翻译： def track(self, frame): ''' 开启追踪 ''' message = None if self.isWorking: status, self.coord = self.tracker.update(frame) if status: message = {"coord": [((int(self.coord[0]), int(self.coord[1])), (int(self.coord[0] + self.coord[2]), int(self.coord[1] + self.coord[3])))]} if self.draw_coord: p1 = (int(self.coord[0]), int(self.coord[1])) p2 = (int(self.coord[0] + self.coord[2]), int(self.coord[1] + self.coord[3])) cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1) message['msg'] = "is tracking" return MessageItem(frame, message)

修改以下错误代码class Face(object): def init(self, L, m, H, q, tanbeta, b): #工作面长度L 采厚m 平均采深H self.L = L self.m = m self.H = H #地标移动参数 self.q = q self.tanbeta = tanbeta self.b = b #坐标系统 self.S = 0.05H self.r = H/tanbeta self.l = L-20.05*H #最大值 class Coord_Sys(Face): def init(self): self.face = Face并创建一个实例

修改后的代码如下： class Face(object): ...3. 在 Coord_Sys 类的构造函数中，将 self.face = Face 改为 self.face = Face(L, m, H, q, tanbeta, b)，是创建 Face 类的实例，并传入正确的参数。

修改以下错误代码class Face(object): def init(self, L, m, H, q, tanbeta, b): #工作面长度L 采厚m 平均采深H self.L = L self.m = m self.H = H #地标移动参数 self.q = q self.tanbeta = tanbeta self.b = b #坐标系统 self.S = 0.05H self.r = H/tanbeta self.l = L-20.05*H #最大值 class Coord_Sys(Face): def init(self): self.face = Face

def __init__(self, L, m, H, q, tanbeta, b): # 工作面长度L，采厚m，平均采深H self.L = L self.m = m self.H = H # 地标移动参数 self.q = q self.tanbeta = tanbeta self.b = b # 坐标系统 self.S = ...

def CamealToPlane(self,pm=None): # init camera if(pm is None): return False pm[np.isnan(pm[:,0])]=[0,0,0] pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(np.asarray(pm)) downpcd = pcd.voxel_down_sample(0.05) plane,inl = downpcd.segment_plane(0.01,5,10000) if(self.showResult == 2): print("Plane:",plane) vecz=plane[:3] # plane_heigh = -plane[3]/plane[2] vecz/= np.sqrt(vecz.dot(vecz)) vecy = np.cross(vecz,[0,0,1]) vecx = np.cross(vecz,vecy) vecx/= np.sqrt(vecx.dot(vecx)) vecy = np.cross(vecz,vecx) PlaneRotation=np.asarray([vecx,vecy,vecz]) TPlaneRotation = PlaneRotation.T print(TPlaneRotation) self.Rt = TPlaneRotation.reshape((3, 3)) if(self.showResult): innerpoints = downpcd.select_by_index(inl) # innerpoints.paint_uniform_color([1, 0, 0]) # outpoints = downpcd.select_by_index(inl,invert=True) # outpoints.paint_uniform_color([0, 1, 0]) # o3d.visualization.draw_geometries([innerpoints,outpoints],'Vis') self.pcd_show.points=innerpoints.points self.T_show = [] self.Datachaged=True self.SavePara(self.config_path) # o3d.visualization.draw_geometries([coord,coordtemp,pcd,visplane],'Vis')

如果设置了 showResult 标志位，则将内点的点云数据赋值给类成员变量 self.pcd_show，并将类成员变量 self.T_show 置为空列表，将类成员变量 self.Datachaged 设为 True。最后调用 SavePara() 函数保存参数。根据...

def constant(self):if len(self.old) < self.old_size:return False arr = []for i in self.old:if i not in arr:arr.append(i)if len(arr) < 10:return True def current(self, Items=None):arr = []if Items == None:Items = self.items[:]for item in Items:b = 0for i, n in enumerate(item[::-1]): b += n * 2 ** iarr.append(b)self.old.append(arr)def paint_world(): global tv, square # 网格世界，正方形 tv = tk.Canvas(world, width=world.winfo_width(), height=world.winfo_height())tv.pack(side="top")coord = 38, 38, 760, 760tv.create_rectangle(coord, width=2)R, XY = 8, [50 + i * 20 for i in range(36)]square = [[0] * 36 for _ in range(36)]for i, x in enumerate(XY):for j, y in enumerate(XY):square[i][j] = tv.create_rectangle(x - R, y - R, x + R, y + R, tags=('imgButton1'))tv.itemconfig(square[i][j], fill='white', outline='white')tv.tag_bind('imgButton1', '<Button-1>', click_cell) 解释代码

