Spaces:
Runtime error
Runtime error
File size: 9,375 Bytes
cfa5142 0f36b51 cfa5142 41938cd cfa5142 d3e66e1 cfa5142 2c719e3 cfa5142 5f8864d cfa5142 c5a4e30 cfa5142 5f8864d cfa5142 60434a4 2878798 cfa5142 0f36b51 c5a4e30 cfa5142 8d612b7 cfa5142 8d612b7 cfa5142 0f36b51 2878798 0f36b51 cfa5142 0f36b51 8d52a7d 2878798 cfa5142 2c719e3 87a101a 5f8864d 0f36b51 87a101a cfa5142 2c719e3 41938cd 2c719e3 8d52a7d cfa5142 2c719e3 cfa5142 87a101a 41938cd 87a101a 5f8864d 0f36b51 2c719e3 2878798 0f36b51 2878798 2c719e3 0f36b51 002d880 2c719e3 002d880 2c719e3 16bf670 41938cd ebdd2a9 41938cd 63be95c 41938cd 63be95c 2c719e3 16bf670 2c719e3 63be95c 002d880 2c719e3 16bf670 8d52a7d 2c719e3 0f36b51 cfa5142 0f36b51 16bf670 ebdd2a9 002d880 16bf670 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 |
from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
from sam2.build_sam import build_sam2, build_sam2_video_predictor
from sam2.sam2_image_predictor import SAM2ImagePredictor
from typing import Dict, List, Optional
import torch
import os
from datetime import datetime
import numpy as np
from modules.model_downloader import (
AVAILABLE_MODELS, DEFAULT_MODEL_TYPE, OUTPUT_DIR,
is_sam_exist,
download_sam_model_url
)
from modules.paths import SAM2_CONFIGS_DIR, MODELS_DIR
from modules.constants import BOX_PROMPT_MODE, AUTOMATIC_MODE
from modules.mask_utils import (
save_psd_with_masks,
create_mask_combined_images,
create_mask_gallery
)
from modules.logger_util import get_logger
MODEL_CONFIGS = {
"sam2_hiera_tiny": os.path.join(SAM2_CONFIGS_DIR, "sam2_hiera_t.yaml"),
"sam2_hiera_small": os.path.join(SAM2_CONFIGS_DIR, "sam2_hiera_s.yaml"),
"sam2_hiera_base_plus": os.path.join(SAM2_CONFIGS_DIR, "sam2_hiera_b+.yaml"),
"sam2_hiera_large": os.path.join(SAM2_CONFIGS_DIR, "sam2_hiera_l.yaml"),
}
logger = get_logger()
class SamInference:
def __init__(self,
model_dir: str = MODELS_DIR,
output_dir: str = OUTPUT_DIR
):
self.model = None
self.available_models = list(AVAILABLE_MODELS.keys())
self.model_type = DEFAULT_MODEL_TYPE
self.model_dir = model_dir
self.output_dir = output_dir
self.model_path = os.path.join(self.model_dir, AVAILABLE_MODELS[DEFAULT_MODEL_TYPE][0])
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.mask_generator = None
self.image_predictor = None
self.video_predictor = None
self.video_inference_state = None
def load_model(self,
load_video_predictor: bool = False):
config = MODEL_CONFIGS[self.model_type]
filename, url = AVAILABLE_MODELS[self.model_type]
model_path = os.path.join(self.model_dir, filename)
if not is_sam_exist(self.model_type):
logger.info(f"No SAM2 model found, downloading {self.model_type} model...")
download_sam_model_url(self.model_type)
logger.info(f"Applying configs to model..")
