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新增pose模型，注意参数设置

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HJW
2025-03-27 10:21:20 +08:00
parent 69e88c2b6b
commit 92778fa76a
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
# @Time : 2025/3/18 15:29
# @Author : hjw
# @File : yolov8_pt_pose.py
'''
import os.path
import random
import cv2
import numpy as np
import torch
import time
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.utils import ops
class yolov8_pose:
def __init__(self, weights, cuda, conf_thres=0.45, iou_thres=0.45) -> None:
"""
weights = r'./runs/pose/train25/weights/last.pt'
cuda = 'cpu'
save_path = "./img_test"
"""
self.imgsz = 640
self.device = cuda
self.model = AutoBackend(weights, device=torch.device(cuda))
self.model.eval()
self.names = self.model.names
self.half = False
self.conf = conf_thres
self.iou = iou_thres
self.color = {"font": (255, 255, 255)}
self.color.update(
{self.names[i]: (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
for i in range(len(self.names))})
# self.skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8],
# [7, 9], [8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
# pose_palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], [230, 230, 0], [255, 153, 255],
# [153, 204, 255], [255, 102, 255], [255, 51, 255], [102, 178, 255], [51, 153, 255],
# [255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102],
# [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], [255, 255, 255]], dtype=np.uint8)
# self.kpt_color = pose_palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]
# self.limb_color = pose_palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]
self.skeleton = [[1, 2], [2, 3], [3, 4]]
pose_palette = np.array([[255, 0, 0], [255, 153, 51], [255, 3, 102], [0, 230, 0]], dtype=np.uint8)
self.kpt_color = pose_palette[[0, 1, 2, 3]]
self.limb_color = pose_palette[[0, 1, 2, 3]]
# print(len(self.skeleton ))
# print(len(pose_palette))
# print(len(self.kpt_color))
# print(len(self.limb_color))
def model_inference(self, img_src):
img = self.precess_image(img_src, self.imgsz, self.half, self.device)
preds = self.model(img) # shape [1, 56, 6300]
det = ops.non_max_suppression(preds, self.conf, self.iou, classes=None, agnostic=False, max_det=300,
nc=len(self.names))
point_xy = []
name_list = []
score_list = []
for i, pred in enumerate(det):
lw = max(round(sum(img_src.shape) / 2 * 0.003), 2) # line width
tf = max(lw - 1, 1) # font thickness
sf = lw / 3 # font scale
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], img_src.shape)
pred_bbox = pred[:, :6].cpu().detach().numpy()
pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, img_src.shape)
pred_kpts = pred_kpts.cpu().detach().numpy()
point_xy = []
for kpts, bbox in zip(pred_kpts, pred_bbox):
box = bbox[:4]
score = bbox[4]
name = self.names[bbox[5]]
shape = (640, 640)
radius = 5
kpt_line = True
nkpt, ndim = kpts.shape
is_pose = nkpt == 4 and ndim in {2, 3}
kpt_line &= is_pose # `kpt_line=True` for now only supports human pose plotting
xy = []
for i, k in enumerate(kpts):
color_k = [int(x) for x in self.kpt_color[i]]
x_coord, y_coord = k[0], k[1]
if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
if len(k) == 3:
conf = k[2]
if conf < 0.5:
continue
xy.append([int(x_coord), int(y_coord)])
cv2.circle(img_src, (int(x_coord), int(y_coord)), radius, color_k, -1, lineType=cv2.LINE_AA)
point_xy.append(xy)
name_list.append(name)
score_list.append(score)
return True, point_xy, name_list, score_list
def draw_box(self, img_src, box, conf, cls_name, lw, sf, tf):
color = self.color[cls_name]
label = f'{cls_name} {conf}'
p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
# 绘制矩形框
cv2.rectangle(img_src, p1, p2, color, thickness=lw, lineType=cv2.LINE_AA)
# text width, height
w, h = cv2.getTextSize(label, 0, fontScale=sf, thickness=tf)[0]
# label fits outside box
outside = box[1] - h - 3 >= 0
p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
# 绘制矩形框填充
cv2.rectangle(img_src, p1, p2, color, -1, cv2.LINE_AA)
# 绘制标签
cv2.putText(img_src, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
0, sf, self.color["font"], thickness=2, lineType=cv2.LINE_AA)
def draw_kpts(self, img_src, kpts, box, score, name, lw, sf, tf, shape=(640, 640), radius=5, kpt_line=True):
flag = False
nkpt, ndim = kpts.shape
is_pose = nkpt == 4 and ndim in {2, 3}
kpt_line &= is_pose # `kpt_line=True` for now only supports human pose plotting
for i, k in enumerate(kpts):
color_k = [int(x) for x in self.kpt_color[i]]
x_coord, y_coord = k[0], k[1]
if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
if len(k) == 3:
conf = k[2]
if conf < 0.5:
continue
cv2.circle(img_src, (int(x_coord), int(y_coord)), radius, color_k, -1, lineType=cv2.LINE_AA)
if kpt_line:
ndim = kpts.shape[-1]
for i, sk in enumerate(self.skeleton):
pos1 = (int(kpts[(sk[0] - 1), 0]), int(kpts[(sk[0] - 1), 1]))
pos2 = (int(kpts[(sk[1] - 1), 0]), int(kpts[(sk[1] - 1), 1]))
if ndim == 3:
conf1 = kpts[(sk[0] - 1), 2]
conf2 = kpts[(sk[1] - 1), 2]
if conf1 < 0.5 or conf2 < 0.5:
continue
if pos1[0] % shape[1] == 0 or pos1[1] % shape[0] == 0 or pos1[0] < 0 or pos1[1] < 0:
continue
if pos2[0] % shape[1] == 0 or pos2[1] % shape[0] == 0 or pos2[0] < 0 or pos2[1] < 0:
continue
cv2.line(img_src, pos1, pos2, [int(x) for x in self.limb_color[i]], thickness=2, lineType=cv2.LINE_AA)
flag = True
if flag:
self.draw_box(img_src, box, score, name, lw, sf, tf)
@staticmethod
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), scaleup=True, stride=32):
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
# minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return im, ratio, (dw, dh)
def precess_image(self, img_src, img_size, half, device):
# Padded resize
img = self.letterbox(img_src, img_size)[0]
# Convert
img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img = img / 255 # 0 - 255 to 0.0 - 1.0
if len(img.shape) == 3:
img = img[None] # expand for batch dim
return img
# if __name__ == '__main__':
# weights = r'./runs/pose/train25/weights/last.pt'
# cuda = 'cpu'
# save_path = "./img_test"
# start = time.time()
# if not os.path.exists(save_path):
# os.mkdir(save_path)
#
# model = yolov8_pose(weights, cuda, 0.45, 0.45)
#
# img_path = r'./1106-08-pe-518.png'
# model.infer(img_path, save_path)
# end = time.time()
# print('推理时间：',end -start)