Spaces:
Build error
Build error
Upload 4 files
Browse files- README.md +18 -16
- app.py +15 -74
- inference.py +156 -0
- requirements.txt +1 -2
README.md
CHANGED
@@ -1,25 +1,18 @@
|
|
1 |
-
---
|
2 |
-
title: Segment Anything
|
3 |
-
emoji: 🚀
|
4 |
-
colorFrom: gray
|
5 |
-
colorTo: pink
|
6 |
-
sdk: gradio
|
7 |
-
sdk_version: 3.24.1
|
8 |
-
app_file: app.py
|
9 |
-
pinned: false
|
10 |
-
---
|
11 |
-
|
12 |
# Segment Anything WebUI
|
13 |
|
14 |
-
This project is based on **[Segment Anything Model](https://segment-anything.com/)
|
15 |
|
16 |
- Try deme on HF: [AIBoy1993/segment_anything_webui](https://huggingface.co/spaces/AIBoy1993/segment_anything_webui)
|
|
|
17 |
|
18 |
![](./images/20230408023615.png)
|
19 |
|
20 |
## Change Logs
|
21 |
|
22 |
-
- [2023-4-11]
|
|
|
|
|
|
|
23 |
|
24 |
## **Usage**
|
25 |
|
@@ -45,16 +38,25 @@ git clone https://github.com/5663015/segment_anything_webui.git
|
|
45 |
|
46 |
- `vit_b`: [ViT-B SAM model](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth)
|
47 |
|
|
|
48 |
- Run:
|
49 |
|
50 |
```
|
51 |
python app.py
|
52 |
```
|
53 |
|
54 |
-
**Note:** Default model is `vit_b`,the demo can run on CPU. Default device is `
|
55 |
|
56 |
## TODO
|
57 |
|
58 |
-
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
59 |
|
60 |
-
- Add text prompt
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
# Segment Anything WebUI
|
2 |
|
3 |
+
This project is based on **[Segment Anything Model](https://segment-anything.com/)** by Meta. The UI is based on [Gradio](https://gradio.app/).
|
4 |
|
5 |
- Try deme on HF: [AIBoy1993/segment_anything_webui](https://huggingface.co/spaces/AIBoy1993/segment_anything_webui)
|
6 |
+
- [GitHub](https://github.com/5663015/segment_anything_webui)
|
7 |
|
8 |
![](./images/20230408023615.png)
|
9 |
|
10 |
## Change Logs
|
11 |
|
12 |
+
- [2023-4-11]
|
13 |
+
- Support video segmentation. A short video can be automatically segmented by SAM.
|
14 |
+
- Support text prompt segmentation using [OWL-ViT](https://huggingface.co/docs/transformers/v4.27.2/en/model_doc/owlvit#overview) (Vision Transformer for Open-World Localization) model.
|
15 |
+
|
16 |
|
17 |
## **Usage**
|
18 |
|
|
|
38 |
|
39 |
- `vit_b`: [ViT-B SAM model](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth)
|
40 |
|
41 |
+
- Under `checkpoints`, make a new folder named `models--google--owlvit-base-patch32`, and put the downloaded [OWL-ViT weights](https://huggingface.co/google/owlvit-base-patch32) files in `models--google--owlvit-base-patch32`.
|
42 |
- Run:
|
43 |
|
44 |
```
|
45 |
python app.py
|
46 |
```
|
47 |
|
48 |
+
**Note:** Default model is `vit_b`,the demo can run on CPU. Default device is `cpu`。
|
49 |
|
50 |
## TODO
|
51 |
|
52 |
+
- [x] Video segmentation
|
53 |
+
|
54 |
+
- [x] Add text prompt
|
55 |
+
|
56 |
+
- [ ] Add segmentation prompt (point and box)
|
57 |
+
|
58 |
+
## Reference
|
59 |
+
|
60 |
+
- Thanks to the wonderful work [Segment Anything](https://segment-anything.com/) and [OWL-ViT](https://arxiv.org/abs/2205.06230)
|
61 |
+
- Some video processing code references [kadirnar/segment-anything-video](https://github.com/kadirnar/segment-anything-video), and some OWL-ViT code references [ngthanhtin/owlvit_segment_anything](https://github.com/ngthanhtin/owlvit_segment_anything).
