File size: 12,561 Bytes
55a3c9a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 |
from S2I.samer import SegMent, generate_sam_args
from S2I.logger import logger
from tqdm import tqdm
import gradio as gr
import numpy as np
import os
import shutil
import cv2
import requests
class SAMController:
def __init__(self):
self.current_model_type = None
self.refine_mask = None
@staticmethod
def clean():
return None, None, None, None, None, [[]]
@staticmethod
def save_mask(refined_mask=None, save=False):
if refined_mask is not None and save:
if os.path.exists(os.path.join(os.getcwd(), 'output_render')):
shutil.rmtree(os.path.join(os.getcwd(), 'output_render'))
save_path = os.path.join(os.getcwd(), 'output_render')
os.makedirs(save_path, exist_ok=True)
cv2.imwrite(os.path.join(save_path, f'refined_mask_result.png'), (refined_mask * 255).astype('uint8'))
elif refined_mask is None and save:
return os.path.join(os.path.join(os.getcwd(), 'output_render'), f'refined_mask_result.png')
@staticmethod
def download_models(model_type):
dir_path = os.path.join(os.getcwd(), 'root_model')
sam_models_path = os.path.join(dir_path, 'sam_models')
# Models URLs
models_urls = {
'sam_models': {
'vit_b': 'https://huggingface.co/ybelkada/segment-anything/resolve/main/checkpoints/sam_vit_b_01ec64.pth?download=true',
'vit_l': 'https://huggingface.co/segments-arnaud/sam_vit_l/resolve/main/sam_vit_l_0b3195.pth?download=true',
'vit_h': 'https://huggingface.co/segments-arnaud/sam_vit_h/resolve/main/sam_vit_h_4b8939.pth?download=true'
}
}
# Download specified model type
if model_type in models_urls['sam_models']:
model_url = models_urls['sam_models'][model_type]
os.makedirs(sam_models_path, exist_ok=True)
model_path = os.path.join(sam_models_path, model_type + '.pth')
if not os.path.exists(model_path):
logger.info(f"Downloading {model_type} model...")
response = requests.get(model_url, stream=True)
response.raise_for_status() # Raise an exception for non-2xx status codes
total_size = int(response.headers.get('content-length', 0)) # Get file size from headers
with tqdm(total=total_size, unit="B", unit_scale=True, desc=f"Downloading {model_type} model") as pbar:
with open(model_path, 'wb') as f:
for chunk in response.iter_content(chunk_size=1024):
f.write(chunk)
pbar.update(len(chunk))
logger.info(f"{model_type} model downloaded.")
else:
logger.info(f"{model_type} model already exists.")
return logger.info(f"{model_type} model download complete.")
else:
return logger.info(f"Invalid model type: {model_type}")
@staticmethod
def get_models_path(model_type=None, segment=False):
sam_models_path = os.path.join(os.getcwd(), 'root_model', 'sam_models')
if segment:
sam_args = generate_sam_args(sam_checkpoint=sam_models_path, model_type=model_type)
return sam_args, sam_models_path
@staticmethod
def get_click_prompt(click_stack, point):
click_stack[0].append(point["coord"])
click_stack[1].append(point["mode"]
)
prompt = {
"points_coord": click_stack[0],
"points_mode": click_stack[1],
"multi_mask": "True",
}
return prompt
@staticmethod
def read_temp_file(temp_file_wrapper):
name = temp_file_wrapper.name
with open(temp_file_wrapper.name, 'rb') as f:
# Read the content of the file
file_content = f.read()
return file_content, name
def get_meta_from_image(self, input_img):
file_content, _ = self.read_temp_file(input_img)
np_arr = np.frombuffer(file_content, np.uint8)
img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
first_frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return first_frame, first_frame
def is_sam_model(self, model_type):
sam_args, sam_models_dir = self.get_models_path(model_type=model_type, segment=True)
model_path = os.path.join(sam_models_dir, model_type + '.pth')
if not os.path.exists(model_path):
self.download_models(model_type=model_type)
return 'Model is downloaded', sam_args
else:
return 'Model is already downloaded', sam_args
@staticmethod
def init_segment(
points_per_side,
origin_frame,
sam_args,
predict_iou_thresh=0.8,
stability_score_thresh=0.9,
crop_n_layers=1,
crop_n_points_downscale_factor=2,
min_mask_region_area=200):
if origin_frame is None:
return None, origin_frame, [[], []]
sam_args["generator_args"]["points_per_side"] = points_per_side
sam_args["generator_args"]["pred_iou_thresh"] = predict_iou_thresh
sam_args["generator_args"]["stability_score_thresh"] = stability_score_thresh
sam_args["generator_args"]["crop_n_layers"] = crop_n_layers
sam_args["generator_args"]["crop_n_points_downscale_factor"] = crop_n_points_downscale_factor
sam_args["generator_args"]["min_mask_region_area"] = min_mask_region_area
segment = SegMent(sam_args)
logger.info(f"Model Init: {sam_args}")
return segment, origin_frame, [[], []]
@staticmethod
def seg_acc_click(segment, prompt, origin_frame):
# seg acc to click
refined_mask, masked_frame = segment.seg_acc_click(
origin_frame=origin_frame,
coords=np.array(prompt["points_coord"]),
modes=np.array(prompt["points_mode"]),
multimask=prompt["multi_mask"],
)
return refined_mask, masked_frame
def undo_click_stack_and_refine_seg(self, segment, origin_frame, click_stack):
if segment is None:
return segment, origin_frame, [[], []]
logger.info("Undo !")
