214 lines
9.2 KiB
Python
214 lines
9.2 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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'''
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# @Time : 2025/3/18 15:29
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# @Author : hjw
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# @File : yolov8_pt_pose.py
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'''
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import os.path
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import random
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import cv2
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import numpy as np
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import torch
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import time
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from ultralytics.nn.autobackend import AutoBackend
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from ultralytics.utils import ops
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class yolov8_pose:
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def __init__(self, weights, cuda, conf_thres=0.45, iou_thres=0.45) -> None:
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"""
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weights = r'./runs/pose/train25/weights/last.pt'
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cuda = 'cpu'
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save_path = "./img_test"
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"""
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self.imgsz = 640
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self.device = cuda
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self.model = AutoBackend(weights, device=torch.device(cuda))
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self.model.eval()
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self.names = self.model.names
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self.half = False
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self.conf = conf_thres
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self.iou = iou_thres
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self.color = {"font": (255, 255, 255)}
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self.color.update(
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{self.names[i]: (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
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for i in range(len(self.names))})
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# self.skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8],
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# [7, 9], [8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
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# pose_palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], [230, 230, 0], [255, 153, 255],
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# [153, 204, 255], [255, 102, 255], [255, 51, 255], [102, 178, 255], [51, 153, 255],
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# [255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102],
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# [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], [255, 255, 255]], dtype=np.uint8)
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# self.kpt_color = pose_palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]
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# self.limb_color = pose_palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]
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self.skeleton = [[1, 2], [2, 3], [3, 4]]
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pose_palette = np.array([[255, 0, 0], [255, 153, 51], [255, 3, 102], [0, 230, 0]], dtype=np.uint8)
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self.kpt_color = pose_palette[[0, 1, 2, 3]]
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self.limb_color = pose_palette[[0, 1, 2, 3]]
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# print(len(self.skeleton ))
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# print(len(pose_palette))
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# print(len(self.kpt_color))
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# print(len(self.limb_color))
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def model_inference(self, img_src):
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img = self.precess_image(img_src, self.imgsz, self.half, self.device)
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preds = self.model(img) # shape [1, 56, 6300]
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det = ops.non_max_suppression(preds, self.conf, self.iou, classes=None, agnostic=False, max_det=300,
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nc=len(self.names))
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point_xy = []
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name_list = []
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score_list = []
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for i, pred in enumerate(det):
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lw = max(round(sum(img_src.shape) / 2 * 0.003), 2) # line width
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tf = max(lw - 1, 1) # font thickness
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sf = lw / 3 # font scale
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], img_src.shape)
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pred_bbox = pred[:, :6].cpu().detach().numpy()
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pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
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pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, img_src.shape)
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pred_kpts = pred_kpts.cpu().detach().numpy()
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point_xy = []
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for kpts, bbox in zip(pred_kpts, pred_bbox):
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box = bbox[:4]
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score = bbox[4]
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name = self.names[bbox[5]]
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shape = (640, 640)
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radius = 5
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kpt_line = True
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nkpt, ndim = kpts.shape
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is_pose = nkpt == 4 and ndim in {2, 3}
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kpt_line &= is_pose # `kpt_line=True` for now only supports human pose plotting
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xy = []
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for i, k in enumerate(kpts):
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color_k = [int(x) for x in self.kpt_color[i]]
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x_coord, y_coord = k[0], k[1]
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if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
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if len(k) == 3:
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conf = k[2]
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if conf < 0.5:
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continue
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xy.append([int(x_coord), int(y_coord)])
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cv2.circle(img_src, (int(x_coord), int(y_coord)), radius, color_k, -1, lineType=cv2.LINE_AA)
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point_xy.append(xy)
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name_list.append(name)
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score_list.append(score)
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return True, point_xy, name_list, score_list
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def draw_box(self, img_src, box, conf, cls_name, lw, sf, tf):
