]
original_h = image.height
original_w = image.width
# fig = plt.figure(figsize=(10, 10))
# plt.imshow(image)
if high_quality == True:
if isinstance(annotations[0],torch.Tensor):
annotations = np.array(annotations.cpu())
for i, mask in enumerate(annotations):
mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
if device == 'cpu':
annotations = np.array(annotations)
inner_mask = fast_show_mask(annotations,
plt.gca(),
bbox=None,
points=None,
pointlabel=None,
retinamask=True,
target_height=original_h,
target_width=original_w)
else:
if isinstance(annotations[0],np.ndarray):
annotations = torch.from_numpy(annotations)
inner_mask = fast_show_mask_gpu(annotations,
plt.gca(),
bbox=None,
points=None,
pointlabel=None)
if isinstance(annotations, torch.Tensor):
annotations = annotations.cpu().numpy()
if high_quality:
contour_all = []
temp = np.zeros((original_h, original_w,1))
for i, mask in enumerate(annotations):
if type(mask) == dict:
mask = mask['segmentation']
annotation = mask.astype(np.uint8)
contours, _ = cv2.findContours(annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
contour_all.append(contour)
cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2)
color = np.array([0 / 255, 0 / 255, 255 / 255, 0.9])
contour_mask = temp / 255 * color.reshape(1, 1, -1)
# plt.imshow(contour_mask)
image = image.convert('RGBA')
overlay_inner = Image.fromarray((inner_mask * 255).astype(np.uint8), 'RGBA')
image.paste(overlay_inner, (0, 0), overlay_inner)
if high_quality:
overlay_contour = Image.fromarray((contour_mask * 255).astype(np.uint8), 'RGBA')
image.paste(overlay_contour, (0, 0), overlay_contour)
return image
# plt.axis('off')
# plt.tight_layout()
# return fig
# CPU post process
def fast_show_mask(annotation, ax, bbox=None,
points=None, pointlabel=None,
retinamask=True, target_height=960,
target_width=960):
msak_sum = annotation.shape[0]
height = annotation.shape[1]
weight = annotation.shape[2]
# 将annotation 按照面积 排序
areas = np.sum(annotation, axis=(1, 2))
sorted_indices = np.argsort(areas)[::1]
annotation = annotation[sorted_indices]
index = (annotation != 0).argmax(axis=0)
color = np.random.random((msak_sum,1,1,3))
transparency = np.ones((msak_sum,1,1,1)) * 0.6
visual = np.concatenate([color,transparency],axis=-1)
mask_image = np.expand_dims(annotation,-1) * visual
mask = np.zeros((height,weight,4))
h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing='ij')
indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
# 使用向量化索引更新show的值
mask[h_indices, w_indices, :] = mask_image[indices]
if bbox is not None:
x1, y1, x2, y2 = bbox
ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
# draw point
if points is not None:
plt.scatter([point[0] for i, point in enumerate(points) if pointlabel[i]==1], [point[1] for i, point in enumerate(points) if pointlabel[i]==1], s=20, c='y')
plt.scatter([point[0] for i, point in enumerate(points) if pointlabel[i]==0], [point[1] for i, point in enumerate(points) if pointlabel[i]==0], s=20, c='m')
if retinamask==False:
mask = cv2.resize(mask, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
# ax.imshow(mask)
return mask
def fast_show_mask_gpu(annotation, ax,
bbox=None, points=None,
pointlabel=None):
msak_sum = annotation.shape[0]
height = annotation.shape[1]
weight = annotation.shape[2]
areas = torch.sum(annotation, dim=(1, 2))
sorted_indices = torch.argsort(areas, descending=False)
annotation = annotation[sorted_indices]
# 找每个位置第一个非零值下标
index = (annotation != 0).to(torch.long).argmax(dim=0)
color = torch.rand((msak_sum,1,1,3)).to(annotation.device)
transparency = torch.ones((msak_sum,1,1,1)).to(annotation.device) * 0.6
visual = torch.cat([color,transparency],dim=-1)
mask_image = torch.unsqueeze(annotation,-1) * visual
# 按index取数，index指每个位置选哪个batch的数，把mask_image转成一个batch的形式
mask = torch.zeros((height,weight,4)).to(annotation.device)
h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight))
indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
# 使用向量化索引更新show的值
mask[h_indices, w_indices, :] = mask_image[indices]
mask_cpu = mask.cpu().numpy()
if bbox is not None:
x1, y1, x2, y2 = bbox
ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
# draw point
if points is not None:
plt.scatter([point[0] for i, point in enumerate(points) if pointlabel[i]==1], [point[1] for i, point in enumerate(points) if pointlabel[i]==1], s=20, c='y')
plt.scatter([point[0] for i, point in enumerate(points) if pointlabel[i]==0], [point[1] for i, point in enumerate(points) if pointlabel[i]==0], s=20, c='m')
# ax.imshow(mask_cpu)
return mask_cpu
# # 预测队列
# prediction_queue = queue.Queue(maxsize=5)
# # 线程锁
# lock = threading.Lock()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
def predict(input, input_size=1024, high_visual_quality=True):
input_size = int(input_size) # 确保 imgsz 是整数
# Thanks for the suggestion by hysts in HuggingFace.
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=0.7, conf=0.25, imgsz=input_size)
fig = fast_process(annotations=results[0].masks.data,
image=input, high_quality=high_visual_quality, device=device)
return fig
# input_size=1024
# high_quality_visual=True
# inp = 'assets/sa_192.jpg'
# input = Image.open(inp)
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
# input_size = int(input_size) # 确保 imgsz 是整数
# results = model(input, device=device, retina_masks=True, iou=0.7, conf=0.25, imgsz=input_size)
# pil_image = fast_process(annotations=results[0].masks.data,
# image=input, high_quality=high_quality_visual, device=device)
app_interface = gr.Interface(fn=predict,
inputs=[gr.Image(type='pil'),
gr.components.Slider(minimum=512, maximum=1024, value=1024, step=64, label='input_size'),
gr.components.Checkbox(value=True, label='high_visual_quality')],
# outputs=['plot'],
outputs=gr.Image(type='pil'),
examples=[["assets/sa_8776.jpg"]],
# # ["assets/sa_1309.jpg", 1024]],
# examples=[["assets/sa_192.jpg"], ["assets/sa_414.jpg"],
# ["assets/sa_561.jpg"], ["assets/sa_862.jpg"],
# ["assets/sa_1309.jpg"], ["assets/sa_8776.jpg"],
# ["assets/sa_10039.jpg"], ["assets/sa_11025.jpg"],],
cache_examples=True,
title="Fast Segment Anything (Everything mode)"
)
app_interface.queue(concurrency_count=1, max_size=20)
app_interface.launch() |