def calCrowdcarCost(crowd_route_list, model): cost_of_distance = 0 crowd_wait_time = 0 model.crowdcarcost = 0 for route in crowd_route_list: timetable = [] for i in range(len(route)): if i == 0: depot_id = route[i] next_node_id = route[i+1] cost_of_distance = model.distance_matrix[depot_id, next_node_id] travel_time = int(model.distance_matrix[depot_id, next_node_id]) departure = max(0, model.demand_dict[next_node_id].start_time - travel_time) if departure == 0: crowd_wait_time = travel_time - model.demand_dict[next_node_id].start_time timetable.append((departure, departure)) elif 1 <= i <= len(route)-2: last_node_id = route[i-1] current_node_id = route[i] current_node = model.demand_dict[current_node_id] travel_time = int(model.distance_matrix[last_node_id, current_node_id]) cost_of_distance += model.distance_matrix[last_node_id, current_node_id] arrival = max(timetable[-1][1]+travel_time, current_node.start_time) if arrival == timetable[-1][1]+travel_time: crowd_wait_time += timetable[-1][1]+travel_time - current_node.start_time else: break model.crowdcarcost += model.fixcost + model.percost * cost_of_distance return model.crowdcarcost, crowd_wait_time 在这一段代码中我想知道每个点的等待时间总和，等待时间等于到达这个的时间减去它的时间窗上限

def local_search_one(crowd_route_list, route_list, sol, model, node): b = 0 while b < 15: route_list_l1 = copy.deepcopy(route_list) owncar_route = random.choice(route_list_l1) route_list_l1.remove(owncar_route) owncar_nodeIndex1 = random.choice(range(1, len(owncar_route) - 1)) owncar_nodeIndex2 = random.choice(range(1, len(owncar_route) - 1)) if owncar_nodeIndex1 != owncar_nodeIndex2: owncar_node1 = owncar_route[owncar_nodeIndex1] owncar_route[owncar_nodeIndex1] = owncar_route[owncar_nodeIndex2] owncar_route[owncar_nodeIndex2] = owncar_node1 owncar_route = list(set(owncar_route)) a1 = checkPriority(owncar_route) # a2 = checkTimeWindow(owncar_route, model, node) a3 = checkCapacity(owncar_route, model) if a1+a3 == 0: route_list_l1.append(owncar_route) model.tempt_cost = calobj(crowd_route_list, route_list_l1, sol, model) else: continue else: continue b = b + 1 return model.tempt_cost, route_list_l1 这一段代码有什么问题吗

这段代码的问题可能是出现在checkPriority()、checkCapacity()和calobj()这三个函数中的实现。在没有看到这些函数的实现...另外，如果在函数内部对route_list进行了修改，需要注意是否会影响函数外部的变量。

def generateOwnCarRoute(service_time, model, sol): pickup_node = copy.deepcopy(model.demand_id_list[0: 16]) own_pickup_node = [] own_delivery_node = [] route = [] sol.route_list = [] depot = model.depot_dict['d1'] vehicle_number = depot.depot_capacity departure = 0 arrival = 0 for i in pickup_node: if i not in model.crowd_pickup_node: own_pickup_node.append(i) own_delivery_node.append(i+16) while vehicle_number > 0 and len(own_pickup_node) > 0: route.append(depot.depot_id) minIndex = np.argmin([model.distance_matrix[depot.depot_id, own_pickup_node[i]] for i in range(0, len(own_pickup_node))]) minnode = own_pickup_node[minIndex] route.append(minnode) arrival = model.time_matrix[depot.depot_id, minnode] departure = arrival + service_time route.append(own_delivery_node[minIndex]) arrival = departure + model.time_matrix[minnode, own_delivery_node[minIndex]] departure += arrival + service_time last_node = own_delivery_node[minIndex] own_pickup_node.remove(minnode) own_delivery_node.remove(own_delivery_node[minIndex]) for j in own_pickup_node: next_minIndex = np.argmin([model.distance_matrix[last_node, j]]) next_minnode = own_pickup_node[next_minIndex] arrival = departure + model.time_matrix[last_node, next_minnode] if arrival <= model.demand_dict[next_minnode].end_time and arrival <= depot.dend_time: route.append(next_minnode) departure = arrival + service_time route.append(own_delivery_node[next_minIndex]) arrival = departure + model.time_matrix[next_minnode, own_delivery_node[next_minIndex]] departure += arrival + service_time last_node = own_delivery_node[next_minIndex] own_pickup_node.remove(next_minnode) own_delivery_node.remove(own_delivery_node[next_minIndex]) else: continue route.append(depot.depot_id) sol.route_list.append(route) vehicle_number = vehicle_number - 1 route = [] print(sol.route_list) return sol.route_list 这段代码的问题是有可能vehicle_number为0了，但是owner_pickup_node的长度还不为0，这种情况怎么解决