|
62 |
|
|
app.py
CHANGED
@@ -1,73 +1,8 @@
|
|
1 |
import os
|
2 |
-
import cv2
|
3 |
-
import sys
|
4 |
-
import numpy as np
|
5 |
import gradio as gr
|
6 |
-
from
|
7 |
-
import matplotlib.pyplot as plt
|
8 |
-
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
|
9 |
|
10 |
|
11 |
-
models = {
|
12 |
-
'vit_b': './checkpoints/sam_vit_b_01ec64.pth',
|
13 |
-
'vit_l': './checkpoints/sam_vit_l_0b3195.pth',
|
14 |
-
'vit_h': './checkpoints/sam_vit_h_4b8939.pth'
|
15 |
-
}
|
16 |
-
|
17 |
-
|
18 |
-
def segment_one(img, mask_generator, seed=None):
|
19 |
-
if seed is not None:
|
20 |
-
np.random.seed(seed)
|
21 |
-
masks = mask_generator.generate(img)
|
22 |
-
sorted_anns = sorted(masks, key=(lambda x: x['area']), reverse=True)
|
23 |
-
mask_all = np.ones((img.shape[0], img.shape[1], 3))
|
24 |
-
for ann in sorted_anns:
|
25 |
-
m = ann['segmentation']
|
26 |
-
color_mask = np.random.random((1, 3)).tolist()[0]
|
27 |
-
for i in range(3):
|
28 |
-
mask_all[m == True, i] = color_mask[i]
|
29 |
-
result = img / 255 * 0.3 + mask_all * 0.7
|
30 |
-
return result, mask_all
|
31 |
-
|
32 |
-
|
33 |
-
def inference(device, model_type, points_per_side, pred_iou_thresh, stability_score_thresh, min_mask_region_area,
|
34 |
-
stability_score_offset, box_nms_thresh, crop_n_layers, crop_nms_thresh, input_x, progress=gr.Progress()):
|
35 |
-
# sam model
|
36 |
-
sam = sam_model_registry[model_type](checkpoint=models[model_type]).to(device)
|
37 |
-
mask_generator = SamAutomaticMaskGenerator(
|
38 |
-
sam,
|
39 |
-
points_per_side=points_per_side,
|
40 |
-
pred_iou_thresh=pred_iou_thresh,
|
41 |
-
stability_score_thresh=stability_score_thresh,
|
42 |
-
stability_score_offset=stability_score_offset,
|
43 |
-
box_nms_thresh=box_nms_thresh,
|
44 |
-
crop_n_layers=crop_n_layers,
|
45 |
-
crop_nms_thresh=crop_nms_thresh,
|
46 |
-
crop_overlap_ratio=512 / 1500,
|
47 |
-
crop_n_points_downscale_factor=1,
|
48 |
-
point_grids=None,
|
49 |
-
min_mask_region_area=min_mask_region_area,
|
50 |
-
output_mode='binary_mask'
|
51 |
-
)
|
52 |
-
|
53 |
-
# input is image, type: numpy
|
54 |
-
if type(input_x) == np.ndarray:
|
55 |
-
result, mask_all = segment_one(input_x, mask_generator)
|
56 |
-
return result, mask_all
|
57 |
-
elif isinstance(input_x, str): # input is video, type: path (str)
|
58 |
-
cap = cv2.VideoCapture(input_x) # read video
|
59 |
-
frames_num = cap.get(cv2.CAP_PROP_FRAME_COUNT)
|
60 |
-
W, H = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
61 |
-
fps = int(cap.get(cv2.CAP_PROP_FPS))
|
62 |
-
out = cv2.VideoWriter("output.mp4", cv2.VideoWriter_fourcc('x', '2', '6', '4'), fps, (W, H), isColor=True)
|
63 |
-
for _ in progress.tqdm(range(int(frames_num)), desc='Processing video ({} frames, size {}x{})'.format(int(frames_num), W, H)):
|
64 |
-
ret, frame = cap.read() # read a frame
|
65 |
-
result, mask_all = segment_one(frame, mask_generator, seed=2023)
|
66 |
-
result = (result * 255).astype(np.uint8)
|
67 |
-
out.write(result)
|
68 |
-
out.release()
|
69 |
-
cap.release()
|
70 |
-
return 'output.mp4'
|
71 |
|
72 |
|
73 |
with gr.Blocks() as demo:
|
@@ -82,9 +17,9 @@ with gr.Blocks() as demo:
|
|
82 |
# select model
|
83 |
model_type = gr.Dropdown(["vit_b", "vit_l", "vit_h"], value='vit_b', label="Select Model")
|
84 |
# select device
|
85 |
-
device = gr.Dropdown(["cpu"], value='cpu', label="Select Device")
|
86 |
|
87 |
-
#
|
88 |
with gr.Accordion(label='Parameters', open=False):
|
89 |
with gr.Row():
|
90 |
points_per_side = gr.Number(value=32, label="points_per_side", precision=0,
|
