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wip: remove ultralytics, always use manual sam for image mask instead of rembg

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Manuel Schmid
2024-06-10 01:33:03 +02:00
parent 09e23f5509
commit 8a81993940
4 changed files with 146 additions and 155 deletions
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extras/inpaint_mask.py
+114 -32
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@@ -1,47 +1,129 @@
import numpy as np
import torch
from rembg import remove, new_session
from segment_anything import sam_model_registry, SamPredictor
from segment_anything.utils.amg import remove_small_regions
from extras.GroundingDINO.util.inference import default_groundingdino
def generate_mask_from_image(image: np.ndarray, mask_model: str, extras: dict, box_erode_or_dilate: int=0, debug_dino: bool=False) -> np.ndarray | None:
class SAMOptions:
def __init__(self,
# GroundingDINO
dino_prompt: str = '',
dino_box_threshold=0.3,
dino_text_threshold=0.25,
box_erode_or_dilate=0,
# SAM
max_num_boxes=2,
sam_checkpoint="./models/sam/sam_vit_l_0b3195.pth",
model_type="vit_l"
):
self.dino_prompt = dino_prompt
self.dino_box_threshold = dino_box_threshold
self.dino_text_threshold = dino_text_threshold
self.box_erode_or_dilate = box_erode_or_dilate
self.max_num_boxes = max_num_boxes
self.sam_checkpoint = sam_checkpoint
self.model_type = model_type
def optimize_masks(masks: torch.Tensor) -> torch.Tensor:
"""
removes small disconnected regions and holes
"""
fine_masks = []
for mask in masks.to('cpu').numpy(): # masks: [num_masks, 1, h, w]
fine_masks.append(remove_small_regions(mask[0], 400, mode="holes")[0])
masks = np.stack(fine_masks, axis=0)[:, np.newaxis]
return torch.from_numpy(masks)
def generate_mask_from_image(image: np.ndarray, mask_model: str = 'sam', extras=None,
sam_options: SAMOptions | None = SAMOptions) -> np.ndarray | None:
if image is None:
return
if extras is None:
extras = {}
if 'image' in image:
image = image['image']
if mask_model == 'sam':
detections, _, _, _ = default_groundingdino(
image=image,
caption=extras['sam_prompt_text'],
box_threshold=extras['box_threshold'],
text_threshold=extras['text_threshold']
if mask_model != 'sam' and sam_options is None:
return remove(
image,
session=new_session(mask_model, **extras),
only_mask=True,
**extras
)
detection_boxes = detections.xyxy
# use full image if no box has been found
detection_boxes = np.array([[0, 0, image.shape[1], image.shape[0]]]) if len(detection_boxes) == 0 else detection_boxes
extras['sam_prompt'] = []
for idx, box in enumerate(detection_boxes):
box_list = box.tolist()
if box_erode_or_dilate != 0:
box_list[0] -= box_erode_or_dilate
box_list[1] -= box_erode_or_dilate
box_list[2] += box_erode_or_dilate
box_list[3] += box_erode_or_dilate
extras['sam_prompt'] += [{"type": "rectangle", "data": box_list}]
assert sam_options is not None
if debug_dino:
from PIL import ImageDraw, Image
debug_dino_image = Image.new("RGB", (image.shape[1], image.shape[0]), color="black")
draw = ImageDraw.Draw(debug_dino_image)
for box in extras['sam_prompt']:
draw.rectangle(box['data'], fill="white")
return np.array(debug_dino_image)
return remove(
image,
session=new_session(mask_model, **extras),
only_mask=True,
**extras
detections, boxes, logits, phrases = default_groundingdino(
image=image,
caption=sam_options.dino_prompt,
box_threshold=sam_options.dino_box_threshold,
text_threshold=sam_options.dino_text_threshold
)
# detection_boxes = detections.xyxy
# # use full image if no box has been found
# detection_boxes = np.array([[0, 0, image.shape[1], image.shape[0]]]) if len(detection_boxes) == 0 else detection_boxes
#
#
# for idx, box in enumerate(detection_boxes):
# box_list = box.tolist()
# if box_erode_or_dilate != 0:
# box_list[0] -= box_erode_or_dilate
# box_list[1] -= box_erode_or_dilate
# box_list[2] += box_erode_or_dilate
# box_list[3] += box_erode_or_dilate
# extras['sam_prompt'] += [{"type": "rectangle", "data": box_list}]
#
# if debug_dino:
# from PIL import ImageDraw, Image
# debug_dino_image = Image.new("RGB", (image.shape[1], image.shape[0]), color="black")
# draw = ImageDraw.Draw(debug_dino_image)
# for box in extras['sam_prompt']:
# draw.rectangle(box['data'], fill="white")
# return np.array(debug_dino_image)
# TODO add support for box_erode_or_dilate again
H, W = image.shape[0], image.shape[1]
boxes = boxes * torch.Tensor([W, H, W, H])
boxes[:, :2] = boxes[:, :2] - boxes[:, 2:] / 2
boxes[:, 2:] = boxes[:, 2:] + boxes[:, :2]
# TODO add model patcher for model logic and device management
device = "cuda" if torch.cuda.is_available() else "cpu"
sam = sam_model_registry[sam_options.model_type](checkpoint=sam_options.sam_checkpoint)
sam.to(device=device)
sam_predictor = SamPredictor(sam)
final_mask_tensor = torch.zeros((image.shape[0], image.shape[1]))
if boxes.size(0) > 0:
sam_predictor.set_image(image)
transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes, image.shape[:2])
masks, _, _ = sam_predictor.predict_torch(
point_coords=None,
point_labels=None,
boxes=transformed_boxes.to(device),
multimask_output=False,
)
masks = optimize_masks(masks)
num_obj = min(len(logits), sam_options.max_num_boxes)
for obj_ind in range(num_obj):
mask_tensor = masks[obj_ind][0]
final_mask_tensor += mask_tensor
final_mask_tensor = (final_mask_tensor > 0).to('cpu').numpy()
mask_image = np.dstack((final_mask_tensor, final_mask_tensor, final_mask_tensor)) * 255
mask_image = np.array(mask_image, dtype=np.uint8)
return mask_image