prev()_next()使用报错

本文介绍了一种购物车中商品数量动态调整的方法,通过自定义函数实现商品数量的增加和减少,并确保数量不会超出合理范围。

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//购物车数量的自增自减
	function qtyUpdate(kind,obj){
	    var count = 0;
	    if(kind == "up"){
	       	var tmp=obj.prev();
	       	count=$(tmp).val();
	       	count++;
	       	$(tmp).val(count);
	    }else if(kind == "down"){
	        if(count > 1) {
		        var ne=obj.next();
		        count=$(ne).val();
		        count--;
		        $(ne).val(count);
	        }
	    }
	}

 

---------------------------------------******************正解******************----------------------------------------

//购物车数量的自增自减
   	function qtyUpdate(kind,obj){
		var count = 0;
		if(kind == "up"){
		    var tmp=$(obj).prev();
		    count=$(tmp).val();
		     count++;
		     $(tmp).val(count);
		}else if(kind == "down"){
	             var ne=$(obj).next();
		     count=$(ne).val();
		     if(count > 1) {
			count--;
			$(ne).val(count);
		     }
		}
	}

 

 

def process_collision_data(input_file, output_file, stats_output_file): """ 处理碰撞数据,按scene_id和相机类型分组,生成mask标签 并按scene_id、cam_tag和timestamp降序输出,同时生成统计信息 :param input_file: 输入JSONL文件路径 :param output_file: 输出JSONL文件路径 :param stats_output_file: 统计信息输出JSON文件路径 """ # 读取并解析JSONL文件 data = [] with open(input_file, 'r', encoding='utf-8') as f: for line in f: try: item = json.loads(line) # 添加cam_tag字段到每个数据项 item['cam_tag'] = item['image_path'].split('/')[-2] data.append(item) except json.JSONDecodeError: print(f"JSON解析错误: {line}") continue # 按scene_id和相机类型分组 grouped_data = defaultdict(lambda: defaultdict(list)) for item in data: grouped_data[item['scene_id']][item['cam_tag']].append(item) # 初始化统计数据结构 stats = { "total_scenes": len(grouped_data), "scenes": {}, "overall": { "total_frames": 0, "yes_count": 0, "no_count": 0, "yes_ratio": 0.0, "no_ratio": 0.0 } } # 处理每个分组 processed_data = [] for scene_id, cam_groups in grouped_data.items(): scene_stats = { "scene_id": scene_id, "total_cam_tags": len(cam_groups), "cam_tags": {}, "total_frames": 0, "yes_count": 0, "no_count": 0 } for cam_type, cam_data in cam_groups.items(): # 初始化相机类型统计 cam_stats = { "cam_tag": cam_type, "total_frames": len(cam_data), "yes_count": 0, "no_count": 0 } # 按时间戳降序排序(最新时间在前) cam_data.sort(key=lambda x: x["timestamp"], reverse=True) # 状态跟踪变量(确保正确继承) prev_points = None prev_tag = None # 处理第一帧 if cam_data: if cam_data[0].get('mop_pnc_info'): # 第一帧固定为'yes' cam_data[0]['mop_pnc_info'][0]['tag'] = 'yes' cam_stats["yes_count"] += 1 # 提取(x, y)坐标点 prev_points = extract_points(cam_data[0]['mop_pnc_info'][0].get('roadPoints', [])) prev_tag = 'yes' print(f"场景 {scene_id} 相机 {cam_type}: 帧0 (最新) 标签设置为 'yes'") else: print(f"警告: scene_id={scene_id}, cam_type={cam_type} 的第一帧没有 mop_pnc_info 数据") prev_points = [] prev_tag = None # 处理第二帧(如果存在) if len(cam_data) > 1: current_item = cam_data[1] if current_item.get('mop_pnc_info'): # 提取当前帧的(x, y)坐标点 current_points = extract_points(current_item['mop_pnc_info'][0].get('roadPoints', [])) # 第二帧特殊处理:与第一帧比较 if current_points == prev_points: current_item['mop_pnc_info'][0]['tag'] = 'yes' print(f"场景 {scene_id} 相机 {cam_type}: 帧1 与帧0相同 -> 标签设置为 'yes'") else: current_item['mop_pnc_info'][0]['tag'] = 'no' print(f"场景 {scene_id} 相机 {cam_type}: 帧1 与帧0不同 -> 标签设置为 'no'") # 更新统计 if