if load_video_predictor:
try:
self.model = None
self.video_predictor = build_sam2_video_predictor(
config_file=config,
ckpt_path=model_path,
device=self.device
)
except Exception as e:
logger.exception("Error while loading SAM2 model for video predictor")
raise f"Error while loading SAM2 model for video predictor!: {e}"
try:
self.model = build_sam2(
config_file=config,
ckpt_path=model_path,
device=self.device
)
except Exception as e:
logger.exception("Error while loading SAM2 model")
raise f"Error while loading SAM2 model!: {e}"
def init_video_inference_state(self,
vid_input: str):
if self.video_predictor is None:
self.load_model(load_video_predictor=True)
if self.video_inference_state is not None:
self.video_predictor.reset_state(self.video_inference_state)
self.video_predictor.init_state(video_path=vid_input)
def generate_mask(self,
image: np.ndarray,
model_type: str,
**params):
if self.model is None or self.model_type != model_type:
self.model_type = model_type
self.load_model()
self.mask_generator = SAM2AutomaticMaskGenerator(
model=self.model,
**params
)
try:
generated_masks = self.mask_generator.generate(image)
except Exception as e:
logger.exception("Error while auto generating masks")
raise f"Error while auto generating masks: str({e})"
return generated_masks
def predict_image(self,
image: np.ndarray,
model_type: str,
box: Optional[np.ndarray] = None,
point_coords: Optional[np.ndarray] = None,
point_labels: Optional[np.ndarray] = None,
**params):
if self.model is None or self.model_type != model_type:
self.model_type = model_type
self.load_model()
self.image_predictor = SAM2ImagePredictor(sam_model=self.model)
self.image_predictor.set_image(image)
try:
masks, scores, logits = self.image_predictor.predict(
box=box,
point_coords=point_coords,
point_labels=point_labels,
multimask_output=params["multimask_output"],
)
except Exception as e:
logger.exception("Error while predicting image with prompt")
raise f"Error while predicting image with prompt: {str(e)}"
return masks, scores, logits
def predict_frame(self,
frame_idx: int,
obj_id: int,
inference_state: Dict,
points: np.ndarray,
labels: np.ndarray):
if self.video_inference_state is None:
logger.exception("Error while predicting frame from video, load video predictor first")
raise f"Error while predicting frame from video"
try:
out_masks, out_obj_ids, out_mask_logits = self.video_predictor.add_new_points_or_box(
inference_state=inference_state,
frame_idx=frame_idx,
obj_id=obj_id,
points=points,
labels=labels,
)
except Exception as e:
logger.exception("Error while predicting frame with prompt")
raise f"Error while predicting frame with prompt: {str(e)}"
return out_masks, out_obj_ids, out_mask_logits
def predict_video(self,
video_input):
pass
def add_filter_to_preview(self,
image: np.ndarray,
):
pass
def divide_layer(self,
image_input: np.ndarray,
image_prompt_input_data: Dict,
input_mode: str,
model_type: str,
*params):
timestamp = datetime.now().strftime("%m%d%H%M%S")
output_file_name = f"result-{timestamp}.psd"
output_path = os.path.join(self.output_dir, "psd", output_file_name)
# Pre-processed gradio components
hparams = {
'points_per_side': int(params[0]),
'points_per_batch': int(params[1]),
'pred_iou_thresh': float(params[2]),
'stability_score_thresh': float(params[3]),
'stability_score_offset': float(params[4]),
'crop_n_layers': int(params[5]),
'box_nms_thresh': float(params[6]),
'crop_n_points_downscale_factor': int(params[7]),
'min_mask_region_area': int(params[8]),
'use_m2m': bool(params[9]),
'multimask_output': bool(params[10])
}
if input_mode == AUTOMATIC_MODE:
image = image_input
generated_masks = self.generate_mask(
image=image,
model_type=model_type,
**hparams
)
elif input_mode == BOX_PROMPT_MODE:
image = image_prompt_input_data["image"]
image = np.array(image.convert("RGB"))
prompt = image_prompt_input_data["points"]
if len(prompt) == 0:
return [image], []
point_labels, point_coords, box = [], [], []
for x1, y1, left_click_indicator, x2, y2, point_indicator in prompt:
if point_indicator == 4.0:
point_labels.append(left_click_indicator)
point_coords.append([x1, y1])
else:
box.append([x1, y1, x2, y2])
predicted_masks, scores, logits = self.predict_image(
image=image,
model_type=model_type,
box=np.array(box) if box else None,
point_coords=np.array(point_coords) if point_coords else None,
point_labels=np.array(point_labels) if point_labels else None,
multimask_output=hparams["multimask_output"]
)
generated_masks = self.format_to_auto_result(predicted_masks)
save_psd_with_masks(image, generated_masks, output_path)
mask_combined_image = create_mask_combined_images(image, generated_masks)
gallery = create_mask_gallery(image, generated_masks)
gallery = [mask_combined_image] + gallery
return gallery, output_path
@staticmethod
def format_to_auto_result(
masks: np.ndarray
):
place_holder = 0
if len(masks.shape) <= 3:
masks = np.expand_dims(masks, axis=0)
result = [{"segmentation": mask[0], "area": place_holder} for mask in masks]
return result
|