while vehicle_number > 0 and len(own_pickup_node) > 0 and len(sol.route_list) < model.depot_dict['d1'].depot_capacity: 这样，当所有车辆都被分配任务后，循环就会停止，避免了程序陷入无限循环的情况。

#!/usr/bin/env python from future import absolute_import from future import division from future import print_function from future import unicode_literals import os, sys import numpy as np import json import time from datetime import timedelta from collections import defaultdict import argparse import multiprocessing import PIL.Image as Image from panopticapi.utils import get_traceback, rgb2id OFFSET = 256 * 256 * 256 VOID = 0 class PQStatCat(): def init(self): self.iou = 0.0 self.tp = 0 self.fp = 0 self.fn = 0 def iadd(self, pq_stat_cat): self.iou += pq_stat_cat.iou self.tp += pq_stat_cat.tp self.fp += pq_stat_cat.fp self.fn += pq_stat_cat.fn return self class PQStat(): def init(self): self.pq_per_cat = defaultdict(PQStatCat) def getitem(self, i): return self.pq_per_cat[i] def iadd(self, pq_stat): for label, pq_stat_cat in pq_stat.pq_per_cat.items(): self.pq_per_cat[label] += pq_stat_cat return self def pq_average(self, categories, isthing): pq, sq, rq, n = 0, 0, 0, 0 per_class_results = {} for label, label_info in categories.items(): if isthing is not None: cat_isthing = label_info['isthing'] == 1 if isthing != cat_isthing: continue iou = self.pq_per_cat[label].iou tp = self.pq_per_cat[label].tp fp = self.pq_per_cat[label].fp fn = self.pq_per_cat[label].fn if tp + fp + fn == 0: per_class_results[label] = {'pq': 0.0, 'sq': 0.0, 'rq': 0.0} continue n += 1 pq_class = iou / (tp + 0.5 * fp + 0.5 * fn) sq_class = iou / tp if tp != 0 else 0 rq_class = tp / (tp + 0.5 * fp + 0.5 * fn) per_class_results[label] = {'pq': pq_class, 'sq': sq_class, 'rq': rq_class} pq += pq_class sq += sq_class rq += rq_class return {'pq': pq / n, 'sq': sq / n, 'rq': rq / n, 'n': n}, per_class_results @get_traceback def pq_compute_single_core(proc_id, annotation_set, gt_folder, pred_folder, categories): pq_stat = PQStat() idx = 0 for gt_ann, pred_ann in annotation_set: if idx % 100 == 0: print('Core: {}, {} from {} images processed'.format(proc_id, idx, len(annotation_set))) idx += 1 pan_gt = np.array(Image.open(os.path.join(gt_folder, gt_ann['file_name'])), dtype=np.uint32) pan_gt = rgb2id(pan_gt) pan_pred = np.array(Image.open(os.path.join(pred_folder, pred_ann['file_name'])), dtype=np.uint32) pan_pred = rgb2id(pan_pred) gt_segms = {el['id']: el for el in gt_ann['segments_info']} pred_segms = {el['id']: el for el in pred_ann['segments_info']} # predicted segments area calculation + prediction sanity checks pred_labels_set = set(el['id'] for el in pred_ann['segments_info']) labels, labels_cnt = np.unique(pan_pred, return_counts=True) for label, label_cnt in zip(labels, labels_cnt): if label not in pred_segms: if label == VOID: continue raise KeyError('In the image with ID {} segment with ID {} is presented in PNG and not presented in JSON.'.format(gt_ann['image_id'], label)) pred_segms[label]['area'] = label_cnt pred_labels_set.remove(label) if pred_segms[label]['category_id'] not in categories: raise KeyError('In the image with ID {} segment with ID {} has unknown category_id {}.'.format(gt_ann['image_id'], label, pred_segms[label]['category_id'])) if len(pred_labels_set) != 0: raise KeyError('In the image with ID {} the following segment IDs {} are presented in JSON and not presented in PNG.'.format(gt_ann['image_id'], list(pred_labels_set))) # confusion matrix calculation pan_gt_pred = pan_gt.astype(np.uint64) * OFFSET + pan_pred.astype(np.uint64) gt_pred_map = {} labels, labels_cnt = np.unique(pan_gt_pred, return_counts=True) for label, intersection in zip(labels, labels_cnt): gt_id = label // OFFSET pred_id = label % OFFSET gt_pred_map[(gt_id, pred_id)] = intersection # count all matched pairs gt_matched = set() pred_matched = set() for label_tuple, intersection in gt_pred_map.items(): gt_label, pred_label = label_tuple if gt_label not in gt_segms: continue if pred_label not in pred_segms: continue if gt_segms[gt_label]['iscrowd'] == 1: continue if gt_segms[gt_label]['category_id'] != pred_segms[pred_label]['category_id']: continue union = pred_segms[pred_label]['area'] + gt_segms[gt_label]['area'] - intersection - gt_pred_map.get((VOID, pred_label), 0) iou = intersection / union if iou > 0.5: pq_stat[gt_segms[gt_label]['category_id']].tp += 1 pq_stat[gt_segms[gt_label]['category_id']].iou += iou gt_matched.add(gt_label) pred_matched.add(pred_label) # count