@@ -115,8 +50,14 @@ with gr.Blocks() as demo:
|
|
115 |
with gr.Row().style(equal_height=True):
|
116 |
with gr.Column():
|
117 |
input_image = gr.Image(type="numpy")
|
118 |
-
|
119 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
120 |
with gr.Tab(label='Image+Mask'):
|
121 |
output_image = gr.Image(type='numpy')
|
122 |
with gr.Tab(label='Mask'):
|
@@ -157,14 +98,14 @@ with gr.Blocks() as demo:
|
|
157 |
)
|
158 |
|
159 |
# button image
|
160 |
-
button.click(
|
161 |
min_mask_region_area, stability_score_offset, box_nms_thresh, crop_n_layers,
|
162 |
-
crop_nms_thresh, input_image],
|
163 |
outputs=[output_image, output_mask])
|
164 |
# button video
|
165 |
-
button_video.click(
|
166 |
min_mask_region_area, stability_score_offset, box_nms_thresh, crop_n_layers,
|
167 |
-
crop_nms_thresh, input_video],
|
168 |
outputs=[output_video])
|
169 |
|
170 |
|
|
|
1 |
import os
|
|
|
|
|
|
|
2 |
import gradio as gr
|
3 |
+
from inference import run_inference
|
|
|
|
|
4 |
|
5 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
6 |
|
7 |
|
8 |
with gr.Blocks() as demo:
|
|
|
17 |
# select model
|
18 |
model_type = gr.Dropdown(["vit_b", "vit_l", "vit_h"], value='vit_b', label="Select Model")
|
19 |
# select device
|
20 |
+
device = gr.Dropdown(["cpu", "cuda"], value='cpu', label="Select Device")
|
21 |
|
22 |
+
# parameters
|
23 |
with gr.Accordion(label='Parameters', open=False):
|
24 |
with gr.Row():
|
25 |
points_per_side = gr.Number(value=32, label="points_per_side", precision=0,
|
|
|
50 |
with gr.Row().style(equal_height=True):
|
51 |
with gr.Column():
|
52 |
input_image = gr.Image(type="numpy")
|
53 |
+
text = gr.Textbox(label='Text prompt(optional)', info=
|
54 |
+
'If you type words, the OWL-ViT model will be used to detect the objects in the image, '
|
55 |
+
'and the boxes will be feed into SAM model to predict mask. Please use English.',
|
56 |
+
placeholder='Multiple words are separated by commas')
|
57 |
+
owl_vit_threshold = gr.Slider(value=0.1, minimum=0, maximum=1.0, step=0.01, label="OWL ViT Object Detection threshold",
|
58 |
+
info='''A small threshold will generate more objects, but may causing OOM.
|
59 |
+
A big threshold may not detect objects, resulting in an error ''')
|
60 |
+
button = gr.Button("Auto!")
|
61 |
with gr.Tab(label='Image+Mask'):
|
62 |
output_image = gr.Image(type='numpy')
|
63 |
with gr.Tab(label='Mask'):
|
|
|
98 |
)
|
99 |
|
100 |
# button image
|
101 |
+
button.click(run_inference, inputs=[device, model_type, points_per_side, pred_iou_thresh, stability_score_thresh,
|
102 |
min_mask_region_area, stability_score_offset, box_nms_thresh, crop_n_layers,
|
103 |
+
crop_nms_thresh, owl_vit_threshold, input_image, text],
|
104 |
outputs=[output_image, output_mask])
|
105 |
# button video
|
106 |
+
button_video.click(run_inference, inputs=[device, model_type, points_per_side, pred_iou_thresh, stability_score_thresh,
|
107 |
min_mask_region_area, stability_score_offset, box_nms_thresh, crop_n_layers,
|
108 |
+
crop_nms_thresh, owl_vit_threshold, input_video, text],
|
109 |
outputs=[output_video])
|
110 |
|
111 |
|
inference.py
ADDED
@@ -0,0 +1,156 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import cv2
|
2 |
+
import torch
|
3 |
+
import numpy as np
|
4 |
+
import gradio as gr
|
5 |
+
from PIL import Image, ImageDraw
|
6 |
+
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor
|
7 |
+
from transformers import OwlViTProcessor, OwlViTForObjectDetection
|
8 |
+
import gc
|
9 |
+
|
10 |
+
models = {
|
11 |
+
'vit_b': './checkpoints/sam_vit_b_01ec64.pth',
