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from ultralytics import YOLO
import numpy as np
import matplotlib.pyplot as plt
import gradio as gr
import torch
model = YOLO('checkpoints/FastSAM.pt') # load a custom model
def format_results(result,filter = 0):
annotations = []
n = len(result.masks.data)
for i in range(n):
annotation = {}
mask = result.masks.data[i] == 1.0
if torch.sum(mask) < filter:
continue
annotation['id'] = i
annotation['segmentation'] = mask.cpu().numpy()
annotation['bbox'] = result.boxes.data[i]
annotation['score'] = result.boxes.conf[i]
annotation['area'] = annotation['segmentation'].sum()
annotations.append(annotation)
return annotations
def show_mask(annotation, ax, random_color=True, bbox=None, points=None):
if random_color : # random mask color
color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
else:
color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])
if type(annotation) == dict:
annotation = annotation['segmentation']
mask = annotation
h, w = mask.shape[-2:]
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
# draw box
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:
ax.scatter([point[0] for point in points], [point[1] for point in points], s=10, c='g')
ax.imshow(mask_image)
return mask_image
def post_process(annotations, image, mask_random_color=True, bbox=None, points=None):
fig = plt.figure(figsize=(10, 10))
plt.imshow(image)
for i, mask in enumerate(annotations):
show_mask(mask, plt.gca(),random_color=mask_random_color,bbox=bbox,points=points)
plt.axis('off')
plt.tight_layout()
return fig
# post_process(results[0].masks, Image.open("../data/cake.png"))
def predict(inp, input_size):
input_size = int(input_size) # 确保 imgsz 是整数
results = model(inp, device='cpu', retina_masks=True, iou=0.7, conf=0.25, imgsz=input_size)
results = format_results(results[0], 100)
results.sort(key=lambda x: x['area'], reverse=True)
pil_image = post_process(annotations=results, image=inp)
return pil_image
# inp = 'assets/sa_192.jpg'
# results = model(inp, device='cpu', retina_masks=True, iou=0.7, conf=0.25, imgsz=1024)
# results = format_results(results[0], 100)
# post_process(annotations=results, image_path=inp)
demo = gr.Interface(fn=predict,
inputs=[gr.inputs.Image(type='pil'), gr.inputs.Dropdown(choices=[512, 800, 1024])],
outputs=['plot'],
examples=[["assets/sa_8776.jpg", 1024]],
# ["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"],],
)
demo.launch() |