false positives crowd_labels_dict = {} for gt_label, gt_info in gt_segms.items(): if gt_label in gt_matched: continue # crowd segments are ignored if gt_info['iscrowd'] == 1: crowd_labels_dict[gt_info['category_id']] = gt_label continue pq_stat[gt_info['category_id']].fn += 1 # count false positives for pred_label, pred_info in pred_segms.items(): if pred_label in pred_matched: continue # intersection of the segment with VOID intersection = gt_pred_map.get((VOID, pred_label), 0) # plus intersection with corresponding CROWD region if it exists if pred_info['category_id'] in crowd_labels_dict: intersection += gt_pred_map.get((crowd_labels_dict[pred_info['category_id']], pred_label), 0) # predicted segment is ignored if more than half of the segment correspond to VOID and CROWD regions if intersection / pred_info['area'] > 0.5: continue pq_stat[pred_info['category_id']].fp += 1 print('Core: {}, all {} images processed'.format(proc_id, len(annotation_set))) return pq_stat def pq_compute_multi_core(matched_annotations_list, gt_folder, pred_folder, categories): cpu_num = multiprocessing.cpu_count() annotations_split = np.array_split(matched_annotations_list, cpu_num) print("Number of cores: {}, images per core: {}".format(cpu_num, len(annotations_split[0]))) workers = multiprocessing.Pool(processes=cpu_num) processes = [] for proc_id, annotation_set in enumerate(annotations_split): p = workers.apply_async(pq_compute_single_core, (proc_id, annotation_set, gt_folder, pred_folder, categories)) processes.append(p) pq_stat = PQStat() for p in processes: pq_stat += p.get() return pq_stat def pq_compute(gt_json_file, pred_json_file, gt_folder=None, pred_folder=None): start_time = time.time() with open(gt_json_file, 'r') as f: gt_json = json.load(f) with open(pred_json_file, 'r') as f: pred_json = json.load(f) if gt_folder is None: gt_folder = gt_json_file.replace('.json', '') if pred_folder is None: pred_folder = pred_json_file.replace('.json', '') categories = {el['id']: el for el in gt_json['categories']} print("Evaluation panoptic segmentation metrics:") print("Ground truth:") print("\tSegmentation folder: {}".format(gt_folder)) print("\tJSON file: {}".format(gt_json_file)) print("Prediction:") print("\tSegmentation folder: {}".format(pred_folder)) print("\tJSON file: {}".format(pred_json_file)) if not os.path.isdir(gt_folder): raise Exception("Folder {} with ground truth segmentations doesn't exist".format(gt_folder)) if not os.path.isdir(pred_folder): raise Exception("Folder {} with predicted segmentations doesn't exist".format(pred_folder)) pred_annotations = {el['image_id']: el for el in pred_json['annotations']} matched_annotations_list = [] for gt_ann in gt_json['annotations']: image_id = gt_ann['image_id'] if image_id not in pred_annotations: raise Exception('no prediction for the image with id: {}'.format(image_id)) matched_annotations_list.append((gt_ann, pred_annotations[image_id])) pq_stat = pq_compute_multi_core(matched_annotations_list, gt_folder, pred_folder, categories) metrics = [("All", None), ("Things", True), ("Stuff", False)] results = {} for name, isthing in metrics: results[name], per_class_results = pq_stat.pq_average(categories, isthing=isthing) if name == 'All': results['per_class'] = per_class_results print("{:10s}| {:>5s} {:>5s} {:>5s} {:>5s}".format("", "PQ", "SQ", "RQ", "N")) print("-" * (10 + 7 * 4)) for name, _isthing in metrics: print("{:10s}| {:5.1f} {:5.1f} {:5.1f} {:5d}".format( name, 100 * results[name]['pq'], 100 * results[name]['sq'], 100 * results[name]['rq'], results[name]['n']) ) t_delta = time.time() - start_time print("Time elapsed: {:0.2f} seconds".format(t_delta)) return results if name == "main": parser = argparse.ArgumentParser() parser.add_argument('--gt_json_file', type=str, help="JSON file with ground truth data") parser.add_argument('--pred_json_file', type=str, help="JSON file with predictions data") parser.add_argument('--gt_folder', type=str, default=None, help="Folder with ground turth COCO format segmentations. \ Default: X if the corresponding json file is X.json") parser.add_argument('--pred_folder', type=str, default=None, help="Folder with prediction COCO format segmentations. \ Default: X if the corresponding json file is X.json") args = parser.parse_args() pq_compute(args.gt_json_file, args.pred_json_file, args.gt_folder, args.pred_folder) 调用pq_compute，不修改源码，如何多进程池中pq_compute_single_core的print 重定向