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color = self.color[cls_name]
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label = f'{cls_name} {conf}'
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p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
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# 绘制矩形框
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cv2.rectangle(img_src, p1, p2, color, thickness=lw, lineType=cv2.LINE_AA)
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# text width, height
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w, h = cv2.getTextSize(label, 0, fontScale=sf, thickness=tf)[0]
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# label fits outside box
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outside = box[1] - h - 3 >= 0
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p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
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# 绘制矩形框填充
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cv2.rectangle(img_src, p1, p2, color, -1, cv2.LINE_AA)
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# 绘制标签
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cv2.putText(img_src, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
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0, sf, self.color["font"], thickness=2, lineType=cv2.LINE_AA)
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def draw_kpts(self, img_src, kpts, box, score, name, lw, sf, tf, shape=(640, 640), radius=5, kpt_line=True):
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flag = False
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nkpt, ndim = kpts.shape
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is_pose = nkpt == 4 and ndim in {2, 3}
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kpt_line &= is_pose # `kpt_line=True` for now only supports human pose plotting
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for i, k in enumerate(kpts):
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color_k = [int(x) for x in self.kpt_color[i]]
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x_coord, y_coord = k[0], k[1]
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if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
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if len(k) == 3:
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conf = k[2]
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if conf < 0.5:
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continue
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cv2.circle(img_src, (int(x_coord), int(y_coord)), radius, color_k, -1, lineType=cv2.LINE_AA)
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if kpt_line:
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ndim = kpts.shape[-1]
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for i, sk in enumerate(self.skeleton):
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pos1 = (int(kpts[(sk[0] - 1), 0]), int(kpts[(sk[0] - 1), 1]))
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pos2 = (int(kpts[(sk[1] - 1), 0]), int(kpts[(sk[1] - 1), 1]))
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if ndim == 3:
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conf1 = kpts[(sk[0] - 1), 2]
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conf2 = kpts[(sk[1] - 1), 2]
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if conf1 < 0.5 or conf2 < 0.5:
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continue
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if pos1[0] % shape[1] == 0 or pos1[1] % shape[0] == 0 or pos1[0] < 0 or pos1[1] < 0:
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continue
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if pos2[0] % shape[1] == 0 or pos2[1] % shape[0] == 0 or pos2[0] < 0 or pos2[1] < 0:
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continue
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cv2.line(img_src, pos1, pos2, [int(x) for x in self.limb_color[i]], thickness=2, lineType=cv2.LINE_AA)
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flag = True
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if flag:
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self.draw_box(img_src, box, score, name, lw, sf, tf)
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@staticmethod
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def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), scaleup=True, stride=32):
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# Resize and pad image while meeting stride-multiple constraints
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shape = im.shape[:2] # current shape [height, width]
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if isinstance(new_shape, int):
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new_shape = (new_shape, new_shape)
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# Scale ratio (new / old)
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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if not scaleup: # only scale down, do not scale up (for better val mAP)
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r = min(r, 1.0)
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# Compute padding
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ratio = r, r # width, height ratios
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
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# minimum rectangle
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dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
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dw /= 2 # divide padding into 2 sides
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dh /= 2
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if shape[::-1] != new_unpad: # resize
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im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
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im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
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return im, ratio, (dw, dh)
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def precess_image(self, img_src, img_size, half, device):
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# Padded resize
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img = self.letterbox(img_src, img_size)[0]
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# Convert
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img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
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img = np.ascontiguousarray(img)
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float() # uint8 to fp16/32
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img = img / 255 # 0 - 255 to 0.0 - 1.0
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if len(img.shape) == 3:
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img = img[None] # expand for batch dim
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return img
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# if __name__ == '__main__':
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# weights = r'./runs/pose/train25/weights/last.pt'
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# cuda = 'cpu'
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# save_path = "./img_test"
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# start = time.time()
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# if not os.path.exists(save_path):
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# os.mkdir(save_path)
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#
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# model = yolov8_pose(weights, cuda, 0.45, 0.45)
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#
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# img_path = r'./1106-08-pe-518.png'
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# model.infer(img_path, save_path)
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# end = time.time()
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# print('推理时间：',end -start) |