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Vision/bag_collection.py
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Vision/bag_collection.py
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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'''
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# @Time : 2024/9/25 11:47
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# @Author : hjw
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# @File : detect_person.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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from Vision.tool.CameraHIK import camera_HIK
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from Vision.tool.CameraRVC import camera_rvc
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from Vision.tool.CameraPe import camera_pe
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from Vision.tool.utils import get_disk_space
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from ultralytics.nn.autobackend import AutoBackend
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from ultralytics.utils import ops
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import time
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import os
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class DetectionBag:
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"""
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:param
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api: None
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:return: person [bool], img [ndarray]
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"""
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def __init__(self) -> None:
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model_path = ''.join([os.getcwd(), '/Vision/model/pt/bag_collection.pt'])
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self.camera_rvc = camera_pe()
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self.imgsz = 640
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self.cuda = 'cpu'
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self.conf = 0.40
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self.iou = 0.45
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self.model = AutoBackend(model_path, device=torch.device(self.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.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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def detect_bag(self, save_img_point=0):
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"""
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检测料袋，保存料带图像及点云
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:param save_img_point: 0不保存 ; 1保存原图 ;2保存原点云和原图; 3保存缩小点云和原图
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:return ret: bool 相机是否正常工作
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:return img: ndarry 返回img
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"""
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ret, img_src, pm = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及
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# ret = True
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# img_src= cv2.imread('D:\\原始数据\\抚州第二次出差数据\\图漾\\tu3D\\2024_11_08_10_43_01.png')
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# pm=np.loadtxt('D:\\原始数据\\抚州第二次出差数据\\图漾\\tu3D\\2024_11_08_10_43_01.xyz').reshape((480, 640, 3))
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Bag = False
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# self.camera_rvc.caminit_isok = True
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if self.camera_rvc.caminit_isok == True:
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if ret:
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# img_src = cv2.imread(img_path)
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img_save = img_src.copy()
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img = self.precess_image(img_src, self.imgsz, self.half, self.cuda)
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preds = self.model(img)
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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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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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results = pred.cpu().detach().numpy()
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# cv2.imshow('img2', img_src)
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# cv2.waitKey(1)
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for result in results:
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if self.names[result[5]]=="bag":
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Bag = True
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conf = round(result[4], 2)
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#print(conf)
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self.draw_box(img_src, result[:4], conf, self.names[result[5]], lw, sf, tf)
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if Bag==True:
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if get_disk_space(path=os.getcwd()) < 15: # 内存小于15G,停止保存数据
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save_img_point = 0
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print('系统内存不足，无法保存数据')
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else:
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save_path = ''.join([os.getcwd(), '/Vision/model/data/',
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time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime(time.time()))])
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save_img_name = ''.join([save_path, '.png'])
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save_point_name = ''.join([save_path, '.xyz'])
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if save_img_point == 1 or save_img_point==2 or save_img_point==3:
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cv2.imwrite(save_img_name, img_save)
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if save_img_point == 3:
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row_list = list(range(1, img.shape[0], 2))
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column_list = list(range(1, img.shape[1], 2))
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pm_save = pm.copy()
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pm_save1 = np.delete(pm_save, row_list, axis=0)
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point_new = np.delete(pm_save1, column_list, axis=1)
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point_new = point_new.reshape(-1, 3)
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np.savetxt(save_point_name, point_new)
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if save_img_point==2:
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point_new = pm.reshape(-1, 3)
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np.savetxt(save_point_name, point_new)
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return Bag, img_src
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return Bag, None
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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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conf = str(conf)
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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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@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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