results = pool.map(pq_compute_single_core, args_list) **原理说明**： - 每个子进程在启动时都会执行 init_worker 函数，该函数将子进程的 sys.stdout 重定向到一个空设备（os.devnull），从而抑制...

Crowd_detect_test.rar_OPENCV 人脸识别_detect opencv_opencv 人脸

在这个名为"Crowd_detect_test.rar"的压缩包中，我们聚焦于一个特定的应用领域——人脸识别。这个项目利用了OpenCV的面部检测功能，通过"Detect opencv"模块来识别图像或视频流中的人脸。人脸识别是计算机视觉领域...

import torch, os, cv2 from model.model import parsingNet from utils.common import merge_config from utils.dist_utils import dist_print import torch import scipy.special, tqdm import numpy as np import torchvision.transforms as transforms from data.dataset import LaneTestDataset from data.constant import culane_row_anchor, tusimple_row_anchor if name == "main": torch.backends.cudnn.benchmark = True args, cfg = merge_config() dist_print('start testing...') assert cfg.backbone in ['18','34','50','101','152','50next','101next','50wide','101wide'] if cfg.dataset == 'CULane': cls_num_per_lane = 18 elif cfg.dataset == 'Tusimple': cls_num_per_lane = 56 else: raise NotImplementedError net = parsingNet(pretrained = False, backbone=cfg.backbone,cls_dim = (cfg.griding_num+1,cls_num_per_lane,4), use_aux=False).cuda() # we dont need auxiliary segmentation in testing state_dict = torch.load(cfg.test_model, map_location='cpu')['model'] compatible_state_dict = {} for k, v in state_dict.items(): if 'module.' in k: compatible_state_dict[k[7:]] = v else: compatible_state_dict[k] = v net.load_state_dict(compatible_state_dict, strict=False) net.eval() img_transforms = transforms.Compose([ transforms.Resize((288, 800)), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), ]) if cfg.dataset == 'CULane': splits = ['test0_normal.txt', 'test1_crowd.txt', 'test2_hlight.txt', 'test3_shadow.txt', 'test4_noline.txt', 'test5_arrow.txt', 'test6_curve.txt', 'test7_cross.txt', 'test8_night.txt'] datasets = [LaneTestDataset(cfg.data_root,os.path.join(cfg.data_root, 'list/test_split/'+split),img_transform = img_transforms) for split in splits] img_w, img_h = 1640, 590 row_anchor = culane_row_anchor elif cfg.dataset == 'Tusimple': splits = ['test.txt'] datasets = [LaneTestDataset(cfg.data_root,os.path.join(cfg.data_root, split),img_transform = img_transforms) for split in splits] img_w, img_h = 1280, 720 row_anchor = tusimple_row_anchor else: raise NotImplementedError for split, dataset in zip(splits, datasets): loader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle = False, num_workers=1) fourcc = cv2.VideoWriter_fourcc('MJPG') print(split[:-3]+'avi') vout = cv2.VideoWriter(split[:-3]+'avi', fourcc , 30.0, (img_w, img_h)) for i, data in enumerate(tqdm.tqdm(loader)): imgs, names = data imgs = imgs.cuda() with torch.no_grad(): out = net(imgs) col_sample = np.linspace(0, 800 - 1, cfg.griding_num) col_sample_w = col_sample[1] - col_sample[0] out_j = out[0].data.cpu().numpy() out_j = out_j[:, ::-1, :] prob = scipy.special.softmax(out_j[:-1, :, :], axis=0) idx = np.arange(cfg.griding_num) + 1 idx = idx.reshape(-1, 1, 1) loc = np.sum(prob idx, axis=0) out_j = np.argmax(out_j, axis=0) loc[out_j == cfg.griding_num] = 0 out_j = loc # import pdb; pdb.set_trace() vis = cv2.imread(os.path.join(cfg.data_root,names[0])) for i in range(out_j.shape[1]): if np.sum(out_j[:, i] != 0) > 2: for k in range(out_j.shape[0]): if out_j[k, i] > 0: ppp = (int(out_j[k, i] * col_sample_w * img_w / 800) - 1, int(img_h * (row_anchor[cls_num_per_lane-1-k]/288)) - 1 ) cv2.circle(vis,ppp,5,(0,255,0),-1) vout.write(vis) vout.release()