|
12 |
+
'vit_l': './checkpoints/sam_vit_l_0b3195.pth',
|
13 |
+
'vit_h': './checkpoints/sam_vit_h_4b8939.pth'
|
14 |
+
}
|
15 |
+
|
16 |
+
|
17 |
+
def plot_boxes(img, boxes):
|
18 |
+
img_pil = Image.fromarray(np.uint8(img * 255)).convert('RGB')
|
19 |
+
draw = ImageDraw.Draw(img_pil)
|
20 |
+
for box in boxes:
|
21 |
+
color = tuple(np.random.randint(0, 255, size=3).tolist())
|
22 |
+
x0, y0, x1, y1 = box
|
23 |
+
x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1)
|
24 |
+
draw.rectangle([x0, y0, x1, y1], outline=color, width=6)
|
25 |
+
return img_pil
|
26 |
+
|
27 |
+
|
28 |
+
def segment_one(img, mask_generator, seed=None):
|
29 |
+
if seed is not None:
|
30 |
+
np.random.seed(seed)
|
31 |
+
masks = mask_generator.generate(img)
|
32 |
+
sorted_anns = sorted(masks, key=(lambda x: x['area']), reverse=True)
|
33 |
+
mask_all = np.ones((img.shape[0], img.shape[1], 3))
|
34 |
+
for ann in sorted_anns:
|
35 |
+
m = ann['segmentation']
|
36 |
+
color_mask = np.random.random((1, 3)).tolist()[0]
|
37 |
+
for i in range(3):
|
38 |
+
mask_all[m == True, i] = color_mask[i]
|
39 |
+
result = img / 255 * 0.3 + mask_all * 0.7
|
40 |
+
return result, mask_all
|
41 |
+
|
42 |
+
|
43 |
+
def generator_inference(device, model_type, points_per_side, pred_iou_thresh, stability_score_thresh,
|
44 |
+
min_mask_region_area, stability_score_offset, box_nms_thresh, crop_n_layers, crop_nms_thresh,
|
45 |
+
input_x, progress=gr.Progress()):
|
46 |
+
# sam model
|
47 |
+
sam = sam_model_registry[model_type](checkpoint=models[model_type]).to(device)
|
48 |
+
mask_generator = SamAutomaticMaskGenerator(
|
49 |
+
sam,
|
50 |
+
points_per_side=points_per_side,
|
51 |
+
pred_iou_thresh=pred_iou_thresh,
|
52 |
+
stability_score_thresh=stability_score_thresh,
|
53 |
+
stability_score_offset=stability_score_offset,
|
54 |
+
box_nms_thresh=box_nms_thresh,
|
55 |
+
crop_n_layers=crop_n_layers,
|
56 |
+
crop_nms_thresh=crop_nms_thresh,
|
57 |
+
crop_overlap_ratio=512 / 1500,
|
58 |
+
crop_n_points_downscale_factor=1,
|
59 |
+
point_grids=None,
|
60 |
+
min_mask_region_area=min_mask_region_area,
|
61 |
+
output_mode='binary_mask'
|
62 |
+
)
|
63 |
+
|
64 |
+
# input is image, type: numpy
|
65 |
+
if type(input_x) == np.ndarray:
|
66 |
+
result, mask_all = segment_one(input_x, mask_generator)
|
67 |
+
return result, mask_all
|
68 |
+
elif isinstance(input_x, str): # input is video, type: path (str)
|
69 |
+
cap = cv2.VideoCapture(input_x) # read video
|
70 |
+
frames_num = cap.get(cv2.CAP_PROP_FRAME_COUNT)
|
71 |
+
W, H = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
72 |
+
fps = int(cap.get(cv2.CAP_PROP_FPS))
|
73 |
+
out = cv2.VideoWriter("output.mp4", cv2.VideoWriter_fourcc('x', '2', '6', '4'), fps, (W, H), isColor=True)
|
74 |
+
for _ in progress.tqdm(range(int(frames_num)),
|
75 |
+
desc='Processing video ({} frames, size {}x{})'.format(int(frames_num), W, H)):
|
76 |
+
ret, frame = cap.read() # read a frame
|
77 |
+
result, mask_all = segment_one(frame, mask_generator, seed=2023)
|
78 |
+
result = (result * 255).astype(np.uint8)
|
79 |
+
out.write(result)
|
80 |
+
out.release()
|
81 |
+
cap.release()
|
82 |
+
return 'output.mp4'
|
83 |
+
|
84 |
+
|
85 |
+
def predictor_inference(device, model_type, input_x, input_text, owl_vit_threshold=0.1):
|
86 |
+
# sam model
|
87 |
+
sam = sam_model_registry[model_type](checkpoint=models[model_type]).to(device)
|
88 |
+
predictor = SamPredictor(sam)
|
89 |
+
predictor.set_image(input_x) # Process the image to produce an image embedding
|
90 |
+
|
91 |
+
