Dr.Lt.Data
77d1917484
https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/606#issuecomment-2157594212
234 lines
7.6 KiB
Python
234 lines
7.6 KiB
Python
from pathlib import Path
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from PIL import Image
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import impact.core as core
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import cv2
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import numpy as np
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from torchvision.transforms.functional import to_pil_image
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import torch
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try:
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from ultralytics import YOLO
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except Exception as e:
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print(e)
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print(f"\n!!!!!\n\n[ComfyUI-Impact-Subpack] If this error occurs, please check the following link:\n\thttps://github.com/ltdrdata/ComfyUI-Impact-Pack/blob/Main/troubleshooting/TROUBLESHOOTING.md\n\n!!!!!\n")
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raise e
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def load_yolo(model_path: str):
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try:
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return YOLO(model_path)
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except ModuleNotFoundError:
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# https://github.com/ultralytics/ultralytics/issues/3856
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YOLO("yolov8n.pt")
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return YOLO(model_path)
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def inference_bbox(
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model,
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image: Image.Image,
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confidence: float = 0.3,
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device: str = "",
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):
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pred = model(image, conf=confidence, device=device)
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bboxes = pred[0].boxes.xyxy.cpu().numpy()
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cv2_image = np.array(image)
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if len(cv2_image.shape) == 3:
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cv2_image = cv2_image[:, :, ::-1].copy() # Convert RGB to BGR for cv2 processing
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else:
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# Handle the grayscale image here
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# For example, you might want to convert it to a 3-channel grayscale image for consistency:
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cv2_image = cv2.cvtColor(cv2_image, cv2.COLOR_GRAY2BGR)
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cv2_gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY)
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segms = []
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for x0, y0, x1, y1 in bboxes:
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cv2_mask = np.zeros(cv2_gray.shape, np.uint8)
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cv2.rectangle(cv2_mask, (int(x0), int(y0)), (int(x1), int(y1)), 255, -1)
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cv2_mask_bool = cv2_mask.astype(bool)
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segms.append(cv2_mask_bool)
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n, m = bboxes.shape
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if n == 0:
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return [[], [], [], []]
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results = [[], [], [], []]
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for i in range(len(bboxes)):
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results[0].append(pred[0].names[int(pred[0].boxes[i].cls.item())])
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results[1].append(bboxes[i])
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results[2].append(segms[i])
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results[3].append(pred[0].boxes[i].conf.cpu().numpy())
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return results
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def inference_segm(
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model,
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image: Image.Image,
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confidence: float = 0.3,
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device: str = "",
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):
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pred = model(image, conf=confidence, device=device)
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bboxes = pred[0].boxes.xyxy.cpu().numpy()
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n, m = bboxes.shape
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if n == 0:
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return [[], [], [], []]
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# NOTE: masks.data will be None when n == 0
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segms = pred[0].masks.data.cpu().numpy()
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h_segms = segms.shape[1]
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w_segms = segms.shape[2]
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h_orig = image.size[1]
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w_orig = image.size[0]
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ratio_segms = h_segms / w_segms
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ratio_orig = h_orig / w_orig
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if ratio_segms == ratio_orig:
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h_gap = 0
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w_gap = 0
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elif ratio_segms > ratio_orig:
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h_gap = int((ratio_segms - ratio_orig) * h_segms)
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w_gap = 0
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else:
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h_gap = 0
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ratio_segms = w_segms / h_segms
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ratio_orig = w_orig / h_orig
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w_gap = int((ratio_segms - ratio_orig) * w_segms)
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results = [[], [], [], []]
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for i in range(len(bboxes)):
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results[0].append(pred[0].names[int(pred[0].boxes[i].cls.item())])
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results[1].append(bboxes[i])
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mask = torch.from_numpy(segms[i])
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mask = mask[h_gap:mask.shape[0] - h_gap, w_gap:mask.shape[1] - w_gap]
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scaled_mask = torch.nn.functional.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(image.size[1], image.size[0]),
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mode='bilinear', align_corners=False)
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scaled_mask = scaled_mask.squeeze().squeeze()