这是一个使用PyTorch框架测试模型的代码，代码中导入了许多必要的库以及自定义的模型和数据集等。通过调用PyTorch的后端，启用一些加速技术，然后解析配置参数，并根据数据集类型设置类别数。创建一个模型实例，传入...

Traceback (most recent call last): File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 579, in <module> main(demand_file=demand_file, depot_file=depot_file, v_cap=3) File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 541, in main res1 = local_search_one(crowd_route_list1, route_list1, sol, model, node) File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 448, in local_search_one a1 = checkPriority(owncar_route) File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 276, in checkPriority if int(route[i]) <= 16: ValueError: invalid literal for int() with base 10: 'd1'

你需要检查 route 列表中 route[i] 是否包含有效的数字字符串。如果不是，你需要找到原因并修复它。你可以在代码中添加一些调试语句，例如使用 print 函数输出相关变量的值，以便找到错误的原因。还可以使用...

C:\Users\lmx\Anaconda3\python.exe C:/Users/lmx/PycharmProjects/pythonProject/1.py Traceback (most recent call last): File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 539, in <module> main(demand_file=demand_file, depot_file=depot_file, epochs=250, v_cap=3) File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 503, in main res1 = local_search_one(crowd_route_list1, route_list1, sol, model, node) File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 424, in local_search_one a1 = checkPriority(owncar_route) File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 273, in checkPriority if route[i] <= 16: TypeError: '<=' not supported between instances of 'str' and 'int'

你需要检查一下你的 route 这个列表中的元素类型是否正确。如果其中包含了字符串类型的元素，你需要将它们转换为整数类型或者将整数类型的元素转换为字符串类型，以保证它们能够正确比较。例如： python if ...

Traceback (most recent call last): File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 589, in <module> main(demand_file=demand_file, depot_file=depot_file, v_cap=3) File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 551, in main res1 = local_search_one(crowd_route_list1, route_list1, sol, model, node) File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 453, in local_search_one owncar_route.index(0, 'd1') TypeError: slice indices must be integers or have an index method

根据错误提示，你传递给了owncar_route.index()方法的参数是(0, 'd1')，这个参数是一个元组，其中包含两个元素，第一个元素是0，第二个元素是字符串'd1'。然而，列表的index()方法只接受一个参数，并且这个...