if len(click_stack[0]) > 0:
click_stack[0] = click_stack[0][: -1]
click_stack[1] = click_stack[1][: -1]
if len(click_stack[0]) > 0:
prompt = {
"points_coord": click_stack[0],
"points_mode": click_stack[1],
"multi_mask": "True",
}
_, masked_frame = self.seg_acc_click(segment, prompt, origin_frame)
return segment, masked_frame, click_stack
else:
return segment, origin_frame, [[], []]
def reload_segment(self,
check_sam,
segment,
model_type,
point_per_sides,
origin_frame,
predict_iou_thresh,
stability_score_thresh,
crop_n_layers,
crop_n_points_downscale_factor,
min_mask_region_area):
status, sam_args = check_sam(model_type)
if segment is None or status == 'Model is downloaded':
segment, _, _ = self.init_segment(point_per_sides,
origin_frame,
sam_args,
predict_iou_thresh,
stability_score_thresh,
crop_n_layers,
crop_n_points_downscale_factor,
min_mask_region_area)
self.current_model_type = model_type
return segment, self.current_model_type, status
def sam_click(self,
evt: gr.SelectData,
segment,
origin_frame,
model_type,
point_mode,
click_stack,
point_per_sides,
predict_iou_thresh,
stability_score_thresh,
crop_n_layers,
crop_n_points_downscale_factor,
min_mask_region_area):
logger.info("Click")
if point_mode == "Positive":
point = {"coord": [evt.index[0], evt.index[1]], "mode": 1}
else:
point = {"coord": [evt.index[0], evt.index[1]], "mode": 0}
click_prompt = self.get_click_prompt(click_stack, point)
segment, self.current_model_type, status = self.reload_segment(
self.is_sam_model,
segment,
model_type,
point_per_sides,
origin_frame,
predict_iou_thresh,
stability_score_thresh,
crop_n_layers,
crop_n_points_downscale_factor,
min_mask_region_area)
if segment is not None and model_type != self.current_model_type:
segment = None
segment, _, status = self.reload_segment(
self.is_sam_model,
segment,
model_type,
point_per_sides,
origin_frame,
predict_iou_thresh,
stability_score_thresh,
crop_n_layers,
crop_n_points_downscale_factor,
min_mask_region_area)
refined_mask, masked_frame = self.seg_acc_click(segment, click_prompt, origin_frame)
self.save_mask(refined_mask, save=True)
self.refine_mask = refined_mask
return segment, masked_frame, click_stack, status
@staticmethod
def normalize_image(image):
# Normalize the image to the range [0, 1]
min_val = image.min()
max_val = image.max()
image = (image - min_val) / (max_val - min_val)
return image
@staticmethod
def compute_probability(masks):
p_max = None
for mask in masks:
p = mask['prob']
if p_max is None:
p_max = p
else:
p_max = np.maximum(p_max, p)
return p_max
@staticmethod
def download_opencv_model(model_url):
opencv_model_path = os.path.join(os.getcwd(), 'edges_detection')
os.makedirs(opencv_model_path, exist_ok=True)
model_path = os.path.join(opencv_model_path, 'edges_detection' + '.yml.gz')
response = requests.get(model_url, stream=True)
response.raise_for_status() # Raise an exception for non-2xx status codes
total_size = int(response.headers.get('content-length', 0)) # Get file size from headers
with tqdm(total=total_size, unit="B", unit_scale=True, desc=f"Downloading opencv model") as pbar:
with open(model_path, 'wb') as f:
for chunk in response.iter_content(chunk_size=1024):
f.write(chunk)
pbar.update(len(chunk))
return model_path
def automatic_sam2sketch(self,
segment,
image,
origin_frame,
model_type
):
_, sam_args = self.is_sam_model(model_type)
if segment is None or model_type != sam_args['model_type']:
segment, _, _ = self.init_segment(
points_per_side=16,
origin_frame=origin_frame,
sam_args=sam_args,
predict_iou_thresh=0.8,
stability_score_thresh=0.9,
crop_n_layers=1,
crop_n_points_downscale_factor=2,
min_mask_region_area=200)
model_path = self.download_opencv_model(model_url='https://github.com/nipunmanral/Object-Detection-using-OpenCV/raw/master/model.yml.gz')
masks = segment.automatic_generate_mask(image)
p_max = self.compute_probability(masks)
edges = self.normalize_image(p_max)
edge_detection = cv2.ximgproc.createStructuredEdgeDetection(model_path)
orimap = edge_detection.computeOrientation(edges)
edges = edge_detection.edgesNms(edges, orimap)
edges = (edges * 255).astype('uint8')
edges = 255 - edges
edges = np.stack((edges,) * 3, axis=-1)
return edges
|