current_item['mop_pnc_info'][0]['tag'] == 'yes': cam_stats["yes_count"] += 1 else: cam_stats["no_count"] += 1 # 更新状态(用于下一帧比较) prev_points_for_next = current_points prev_tag_for_next = current_item['mop_pnc_info'][0]['tag'] else: print(f"警告: scene_id={scene_id}, cam_type={cam_type} 的第二帧没有 mop_pnc_info 数据") prev_points_for_next = prev_points prev_tag_for_next = prev_tag # 处理第三帧及后续帧 for i in range(2, len(cam_data)): current_item = cam_data[i] if current_item.get('mop_pnc_info'): # 提取当前帧的(x, y)坐标点 current_points = extract_points(current_item['mop_pnc_info'][0].get('roadPoints', [])) # 后续帧处理:与前一帧比较 if i == 2: # 第三帧特殊处理:与第二帧比较 if current_points == prev_points_for_next: current_item['mop_pnc_info'][0]['tag'] = prev_tag_for_next print(f"场景 {scene_id} 相机 {cam_type}: 帧{i} 与帧{i-1}相同 -> 继承标签 '{prev_tag_for_next}'") else: # 反转前一帧标签 current_item['mop_pnc_info'][0]['tag'] = 'no' if prev_tag_for_next == 'yes' else 'yes' print(f"场景 {scene_id} 相机 {cam_type}: 帧{i} 与帧{i-1}不同 -> 反转标签为 '{current_item['mop_pnc_info'][0]['tag']}'") else: # 第四帧及以后:与前一帧比较 if current_points == prev_points: current_item['mop_pnc_info'][0]['tag'] = prev_tag print(f"场景 {scene_id} 相机 {cam_type}: 帧{i} 与帧{i-1}相同 -> 继承标签 '{prev_tag}'") else: # 反转前一帧标签 current_item['mop_pnc_info'][0]['tag'] = 'no' if prev_tag == 'yes' else 'yes' print(f"场景 {scene_id} 相机 {cam_type}: 帧{i} 与帧{i-1}不同 -> 反转标签为 '{current_item['mop_pnc_info'][0]['tag']}'") # 更新统计 if current_item['mop_pnc_info'][0]['tag'] == 'yes': cam_stats["yes_count"] += 1 else: cam_stats["no_count"] += 1 # 更新状态(用于下一帧比较) prev_points = current_points prev_tag = current_item['mop_pnc_info'][0]['tag'] else: print(f"警告: scene_id={scene_id}, cam_type={cam_type} 的第{i+1}帧没有 mop_pnc_info 数据") # 计算相机类型占比 if cam_stats["total_frames"] > 0: cam_stats["yes_ratio"] = cam_stats["yes_count"] / cam_stats["total_frames"] cam_stats["no_ratio"] = cam_stats["no_count"] / cam_stats["total_frames"] else: cam_stats["yes_ratio"] = 0.0 cam_stats["no_ratio"] = 0.0 # 更新场景统计 scene_stats["cam_tags"][cam_type] = cam_stats scene_stats["total_frames"] += cam_stats["total_frames"] scene_stats["yes_count"] += cam_stats["yes_count"] scene_stats["no_count"] += cam_stats["no_count"] # 添加到处理后的数据 processed_data.extend(cam_data) # 计算场景占比 if scene_stats["total_frames"] > 0: scene_stats["yes_ratio"] = scene_stats["yes_count"] / scene_stats["total_frames"] scene_stats["no_ratio"] = scene_stats["no_count"] / scene_stats["total_frames"] else: scene_stats["yes_ratio"] = 0.0 scene_stats["no_ratio"] = 0.0 # 添加到全局统计 stats["scenes"][scene_id] = scene_stats stats["overall"]["total_frames"] += scene_stats["total_frames"] stats["overall"]["yes_count"] += scene_stats["yes_count"] stats["overall"]["no_count"] += scene_stats["no_count"] # 计算全局占比 if stats["overall"]["total_frames"] > 0: stats["overall"]["yes_ratio"] = stats["overall"]["yes_count"] / stats["overall"]["total_frames"] stats["overall"]["no_ratio"] = stats["overall"]["no_count"] / stats["overall"]["total_frames"] else: stats["overall"]["yes_ratio"] = 0.0 stats["overall"]["no_ratio"] = 0.0 # 按scene_id、cam_tag和timestamp降序排序 processed_data.sort(key=lambda x: ( x['scene_id'], x['cam_tag'], -x['timestamp'] )) # 写入输出文件 with jsonlines.open(output_file, 'w') as writer: for item in processed_data: writer.write(item) # 写入统计文件 with open(stats_output_file, 'w', encoding='utf-8') as f: json.dump(stats, f, indent=4, ensure_ascii=False) return stats 按照这个格式输出统计数据,修改上述生成的代码