class CrossScaleAttention(nn.Module): def init(self, channels): super().init() self.query = nn.Conv2d(channels, channels // 8, 1) self.key = nn.Conv2d(channels, channels // 8, 1) self.value = nn.Conv2d(channels, channels, 1) self.softmax = nn.Softmax(dim=-1) def forward(self, high_res, low_res): B, C, H, W = high_res.shape query = self.query(high_res).view(B, -1, H * W).permute(0, 2, 1) key = self.key(F.interpolate(low_res, size=(H, W))).view(B, -1, H * W) attention = self.softmax(torch.bmm(query, key)) value = self.value(high_res).view(B, -1, H * W) out = torch.bmm(value, attention.permute(0, 2, 1)).view(B, C, H, W) return out + high_res这段代码出自哪里？找到最相似的论文

在谷歌学术搜索关键词：cross scale attention module pytorch，发现一篇论文《Cross-Scale Attention Network for Crowd Counting》，其中可能包含类似的跨尺度注意力模块。此外，还有《Cross-Scale Internal Graph...

Traceback (most recent call last): File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 523, in <module> main(demand_file=demand_file, depot_file=depot_file, epochs=250, v_cap=3) File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 503, in main sol3, route_list3 = local_search_two(crowd_route_list1, route_list2, sol, model, node) TypeError: cannot unpack non-iterable NoneType object

这个错误通常出现在你试图对一个 NoneType 对象...res = local_search_two(crowd_route_list1, route_list2, sol, model, node) if res is not None: sol3, route_list3 = res else: # 处理 res 为 None 的情况

# 伪代码示例 class MonitoringSystem: def init(self): self.modules = { 'helmet': HelmetDetector(model='yolov5s-helmet.pt'), 'crowd': CrowdDetector(model='yolov5s-crowd.pt'), 'intrusion': IntrusionDetector(), 'parking': ParkingDetector() } def run_parallel(self): # 使用多线程处理各模块 with concurrent.futures.ThreadPoolExecutor() as executor: results = list(executor.map(lambda m: m.detect(), self.modules.values()))

model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # 使用预训练的小型模型[^1] def detect_objects(image_path, output_dir): """ 对单张图片进行目标检测，并保存结果到指定目录。 :param image_path:...

library'maven:com.tmall.wireless.crowd:crowd-new-client:2.2.8'has broken path

好的，我现在要解决Maven依赖中crowd-new-client:2.2.8路径损坏的问题。首先，我需要了解这个问题的具体表现是什么。通常，Maven依赖损坏可能有几种情况，比如无法下载依赖、本地仓库中的文件损坏、或者pom文件配置...

Traceback (most recent call last): File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 539, in <module> main(demand_file=demand_file, depot_file=depot_file, epochs=250, v_cap=3) File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 503, in main 众包车辆路径 [['d2', 7, 23], ['d3', 11, 27], ['d4', 5, 21]] [['d1', 10, 26, 1, 17, 2, 18, 3, 19, 4, 20, 'd1'], ['d1', 8, 24, 6, 22, 'd1'], ['d1', 15, 31, 'd1'], ['d1', 12, 28, 9, 25, 13, 29, 'd1'], ['d1', 16, 32, 14, 30, 'd1']] [['d1', 15, 31, 'd1'], ['d1', 12, 28, 9, 25, 13, 29, 'd1'], ['d1', 16, 32, 14, 30, 'd1'], ['d1', 8, 24, 5, 21, 'd1'], ['d1', 10, 26, 1, 17, 2, 18, 3, 19, 4, 20, 'd1']] res1 = local_search_one(crowd_route_list1, route_list1, sol, model, node) File "C:/Users/lmx/PycharmProjects/pythonProject/1.py", line 412, in local_search_one owncar_route = random.choice(route_list) File "C:\Users\lmx\Anaconda3\lib\random.py", line 261, in choice raise IndexError('Cannot choose from an empty sequence') from None IndexError: Cannot choose from an empty sequence

这个错误是因为你在使用 random.choice 时，传入的序列是空的，因此无法从中选择任何元素。你需要检查一下你的 route_list 是否为空。如果是空的，你需要在调用... owncar_route = random.choice(route_list)

$ git commit -m "Initial commit" [master (root-commit) cf094c9] Initial commit 6 files changed, 498 insertions(+) create mode 100644 tmp_dmp_pkg_imei_mapping_d_yc.sql create mode 100644 tmp_imei_account_rta_pass_d.sql create mode 100644 tmp_imei_choice_req_and_miss_msg_cnt_d.sql create mode 100644 tmp_imei_estimate_group_d.sql create mode 100644 tmp_push_account_crowd_ad_cnt.sql create mode 100644 tmp_push_imei_pkg_info_d.sql

git rev-list --max-parents=0 HEAD | head -n 1 此命令会直接输出首次提交的完整哈希值（如9f1d8fa5...）[^1]。 --- ### 3. **查看首次提交的详细信息** 通过git show命令查看具体修改内容： bash git...