这段代码是一个基于 tkinter 库实现的网格世界的可视化界面。其中，constant() 方法用于判断当前的状态是否与历史状态重复，如果重复则返回 True，否则返回 False；current() 方法用于将当前的状态转化为数字并存储...

import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable class Bottleneck(nn.Module): def init(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer): super(Bottleneck, self).init() self.out_planes = out_planes self.dense_depth = dense_depth self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(in_planes) self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False) self.bn2 = nn.BatchNorm2d(in_planes) self.conv3 = nn.Conv2d(in_planes, out_planes+dense_depth, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(out_planes+dense_depth) self.shortcut = nn.Sequential() if first_layer: self.shortcut = nn.Sequential( nn.Conv2d(last_planes, out_planes+dense_depth, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_planes+dense_depth) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) x = self.shortcut(x) d = self.out_planes out = torch.cat([x[:,:d,:,:]+out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1) out = F.relu(out) return out class DPN(nn.Module): def init(self, cfg): super(DPN, self).init() in_planes, out_planes = cfg['in_planes'], cfg['out_planes'] num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth'] self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64) self.last_planes = 64 self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1) self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2) self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2) self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2) self.linear = nn.Linear(out_planes[3]+(num_blocks[3]+1)dense_depth[3], 10) def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride): strides = [stride] + 1 layers = [] for i,stride in (strides): layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i==0)) self.last_planes = out_planes + (i+2) * dense_depth return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = F.avg_pool2d(out, 4) out = out.view(out.size(0), -1) out = self.linear(out) return out def DPN92(): cfg = { 'in_planes': (96,192,384,768), 'out_planes': (256,512,1024,2048), 'num_blocks': (3,4,20,3), 'dense_depth': (16,32,24,128) } return DPN(cfg)基于这个程序修改成对摄像头采集的图像检测与分类输出坐标、大小和种类，并训练和测试

cv2.imshow('frame', frame) # 退出 if cv2.waitKey(1) == ord('q'): break # 释放摄像头 cap.release() # 关闭窗口 cv2.destroyAllWindows() 需要注意的是，这只是一个简单的示例代码，需要根据你的...

分析代码：def get_segment_length(coord): """ Args: coord: Current line segment coordinates Returns:Current line length(unit:meter) """ prev_coord = None distance = 0.0 for i in range(len(coord)): if i == 0: prev_coord = coord[i] else: geom_dict = calc_distance_pyproj(prev_coord[1], prev_coord[0], coord[i][1], coord[i][0]) distance += geom_dict['s12'] prev_coord = coord[i] return distance

- 初始化 prev_coord 和 distance 变量为 None 和 0.0。 - 遍历 coord 列表中的每个点，用 calc_distance_pyproj 函数计算前一个点和当前点之间的距离并加到 distance 中，然后将当前点赋值给 prev...

xyxy=yolov8_results.boxes.xyxy.cpu().numpy(), confidence=yolov8_results.boxes.conf.cpu().numpy(), class_id=yolov8_results.boxes.cls.cpu().numpy().astype(int), mask=extract_yolov8_masks(yolov8_results), 怎么用 boxes = detections.xyxy conf = detections.confidence class_ids = detections.class_id results = [] if polygon.shape[0] > 0: for label, score, seg in zip(class_ids, conf, polygon): # 将多边形的点坐标转换为列表形式 points = [[float(coord[0]), float(coord[1])] for coord in seg] results.append({ "confidence": str(score), "label": labels.get(label, "unknown"), "points": points, "type": "polygon",})

points = [[float(coord[0]), float(coord[1])] for coord in seg] results.append({ "confidence": str(score), "label": labels.get(label, "unknown"), "points": points, "type": "polygon", }) 在...

def draw_view(): global x01 global y01 global z01 t = ts_dict['t_of_now'] filepath = ts_dict['filepath'] with open(filepath + str(t) + ".txt", 'r', encoding='utf-8') as file1: all = file1.read() coord_x = all[re.search('X', all).end() + 2:re.search('Y', all).start() - 24] coord_x = coord_x.split(',') coord_y = all[re.search('Y', all).end() + 2:re.search('Z', all).start() - 24] coord_y = coord_y.split(',') coord_z = all[re.search('Z', all).end() + 2:] coord_z = coord_z.split(',') num01 = len(coord_x) x01 = coord_x y01 = coord_y z01 = coord_z for i in range(num01): x = x01[i] y = y01[i] z = z01[i] ax.scatter3D(x, y, z) canvas.draw()

这是一个Python函数，它的作用是读取一个文件中的坐标数据，并在一个三维坐标系中绘制出来。函数中使用了全局变量x01、y01和z01来存储坐标数据，使用了matplotlib库中的scatter3D函数来绘制散点图。...

