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import folder_paths
from impact.core import *
import os
import mmcv
from mmdet.apis import (inference_detector, init_detector)
from mmdet.evaluation import get_classes
def load_mmdet(model_path):
model_config = os.path.splitext(model_path)[0] + ".py"
model = init_detector(model_config, model_path, device="cpu")
return model
def inference_segm_old(model, image, conf_threshold):
image = image.numpy()[0] * 255
mmdet_results = inference_detector(model, image)
bbox_results, segm_results = mmdet_results
label = "A"
classes = get_classes("coco")
labels = [
np.full(bbox.shape[0], i, dtype=np.int32)
for i, bbox in enumerate(bbox_results)
]
n, m = bbox_results[0].shape
if n == 0:
return [[], [], []]
labels = np.concatenate(labels)
bboxes = np.vstack(bbox_results)
segms = mmcv.concat_list(segm_results)
filter_idxs = np.where(bboxes[:, -1] > conf_threshold)[0]
results = [[], [], []]
for i in filter_idxs:
results[0].append(label + "-" + classes[labels[i]])
results[1].append(bboxes[i])
results[2].append(segms[i])
return results
def inference_segm(image, modelname, conf_thres, lab="A"):
image = image.numpy()[0] * 255
mmdet_results = inference_detector(modelname, image).pred_instances
bboxes = mmdet_results.bboxes.numpy()
segms = mmdet_results.masks.numpy()
scores = mmdet_results.scores.numpy()
classes = get_classes("coco")
n, m = bboxes.shape
if n == 0:
return [[], [], [], []]
labels = mmdet_results.labels
filter_inds = np.where(mmdet_results.scores > conf_thres)[0]
results = [[], [], [], []]
for i in filter_inds:
results[0].append(lab + "-" + classes[labels[i]])
results[1].append(bboxes[i])
results[2].append(segms[i])
results[3].append(scores[i])
return results
def inference_bbox(modelname, image, conf_threshold):
image = image.numpy()[0] * 255
label = "A"
output = inference_detector(modelname, image).pred_instances
cv2_image = np.array(image)
cv2_image = cv2_image[:, :, ::-1].copy()
cv2_gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY)
segms = []
for x0, y0, x1, y1 in output.bboxes:
cv2_mask = np.zeros(cv2_gray.shape, np.uint8)
cv2.rectangle(cv2_mask, (int(x0), int(y0)), (int(x1), int(y1)), 255, -1)
cv2_mask_bool = cv2_mask.astype(bool)
segms.append(cv2_mask_bool)
n, m = output.bboxes.shape
if n == 0:
return [[], [], [], []]
bboxes = output.bboxes.numpy()
scores = output.scores.numpy()
filter_idxs = np.where(scores > conf_threshold)[0]
results = [[], [], [], []]
for i in filter_idxs:
results[0].append(label)
results[1].append(bboxes[i])
results[2].append(segms[i])
results[3].append(scores[i])
return results
class BBoxDetector:
bbox_model = None
def __init__(self, bbox_model):
self.bbox_model = bbox_model
def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
drop_size = max(drop_size, 1)
mmdet_results = inference_bbox(self.bbox_model, image, threshold)
segmasks = create_segmasks(mmdet_results)
if dilation > 0:
segmasks = dilate_masks(segmasks, dilation)
items = []
h = image.shape[1]
w = image.shape[2]
for x in segmasks:
item_bbox = x[0]
item_mask = x[1]
y1, x1, y2, x2 = item_bbox
if x2 - x1 > drop_size and y2 - y1 > drop_size: # minimum dimension must be (2,2) to avoid squeeze issue
crop_region = make_crop_region(w, h, item_bbox, crop_factor)
cropped_image = crop_image(image, crop_region)
cropped_mask = crop_ndarray2(item_mask, crop_region)
confidence = x[2]
# bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, None, None)
items.append(item)
shape = image.shape[1], image.shape[2]
return shape, items
def detect_combined(self, image, threshold, dilation):
mmdet_results = inference_bbox(self.bbox_model, image, threshold)
segmasks = create_segmasks(mmdet_results)
if dilation > 0:
segmasks = dilate_masks(segmasks, dilation)
return combine_masks(segmasks)
def setAux(self, x):
pass
class SegmDetector(BBoxDetector):
segm_model = None
def __init__(self, segm_model):
self.segm_model = segm_model
def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
drop_size = max(drop_size, 1)
mmdet_results = inference_segm(image, self.segm_model, threshold)
segmasks = create_segmasks(mmdet_results)
if dilation > 0:
segmasks = dilate_masks(segmasks, dilation)
items = []
h = image.shape[1]
w = image.shape[2]
for x in segmasks:
item_bbox = x[0]
item_mask = x[1]
y1, x1, y2, x2 = item_bbox
if x2 - x1 > drop_size and y2 - y1 > drop_size: # minimum dimension must be (2,2) to avoid squeeze issue
crop_region = make_crop_region(w, h, item_bbox, crop_factor)
cropped_image = crop_image(image, crop_region)
cropped_mask = crop_ndarray2(item_mask, crop_region)
confidence = x[2]
item = SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, None, None)
items.append(item)
segs = image.shape, items
if detailer_hook is not None and hasattr(detailer_hook, "post_detection"):
segs = detailer_hook.post_detection(segs)
return segs
def detect_combined(self, image, threshold, dilation):
mmdet_results = inference_bbox(self.bbox_model, image, threshold)
segmasks = create_segmasks(mmdet_results)
if dilation > 0:
segmasks = dilate_masks(segmasks, dilation)
return combine_masks(segmasks)
def setAux(self, x):
pass
class MMDetDetectorProvider:
@classmethod
def INPUT_TYPES(s):
bboxs = ["bbox/"+x for x in folder_paths.get_filename_list("mmdets_bbox")]
segms = ["segm/"+x for x in folder_paths.get_filename_list("mmdets_segm")]
return {"required": {"model_name": (bboxs + segms, )}}
RETURN_TYPES = ("BBOX_DETECTOR", "SEGM_DETECTOR")
FUNCTION = "load_mmdet"
CATEGORY = "ImpactPack"
def load_mmdet(self, model_name):
mmdet_path = folder_paths.get_full_path("mmdets", model_name)
model = load_mmdet(mmdet_path)
if model_name.startswith("bbox"):
return BBoxDetector(model), NO_SEGM_DETECTOR()
else:
return NO_BBOX_DETECTOR(), model |