解释一下 # 路径 path = 'D:\\desk\\LaughDetection-master\\crowd_laugh_1.wav' # 时间序列采样概率 num,samplerate = librosa.load(path, mono=True, sr=None, offset=0.0, duration=None) # 音频时间 time = librosa.get_duration(y = num, sr=samplerate, S=None, n_fft=2048, hop_length=512, center=True, path=path) # mfcc处理 mfcc_data = librosa.feature.mfcc(y=num, sr=samplerate, S=None, n_mfcc=1,hop_length=512, n_fft=2048) # 绘制波形的幅度包络线 librosa.display.waveshow(y = num, sr=samplerate, axis='time', offset=0.0, ax=None) plt.show() path1 = 'D:\\desk\\LaughDetection-master\\freinds_laugh.wav' num1, samplerate1 = librosa.load(path1, mono=True, sr=None, offset=0.0, duration=None) time1 = librosa.get_duration(y=num1, sr=samplerate1, S=None, n_fft=2048, hop_length=512, center=True, path=path1) mfcc_data1 = librosa.feature.mfcc(y=num1, sr=samplerate1, S=None, n_mfcc=1,hop_length=512, n_fft=2048) # 绘制波形的幅度包络线 librosa.display.waveshow(y=num1, sr=samplerate1, axis='time', offset=0.0, ax=None) plt.show() print(num)

这段代码是使用Python的librosa库对音频文件进行处理和分析的过程。具体解释如下： - 第一行是定义音频文件的路径。 - 第二行是使用librosa库中的load函数读取音频文件，并返回音频的时间序列和采样率。...

