ImageEditAnything / segmenter /base_segmenter.py
weijiawu's picture
Duplicate from TencentARC/Caption-Anything
0ab9a32
raw
history blame
No virus
5.75 kB
import time
import torch
import cv2
from PIL import Image, ImageDraw, ImageOps
import numpy as np
from typing import Union
from segment_anything import sam_model_registry, SamPredictor, SamAutomaticMaskGenerator
import matplotlib.pyplot as plt
import PIL
class BaseSegmenter:
def __init__(self, device, checkpoint, model_type='vit_h', reuse_feature = True, model=None):
print(f"Initializing BaseSegmenter to {device}")
self.device = device
self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
self.processor = None
self.model_type = model_type
if model is None:
self.checkpoint = checkpoint
self.model = sam_model_registry[self.model_type](checkpoint=self.checkpoint)
self.model.to(device=self.device)
else:
self.model = model
self.reuse_feature = reuse_feature
self.predictor = SamPredictor(self.model)
self.mask_generator = SamAutomaticMaskGenerator(self.model)
self.image_embedding = None
self.image = None
@torch.no_grad()
def set_image(self, image: Union[np.ndarray, Image.Image, str]):
if type(image) == str: # input path
image = Image.open(image)
image = np.array(image)
elif type(image) == Image.Image:
image = np.array(image)
self.image = image
if self.reuse_feature:
self.predictor.set_image(image)
self.image_embedding = self.predictor.get_image_embedding()
print(self.image_embedding.shape)
@torch.no_grad()
def inference(self, image, control):
if 'everything' in control['prompt_type']:
masks = self.mask_generator.generate(image)
new_masks = np.concatenate([mask["segmentation"][np.newaxis,:] for mask in masks])
return new_masks
else:
if not self.reuse_feature or self.image_embedding is None:
self.set_image(image)
self.predictor.set_image(self.image)
else:
assert self.image_embedding is not None
self.predictor.features = self.image_embedding
if 'mutimask_output' in control:
masks, scores, logits = self.predictor.predict(
point_coords = np.array(control['input_point']),
point_labels = np.array(control['input_label']),
multimask_output = True,
)
elif 'input_boxes' in control:
transformed_boxes = self.predictor.transform.apply_boxes_torch(
torch.tensor(control["input_boxes"], device=self.predictor.device),
image.shape[:2]
)
masks, _, _ = self.predictor.predict_torch(
point_coords=None,
point_labels=None,
boxes=transformed_boxes,
multimask_output=False,
)
masks = masks.squeeze(1).cpu().numpy()
else:
input_point = np.array(control['input_point']) if 'click' in control['prompt_type'] else None
input_label = np.array(control['input_label']) if 'click' in control['prompt_type'] else None
input_box = np.array(control['input_box']) if 'box' in control['prompt_type'] else None
masks, scores, logits = self.predictor.predict(
point_coords = input_point,
point_labels = input_label,
box = input_box,
multimask_output = False,
)
if 0 in control['input_label']:
mask_input = logits[np.argmax(scores), :, :]
masks, scores, logits = self.predictor.predict(
point_coords=input_point,
point_labels=input_label,
box = input_box,
mask_input=mask_input[None, :, :],
multimask_output=False,
)
return masks
if __name__ == "__main__":
image_path = 'segmenter/images/truck.jpg'
prompts = [
# {
# "prompt_type":["click"],
# "input_point":[[500, 375]],
# "input_label":[1],
# "multimask_output":"True",
# },
{
"prompt_type":["click"],
"input_point":[[1000, 600], [1325, 625]],
"input_label":[1, 0],
},
# {
# "prompt_type":["click", "box"],
# "input_box":[425, 600, 700, 875],
# "input_point":[[575, 750]],
# "input_label": [0]
# },
# {
# "prompt_type":["box"],
# "input_boxes": [
# [75, 275, 1725, 850],
# [425, 600, 700, 875],
# [1375, 550, 1650, 800],
# [1240, 675, 1400, 750],
# ]
# },
# {
# "prompt_type":["everything"]
# },
]
init_time = time.time()
segmenter = BaseSegmenter(
device='cuda',
# checkpoint='sam_vit_h_4b8939.pth',
checkpoint='segmenter/sam_vit_h_4b8939.pth',
model_type='vit_h',
reuse_feature=True
)
print(f'init time: {time.time() - init_time}')
image_path = 'test_img/img2.jpg'
infer_time = time.time()
for i, prompt in enumerate(prompts):
print(f'{prompt["prompt_type"]} mode')
image = Image.open(image_path)
segmenter.set_image(np.array(image))
masks = segmenter.inference(np.array(image), prompt)
Image.fromarray(masks[0]).save('seg.png')
print(masks.shape)
print(f'infer time: {time.time() - infer_time}')