根据上述代码，改写下列程序try: df1 = pd.read_csv('image_3_2.txt', sep='\t', header=None, error_bad_lines=False, na_values='?') df2 = pd.read_csv('image_6.txt', sep='\t', header=None, error_bad_lines=False, na_values='?') tree = KDTree(df2.iloc[:, :2].values) k = 4 distances, indices = tree.query(df1.iloc[:, :2].values, k=k) values = df2.iloc[indices[:, 1:], 2].values mean_values = values.mean(axis=1) df1.iloc[:, 2] = mean_values df1.to_csv('4.txt', sep='\t', header=None, index=None) except Exception as e: print("Error: ", e)

points3D = pd.read_csv('points3D.txt', sep='\t', header=None, error_bad_lines=False, na_values='?') skeleton = cv2.imread('skeleton.jpg', cv2.IMREAD_GRAYSCALE) tree = KDTree(points3D.iloc[:, :2]....

pcd += coord_frame TypeError: unsupported operand type(s) for +=: 'open3d.cpu.pybind.geometry.PointCloud' and 'open3d.cpu.pybind.geometry.TriangleMesh'

这个错误的原因是你在将一个 TriangleMesh 对象加到 PointCloud 对象上。这是不支持的操作。PointCloud 对象是一个点集合，而 TriangleMesh 对象是一个由三角形构成的网格。它们之间不能直接相加。...

下面一段代码有什么错误：def evaluate_one_image(): workbook = xlsxwriter.Workbook('formatting.xlsx') worksheet = workbook.add_worksheet('My Worksheet') with tf.Graph().as_default(): BATCH_SIZE = 1 N_CLASSES = 4 image = tf.cast(image_array, tf.float32) image = tf.image.per_image_standardization(image) image = tf.reshape(image, [1, 208, 208, 3]) logit = model.cnn_inference(image, BATCH_SIZE, N_CLASSES) logit = tf.nn.softmax(logit) x = tf.placeholder(tf.float32, shape=[208, 208, 3]) logs_train_dir = 'log/' saver = tf.train.Saver() with tf.Session() as sess: print("从指定路径中加载模型...") ckpt = tf.train.get_checkpoint_state(logs_train_dir) if ckpt and ckpt.model_checkpoint_path: global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] saver.restore(sess, ckpt.model_checkpoint_path) print('模型加载成功, 训练的步数为： %s' % global_step) else: print('模型加载失败，checkpoint文件没找到！') prediction = sess.run(logit, feed_dict={x: image_array}) max_index = np.argmax(prediction) workbook.close() def evaluate_images(test_img): coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) for index,img in enumerate(test_img): image = Image.open(img) image = image.resize([208, 208]) image_array = np.array(image) tf.compat.v1.threading.Thread(target=evaluate_one_image, args=(image_array, index)).start() # 请求停止所有线程 coord.request_stop() # 等待所有线程完成 coord.join(threads) if name == 'main': # 调用方法，开始测试 test_dir = 'data/test/' import glob import xlwt test_img = glob.glob(test_dir + '*.jpg') evaluate_images(test_img)

这段代码存在以下几个错误： 1. 缺少必要的库导入语句，例如import tensorflow as tf，import numpy as np，import xlsxwriter，from PIL import Image等。 2. 在evaluate_one_image()函数中，缺少了...

def forward(self, samples: NestedTensor): """ The forward expects a NestedTensor, which consists of: - samples.tensor: batched images, of shape [batch_size x 3 x H x W] - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels It returns a dict with the following elements: - "pred_logits": the classification logits (including no-object) for all queries. Shape= [batch_size x num_queries x (num_classes + 1)] - "pred_boxes": The normalized boxes coordinates for all queries, represented as (center_x, center_y, height, width). These values are normalized in [0, 1], relative to the size of each individual image (disregarding possible padding). See PostProcess for information on how to retrieve the unnormalized bounding box. - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of dictionnaries containing the two above keys for each decoder layer. """ if isinstance(samples, (list, torch.Tensor)): samples = nested_tensor_from_tensor_list(samples) features, pos = self.backbone(samples) src, mask = features[-1].decompose() assert mask is not None hs = self.transformer(self.input_proj(src), mask, self.query_embed.weight, pos[-1])[0] outputs_class = self.class_embed(hs) outputs_coord = self.bbox_embed(hs).sigmoid() out = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord[-1]} if self.aux_loss: out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord) return out

这是一个模型的前向传播函数，它接受一个NestedTensor作为输入，并返回一个包含预测结果的字典。输入的NestedTensor包含两个部分： - samples.tensor：批次图像，形状为[batch_size x 3 x H x W] ...