# split input text
|
92 |
+
input_text = [input_text.split(',')]
|
93 |
+
|
94 |
+
# OWL-ViT model
|
95 |
+
# processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
|
96 |
+
# owlvit_model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32").to(device)
|
97 |
+
processor = OwlViTProcessor.from_pretrained('./checkpoints/models--google--owlvit-base-patch32')
|
98 |
+
owlvit_model = OwlViTForObjectDetection.from_pretrained("./checkpoints/models--google--owlvit-base-patch32").to(device)
|
99 |
+
|
100 |
+
# get outputs
|
101 |
+
input_text = processor(text=input_text, images=input_x, return_tensors="pt").to(device)
|
102 |
+
outputs = owlvit_model(**input_text)
|
103 |
+
target_size = torch.Tensor([input_x.shape[:2]]).to(device)
|
104 |
+
results = processor.post_process_object_detection(outputs=outputs, target_sizes=target_size,
|
105 |
+
threshold=owl_vit_threshold)
|
106 |
+
|
107 |
+
# get the box with best score
|
108 |
+
scores = torch.sigmoid(outputs.logits)
|
109 |
+
# best_scores, best_idxs = torch.topk(scores, k=1, dim=1)
|
110 |
+
# best_idxs = best_idxs.squeeze(1).tolist()
|
111 |
+
|
112 |
+
i = 0 # Retrieve predictions for the first image for the corresponding text queries
|
113 |
+
boxes_tensor = results[i]["boxes"] # [best_idxs]
|
114 |
+
print(boxes_tensor.size())
|
115 |
+
boxes = boxes_tensor.cpu().detach().numpy()
|
116 |
+
transformed_boxes = predictor.transform.apply_boxes_torch(torch.Tensor(boxes).to(device),
|
117 |
+
input_x.shape[:2]) # apply transform to original boxes
|
118 |
+
|
119 |
+
# predict segmentation according to the boxes
|
120 |
+
masks, scores, logits = predictor.predict_torch(
|
121 |
+
point_coords=None,
|
122 |
+
point_labels=None,
|
123 |
+
boxes=transformed_boxes, # only one box
|
124 |
+
multimask_output=False,
|
125 |
+
)
|
126 |
+
masks = masks.cpu().detach().numpy()
|
127 |
+
mask_all = np.ones((input_x.shape[0], input_x.shape[1], 3))
|
128 |
+
for ann in masks:
|
129 |
+
color_mask = np.random.random((1, 3)).tolist()[0]
|
130 |
+
for i in range(3):
|
131 |
+
mask_all[ann[0] == True, i] = color_mask[i]
|
132 |
+
img = input_x / 255 * 0.3 + mask_all * 0.7
|
133 |
+
img = plot_boxes(img, boxes_tensor) # image + mask + boxes
|
134 |
+
|
135 |
+
# free the memory
|
136 |
+
owlvit_model.cpu()
|
137 |
+
del owlvit_model
|
138 |
+
del input_text
|
139 |
+
gc.collect()
|
140 |
+
torch.cuda.empty_cache()
|
141 |
+
|
142 |
+
return img, mask_all
|
143 |
+
|
144 |
+
|
145 |
+
def run_inference(device, model_type, points_per_side, pred_iou_thresh, stability_score_thresh, min_mask_region_area,
|
146 |
+
stability_score_offset, box_nms_thresh, crop_n_layers, crop_nms_thresh, owl_vit_threshold, input_x,
|
147 |
+
input_text):
|
148 |
+
print('prompt text: ', input_text)
|
149 |
+
if input_text != '' and not isinstance(input_x, str): # user input text
|
150 |
+
print('use predictor_inference')
|
151 |
+
return predictor_inference(device, model_type, input_x, input_text, owl_vit_threshold)
|
152 |
+
else:
|
153 |
+
print('use generator_inference')
|
154 |
+
return generator_inference(device, model_type, points_per_side, pred_iou_thresh, stability_score_thresh,
|
155 |
+
min_mask_region_area, stability_score_offset, box_nms_thresh, crop_n_layers,
|
156 |
+
crop_nms_thresh, input_x)
|
requirements.txt
CHANGED
@@ -3,5 +3,4 @@ numpy==1.21.5
|
|
3 |
opencv_python==4.6.0.66
|
4 |
Pillow==9.5.0
|
5 |
segment_anything==1.0
|
6 |
-
|
7 |
-
torchvision
|
|
|
3 |
opencv_python==4.6.0.66
|
4 |
Pillow==9.5.0
|
5 |
segment_anything==1.0
|
6 |
+
transformers==4.27.4
|
|