import os import pandas as pd import SimpleITK as sitk from radiomics import featureextractor def define_extractor(): settings = { ‘Interpolator’: sitk.sitkBSpline, # 保持BSpline插值，适合软组织 # 分辨率优化：乳腺钼靶需要更高分辨率 ‘resampledPixelSpacing’: [0.1, 0.1, 1], # 提高XY平面分辨率(原0.5) # 灰度处理优化：适应乳腺动态范围 ‘voxelArrayShift’: 100, # 提升基线值(原0)，避免低值组织信息丢失 ‘normalizeScale’: 200, # 扩大归一化范围(原50)，保留更多组织差异 # 直方图优化：适应乳腺组织特性 ‘binWidth’: 5, # 减小分箱宽度(原10)，提高灰度灵敏度 # 多尺度滤波器优化 ‘sigma’: [3.0,5.0], # 增加更细粒度的尺度(原[1,2,3]) # 新增乳腺专用参数 'force2D': True, 'correctMask': True, 'textureWindow': 7, # 添加纹理分析窗口大小 'tumorMargin': 3, # 肿瘤边缘扩展像素(用于捕获周边反应) 'glandularWeighting': 0.7 # 腺体组织加权系数 } extractor = featureextractor.RadiomicsFeatureExtractor(**settings) extractor.enableImageTypeByName('LoG') extractor.enableImageTypeByName('Wavelet') extractor.enableImageTypeByName('LBP2D') # 局部二值模式，适合纹理分析 # 启用特定特征 extractor.enableFeaturesByName( firstorder=['Energy', 'TotalEnergy', 'Entropy', 'Minimum', '10Percentile', '90Percentile', 'Maximum', 'Mean', 'Median', 'InterquartileRange', 'Range', 'MeanAbsoluteDeviation', 'RobustMeanAbsoluteDeviation', 'RootMeanSquared', 'Skewness', 'Kurtosis', 'Variance', 'Uniformity'] ) extractor.enableFeaturesByName( shape=['VoxelVolume', 'MeshVolume', 'SurfaceArea', 'SurfaceVolumeRatio','Compactness1', 'Compactness2', 'Sphericity', 'SphericalDisproportion','Maximum3DDiameter', 'Maximum2DDiameterSlice', 'Maximum2DDiameterColumn','Maximum2DDiameterRow', 'MajorAxisLength', 'MinorAxisLength', 'LeastAxisLength', 'Elongation', 'Flatness'] ) extractor.enableFeaturesByName( GLCM=['Autocorrelation', 'JointAverage', 'ClusterProminence', 'ClusterShade', 'ClusterTendency', 'Contrast', 'Correlation', 'DifferenceAverage', 'DifferenceEntropy', 'DifferenceVariance', 'JointEnergy', 'JointEntropy', 'Imc1', 'Imc2', 'Idm', 'MCC', 'Idmn', 'Id', 'Idn', 'InverseVariance', 'MaximumProbability', 'SumAverage', 'SumEntropy', 'SumSquares'] ) return extractor def extract_features_single(image_path, label_path, extractor): “”“提取单个图像对的特征”“” image = sitk.ReadImage(image_path) label = sitk.ReadImage(label_path) feature_vector = extractor.execute(image, label, label=1) return feature_vector def process_patient_folder(patient_dir, extractor, output_dir): “”“处理单个患者的文件夹”“” patient_id = os.path.basename(patient_dir) results = [] # 识别视图类型 (CC/MLO) for file in os.listdir(patient_dir): file_lower = file.lower() if file_lower.endswith(('.nii', '.nii.gz', '.mhd', '.dcm')): # 识别图像类型 if 'cc' in file_lower and 'seg' not in file_lower: image_cc = os.path.join(patient_dir, file) elif 'mlo' in file_lower and 'seg' not in file_lower: image_mlo = os.path.join(patient_dir, file) elif 'cc' in file_lower and 'seg' in file_lower: label_cc = os.path.join(patient_dir, file) elif 'mlo' in file_lower and 'seg' in file_lower: label_mlo = os.path.join(patient_dir, file) # 处理CC视图 if 'image_cc' in locals() and 'label_cc' in locals(): features_cc = extract_features_single(image_cc, label_cc, extractor) features_cc['Patient_ID'] = patient_id features_cc['View'] = 'CC' results.append(features_cc) # 处理MLO视图 if 'image_mlo' in locals() and 'label_mlo' in locals(): features_mlo = extract_features_single(image_mlo, label_mlo, extractor) features_mlo['Patient_ID'] = patient_id features_mlo['View'] = 'MLO' results.append(features_mlo) return results def batch_extract_features(root_dir, output_dir): “”“批量处理所有患者”“” extractor =define_extractor() all_features = [] # 确保输出目录存在 os.makedirs(output_dir, exist_ok=True) # 遍历所有患者文件夹 patient_folders = [d for d in os.listdir(root_dir) if os.path.isdir(os.path.join(root_dir, d))] for folder in patient_folders: patient_dir = os.path.join(root_dir, folder) try: features = process_patient_folder(patient_dir, extractor, output_dir) all_features.extend(features) print(f"成功处理患者: {folder}") except Exception as e: print(f"处理患者{folder}时出错: {str(e)}") # 保存所有结果 if all_features: df = pd.DataFrame(all_features) output_path = os.path.join(output_dir, "breasts_features0") df.to_csv(output_path, index=False) print(f"所有特征已保存至: {output_path}") return df return None if name == ‘main’: # 配置路径 ROOT_DIR = r"F:\breast" # 患者文件夹根目录 OUTPUT_DIR = r"C:\Users\lenovo\doc\radiomics_results" # 输出目录 # 执行批量处理 batch_extract_features(ROOT_DIR, OUTPUT_DIR) 我的代码为社么出现以下问题，我需要移除哪些代码？ C:\Users\lenovo\anaconda3\envs\radi\python.exe F:\PythonProject2\crowd.py Feature Compactness1 is deprecated, use with caution! Feature Compactness2 is deprecated, use with caution! Feature SphericalDisproportion is deprecated, use with caution! GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated Image too small to apply LoG filter, size: [108 82 1] GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated

另外，在batch_extract_features函数中，我们保存结果的文件名是"breasts_features0"，这没有后缀，我们应该加上.csv后缀。所以，我们将输出文件名改为"breasts_features0.csv"。修改后的代码：根据您提供...

C# Socket通信源码：多连接支持与断线重连功能的物联网解决方案

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