mirror of
https://github.com/Refound-445/nonebot-plugin-nailongremove.git
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266 lines
8.1 KiB
Python
266 lines
8.1 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Megvii Inc. All rights reserved.
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# ruff: noqa: ANN001
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import cv2
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import numpy as np
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_COLORS = (
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np.array(
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[
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0.000, 0.447, 0.741,
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0.850, 0.325, 0.098,
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0.929, 0.694, 0.125,
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0.494, 0.184, 0.556,
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0.466, 0.674, 0.188,
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0.301, 0.745, 0.933,
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0.635, 0.078, 0.184,
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0.300, 0.300, 0.300,
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0.600, 0.600, 0.600,
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1.000, 0.000, 0.000,
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1.000, 0.500, 0.000,
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0.749, 0.749, 0.000,
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0.000, 1.000, 0.000,
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0.000, 0.000, 1.000,
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0.667, 0.000, 1.000,
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0.333, 0.333, 0.000,
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0.333, 0.667, 0.000,
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0.333, 1.000, 0.000,
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0.667, 0.333, 0.000,
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0.667, 0.667, 0.000,
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0.667, 1.000, 0.000,
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1.000, 0.333, 0.000,
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1.000, 0.667, 0.000,
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1.000, 1.000, 0.000,
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0.000, 0.333, 0.500,
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0.000, 0.667, 0.500,
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0.000, 1.000, 0.500,
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0.333, 0.000, 0.500,
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0.333, 0.333, 0.500,
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0.333, 0.667, 0.500,
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0.333, 1.000, 0.500,
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0.667, 0.000, 0.500,
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0.667, 0.333, 0.500,
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0.667, 0.667, 0.500,
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0.667, 1.000, 0.500,
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1.000, 0.000, 0.500,
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1.000, 0.333, 0.500,
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1.000, 0.667, 0.500,
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1.000, 1.000, 0.500,
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0.000, 0.333, 1.000,
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0.000, 0.667, 1.000,
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0.000, 1.000, 1.000,
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0.333, 0.000, 1.000,
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0.333, 0.333, 1.000,
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0.333, 0.667, 1.000,
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0.333, 1.000, 1.000,
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0.667, 0.000, 1.000,
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0.667, 0.333, 1.000,
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0.667, 0.667, 1.000,
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0.667, 1.000, 1.000,
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1.000, 0.000, 1.000,
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1.000, 0.333, 1.000,
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1.000, 0.667, 1.000,
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0.333, 0.000, 0.000,
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0.500, 0.000, 0.000,
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0.667, 0.000, 0.000,
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0.833, 0.000, 0.000,
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1.000, 0.000, 0.000,
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0.000, 0.167, 0.000,
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0.000, 0.333, 0.000,
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0.000, 0.500, 0.000,
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0.000, 0.667, 0.000,
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0.000, 0.833, 0.000,
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0.000, 1.000, 0.000,
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0.000, 0.000, 0.167,
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0.000, 0.000, 0.333,
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0.000, 0.000, 0.500,
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0.000, 0.000, 0.667,
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0.000, 0.000, 0.833,
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0.000, 0.000, 1.000,
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0.000, 0.000, 0.000,
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0.143, 0.143, 0.143,
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0.286, 0.286, 0.286,
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0.429, 0.429, 0.429,
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0.571, 0.571, 0.571,
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0.714, 0.714, 0.714,
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0.857, 0.857, 0.857,
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0.000, 0.447, 0.741,
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0.314, 0.717, 0.741,
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0.50, 0.5, 0,
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],
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)
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.astype(np.float32)
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.reshape(-1, 3)
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) # fmt: skip
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def preprocess(img, input_size, swap=(2, 0, 1)):
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if len(img.shape) == 3:
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padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114
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else:
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padded_img = np.ones(input_size, dtype=np.uint8) * 114
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r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])
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resized_img = cv2.resize(
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img,
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(int(img.shape[1] * r), int(img.shape[0] * r)),
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interpolation=cv2.INTER_LINEAR,
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).astype(np.uint8)
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padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img
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padded_img = padded_img.transpose(swap)
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padded_img = np.ascontiguousarray(padded_img, dtype=np.float32)
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return padded_img, r
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def vis(img, boxes, scores, cls_ids, conf=0.5, class_names=None):
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for i in range(len(boxes)):
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box = boxes[i]
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cls_id = int(cls_ids[i])
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score = scores[i]
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if score < conf:
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continue
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x0 = int(box[0])
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y0 = int(box[1])
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x1 = int(box[2])
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y1 = int(box[3])
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color = (_COLORS[cls_id] * 255).astype(np.uint8).tolist()
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text = "{}:{:.1f}%".format(
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class_names[cls_id] if class_names else "",
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score * 100,
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)
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txt_color = (0, 0, 0) if np.mean(_COLORS[cls_id]) > 0.5 else (255, 255, 255)
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font = cv2.FONT_HERSHEY_SIMPLEX
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txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]
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cv2.rectangle(img, (x0, y0), (x1, y1), color, 2)
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txt_bk_color = (_COLORS[cls_id] * 255 * 0.7).astype(np.uint8).tolist()
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cv2.rectangle(
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img,
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(x0, y0 + 1),
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(x0 + txt_size[0] + 1, y0 + int(1.5 * txt_size[1])),
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txt_bk_color,
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-1,
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)
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cv2.putText(
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img,
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text,
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(x0, y0 + txt_size[1]),
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font,
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0.4,
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txt_color,
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thickness=1,
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)
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return img
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def nms(boxes, scores, nms_thr):
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"""Single class NMS implemented in Numpy."""