以下代码有什么错误，怎么修改： import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from PIL import Image import matplotlib.pyplot as plt import input_data import model import numpy as np import xlsxwriter num_threads = 4 def evaluate_one_image(): workbook = xlsxwriter.Workbook('formatting.xlsx') worksheet = workbook.add_worksheet('My Worksheet') with tf.Graph().as_default(): BATCH_SIZE = 1 N_CLASSES = 4 image = tf.cast(image_array, tf.float32) image = tf.image.per_image_standardization(image) image = tf.reshape(image, [1, 208, 208, 3]) logit = model.cnn_inference(image, BATCH_SIZE, N_CLASSES) logit = tf.nn.softmax(logit) x = tf.placeholder(tf.float32, shape=[208, 208, 3]) logs_train_dir = 'log/' saver = tf.train.Saver() with tf.Session() as sess: print("从指定路径中加载模型...") ckpt = tf.train.get_checkpoint_state(logs_train_dir) if ckpt and ckpt.model_checkpoint_path: global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] saver.restore(sess, ckpt.model_checkpoint_path) print('模型加载成功, 训练的步数为： %s' % global_step) else: print('模型加载失败，checkpoint文件没找到！') prediction = sess.run(logit, feed_dict={x: image_array}) max_index = np.argmax(prediction) workbook.close() def evaluate_images(test_img): coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) for index,img in enumerate(test_img): image = Image.open(img) image = image.resize([208, 208]) image_array = np.array(image) tf.compat.v1.threading.Thread(target=evaluate_one_image, args=(image_array, index)).start() coord.request_stop() coord.join(threads) if name == 'main': test_dir = 'data/test/' import glob import xlwt test_img = glob.glob(test_dir + '*.jpg') evaluate_images(test_img)

threads = tf.train.start_queue_runners(coord=coord) for index, img in enumerate(test_img): image = Image.open(img) image = image.resize([208, 208]) image_array = np.array(image) tf.compat.v1....

b+geom_point(size=2,color='blue')+ coord_fixed()+theme_classic()+ geom_smooth(method = "lm",formula = y ~ x,size=2,color="black",fill="green")+ theme (aspect.ratio=1,axis.text.x = element_text (size = 15),axis.title.x = element_text (size = 15),axis.title.y = element_text(size = 15),axis.text.y = element_text (size = 15))+ stat_cor(data=data, method = "pearson")

coord_fixed()表示坐标轴的比例尺不变；theme_classic()表示使用经典的主题风格；geom_smooth(method = "lm",formula = y ~ x,size=2,color="black",fill="green")表示添加一条线性回归线，用黑色表示线的颜色，绿色...

with tf.Session() as sess: sess.run(init) saver = tf.train.Saver() train_writer = tf.summary.FileWriter(log_dir+'/train',sess.graph) test_writer = tf.summary.FileWriter(log_dir+'/test') step = 1 coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess, coord) print('doing1') # Keep training until reach max iterations try: while step * batch_size <= training_iters: # print('doing2') batch_xs, batch_ys = sess.run([hrrp_batch, label_batch]) # print(batch_xs.shape) # print(batch_ys.shape) # 获取批数据 sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout}) if step % display_step == 0: summary= sess.run(merged, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) test_writer.add_summary(summary, step) # 计算精度 acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) # 计算损失值 loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) 所选择的特征是

在这段代码中，没有涉及到特征的选择。这段代码是用于在 TensorFlow 中训练一个神经网络模型的。在这段代码中，使用了一个数据队列来读取数据，其中 hrrp_batch 和 label_batch 是从数据队列中读取的批量数据。...

翻译：class Vehicle(): def init(self): self.depot_id=0 self.x_coord=0 self.y_coord=0 self.type=0 self.capacity=0 self.free_speed=1 self.fixed_cost=1.0 self.variable_cost=1.0 self.numbers=0 self.start_time=0 self.end_time=1440

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翻译：class Vehicle(): def init(self): self.depot_id=0 self.x_coord=0 self.y_coord=0 self.type=0 self.capacity=0 self.free_speed=1 self.fixed_cost=1.0 self.variable_cost=1.0 self.numbers=0 self.start_time=0 self.end_time=1440