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results[2].append(scaled_mask.numpy())
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results[3].append(pred[0].boxes[i].conf.cpu().numpy())
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return results
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class UltraBBoxDetector:
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bbox_model = None
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def __init__(self, bbox_model):
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self.bbox_model = bbox_model
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def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
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drop_size = max(drop_size, 1)
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detected_results = inference_bbox(self.bbox_model, core.tensor2pil(image), threshold)
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segmasks = core.create_segmasks(detected_results)
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if dilation > 0:
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segmasks = core.dilate_masks(segmasks, dilation)
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items = []
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h = image.shape[1]
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w = image.shape[2]
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for x, label in zip(segmasks, detected_results[0]):
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item_bbox = x[0]
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item_mask = x[1]
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y1, x1, y2, x2 = item_bbox
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if x2 - x1 > drop_size and y2 - y1 > drop_size: # minimum dimension must be (2,2) to avoid squeeze issue
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crop_region = core.make_crop_region(w, h, item_bbox, crop_factor)
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if detailer_hook is not None:
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crop_region = detailer_hook.post_crop_region(w, h, item_bbox, crop_region)
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cropped_image = core.crop_image(image, crop_region)
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cropped_mask = core.crop_ndarray2(item_mask, crop_region)
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confidence = x[2]
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# bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
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item = core.SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, label, None)
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items.append(item)
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shape = image.shape[1], image.shape[2]
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segs = shape, items
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if detailer_hook is not None and hasattr(detailer_hook, "post_detection"):
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segs = detailer_hook.post_detection(segs)
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return segs
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def detect_combined(self, image, threshold, dilation):
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detected_results = inference_bbox(self.bbox_model, core.tensor2pil(image), threshold)
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segmasks = core.create_segmasks(detected_results)
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if dilation > 0:
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segmasks = core.dilate_masks(segmasks, dilation)
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return core.combine_masks(segmasks)
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def setAux(self, x):
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pass
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class UltraSegmDetector:
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bbox_model = None
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def __init__(self, bbox_model):
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self.bbox_model = bbox_model
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def detect(self, image, threshold, dilation, crop_factor, drop_size=1, detailer_hook=None):
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drop_size = max(drop_size, 1)
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detected_results = inference_segm(self.bbox_model, core.tensor2pil(image), threshold)
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segmasks = core.create_segmasks(detected_results)
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if dilation > 0:
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segmasks = core.dilate_masks(segmasks, dilation)
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items = []
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h = image.shape[1]
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w = image.shape[2]
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for x, label in zip(segmasks, detected_results[0]):
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item_bbox = x[0]
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item_mask = x[1]
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y1, x1, y2, x2 = item_bbox
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if x2 - x1 > drop_size and y2 - y1 > drop_size: # minimum dimension must be (2,2) to avoid squeeze issue
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crop_region = core.make_crop_region(w, h, item_bbox, crop_factor)
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if detailer_hook is not None:
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crop_region = detailer_hook.post_crop_region(w, h, item_bbox, crop_region)
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cropped_image = core.crop_image(image, crop_region)
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cropped_mask = core.crop_ndarray2(item_mask, crop_region)
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confidence = x[2]
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# bbox_size = (item_bbox[2]-item_bbox[0],item_bbox[3]-item_bbox[1]) # (w,h)
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item = core.SEG(cropped_image, cropped_mask, confidence, crop_region, item_bbox, label, None)
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items.append(item)
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shape = image.shape[1], image.shape[2]
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segs = shape, items
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if detailer_hook is not None and hasattr(detailer_hook, "post_detection"):
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segs = detailer_hook.post_detection(segs)
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return segs
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def detect_combined(self, image, threshold, dilation):
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detected_results = inference_segm(self.bbox_model, core.tensor2pil(image), threshold)
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segmasks = core.create_segmasks(detected_results)
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if dilation > 0:
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segmasks = core.dilate_masks(segmasks, dilation)
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return core.combine_masks(segmasks)
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def setAux(self, x):
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pass |