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x1 = boxes[:, 0]
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y1 = boxes[:, 1]
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x2 = boxes[:, 2]
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y2 = boxes[:, 3]
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = scores.argsort()[::-1]
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keep = []
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while order.size > 0:
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i = order[0]
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keep.append(i)
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xx1 = np.maximum(x1[i], x1[order[1:]])
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yy1 = np.maximum(y1[i], y1[order[1:]])
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xx2 = np.minimum(x2[i], x2[order[1:]])
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yy2 = np.minimum(y2[i], y2[order[1:]])
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w = np.maximum(0.0, xx2 - xx1 + 1)
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h = np.maximum(0.0, yy2 - yy1 + 1)
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inter = w * h
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(ovr <= nms_thr)[0]
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order = order[inds + 1]
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return keep
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def multiclass_nms(boxes, scores, nms_thr, score_thr, class_agnostic=True):
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"""Multiclass NMS implemented in Numpy"""
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if class_agnostic:
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nms_method = multiclass_nms_class_agnostic
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else:
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nms_method = multiclass_nms_class_aware
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return nms_method(boxes, scores, nms_thr, score_thr)
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def multiclass_nms_class_aware(boxes, scores, nms_thr, score_thr):
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"""Multiclass NMS implemented in Numpy. Class-aware version."""
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final_dets = []
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num_classes = scores.shape[1]
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for cls_ind in range(num_classes):
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cls_scores = scores[:, cls_ind]
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valid_score_mask = cls_scores > score_thr
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if valid_score_mask.sum() == 0:
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continue
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valid_scores = cls_scores[valid_score_mask]
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valid_boxes = boxes[valid_score_mask]
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keep = nms(valid_boxes, valid_scores, nms_thr)
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if len(keep) > 0:
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cls_inds = np.ones((len(keep), 1)) * cls_ind
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dets = np.concatenate(
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[valid_boxes[keep], valid_scores[keep, None], cls_inds],
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1,
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)
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final_dets.append(dets)
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if len(final_dets) == 0:
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return None
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return np.concatenate(final_dets, 0)
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def multiclass_nms_class_agnostic(boxes, scores, nms_thr, score_thr):
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"""Multiclass NMS implemented in Numpy. Class-agnostic version."""
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cls_inds = scores.argmax(1)
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cls_scores = scores[np.arange(len(cls_inds)), cls_inds]
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valid_score_mask = cls_scores > score_thr
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if valid_score_mask.sum() == 0:
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return None
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valid_scores = cls_scores[valid_score_mask]
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valid_boxes = boxes[valid_score_mask]
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valid_cls_inds = cls_inds[valid_score_mask]
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keep = nms(valid_boxes, valid_scores, nms_thr)
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if keep:
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return np.concatenate(
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[valid_boxes[keep], valid_scores[keep, None], valid_cls_inds[keep, None]],
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1,
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)
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return None
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def demo_postprocess(outputs, img_size, p6=False):
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grids = []
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expanded_strides = []
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strides = [8, 16, 32] if not p6 else [8, 16, 32, 64]
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hsizes = [img_size[0] // stride for stride in strides]
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wsizes = [img_size[1] // stride for stride in strides]
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for hsize, wsize, stride in zip(hsizes, wsizes, strides):
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xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
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grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
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grids.append(grid)
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shape = grid.shape[:2]
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expanded_strides.append(np.full((*shape, 1), stride))
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grids = np.concatenate(grids, 1)
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expanded_strides = np.concatenate(expanded_strides, 1)
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outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
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outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
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return outputs
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