07-30
import json import os from collections import defaultdict import jsonlines def process_collision_data(input_file, output_file, stats_output_file): """ 处理碰撞数据,按scene_id和相机类型分组,生成mask标签 并按scene_id、cam_tag和timestamp降序输出,同时生成统计信息 :param input_file: 输入JSONL文件路径 :param output_file: 输出JSONL文件路径 :param stats_output_file: 统计信息输出JSON文件路径 """ # 读取并解析JSONL文件 data = [] with open(input_file, 'r', encoding='utf-8') as f: for line in f: try: item = json.loads(line) # 添加cam_tag字段到每个数据项 item['cam_tag'] = item['image_path'].split('/')[-2] data.append(item) except json.JSONDecodeError: print(f"JSON解析错误: {line}") continue # 按scene_id和相机类型分组 grouped_data = defaultdict(lambda: defaultdict(list)) for item in data: grouped_data[item['scene_id']][item['cam_tag']].append(item) # 初始化统计数据结构 stats = { "total_scenes": len(grouped_data), "scenes": {}, "overall": { "total_frames": 0, "yes_count": 0, "no_count": 0, "yes_ratio": 0.0, "no_ratio": 0.0 } } # 处理每个分组 processed_data = [] for scene_id, cam_groups in grouped_data.items(): scene_stats = { "scene_id": scene_id, "total_cam_tags": len(cam_groups), "cam_tags": {}, "total_frames": 0, "yes_count": 0, "no_count": 0 } for cam_type, cam_data in cam_groups.items(): # 初始化相机类型统计 cam_stats = { "cam_tag": cam_type, "total_frames": len(cam_data), "yes_count": 0, "no_count": 0 } # 按时间戳降序排序(最新时间在前) cam_data.sort(key=lambda x: x["timestamp"], reverse=True) # 状态跟踪变量(修复问题:确保正确继承) prev_points = None prev_tag = None # 处理第一帧 if cam_data: if cam_data[0].get('mop_pnc_info'): # 第一帧固定为'yes' cam_data[0]['mop_pnc_info'][0]['tag'] = 'yes' cam_stats["yes_count"] += 1 prev_points = tuple(cam_data[0]['mop_pnc_info'][0].get('roadPoints', [])) prev_tag = 'yes' print(f"场景 {scene_id} 相机 {cam_type}: 帧0 (最新) 标签设置为 'yes'") else: print(f"警告: scene_id={scene_id}, cam_type={cam_type} 的第一帧没有 mop_pnc_info 数据") prev_points = tuple() prev_tag = None # 处理第二帧(如果存在) if len(cam_data) > 1: current_item = cam_data[1] if current_item.get('mop_pnc_info'): current_points = tuple(current_item['mop_pnc_info'][0].get('roadPoints', [])) # 第二帧特殊处理:与第一帧比较 if current_points == prev_points: current_item['mop_pnc_info'][0]['tag'] = 'yes' print(f"场景 {scene_id} 相机 {cam_type}: 帧1 与帧0相同 -> 标签设置为 'yes'") else: current_item['mop_pnc_info'][0]['tag'] = 'no' print(f"场景 {scene_id} 相机 {cam_type}: 帧1 与帧0不同 -> 标签设置为 'no'") # 更新统计 if current_item['mop_pnc_info'][0]['tag'] == 'yes': cam_stats["yes_count"] += 1 else: cam_stats["no_count"] += 1 # 更新状态(仅用于下一帧比较) prev_points_for_next = current_points # 用于下一帧比较的点 prev_tag_for_next = current_item['mop_pnc_info'][0]['tag'] # 用于下一帧比较的标签 else: print(f"警告: scene_id={scene_id}, cam_type={cam_type} 的第二帧没有 mop_pnc_info 数据") prev_points_for_next = prev_points prev_tag_for_next = prev_tag # 处理第三帧及后续帧(修复问题:确保正确继承) for i in range(2, len(cam_data)): current_item = cam_data[i] if current_item.get('mop_pnc_info'): current_points = tuple(current_item['mop_pnc_info'][0].get('roadPoints', [])) # 后续帧处理:与前一帧比较(关键修复:使用前一帧的状态) if i == 2: # 第三帧特殊处理:与第二帧比较 if current_points == prev_points_for_next: current_item['mop_pnc_info'][0]['tag'] = prev_tag_for_next print( f"场景 {scene_id} 相机 {cam_type}: 帧{i} 与帧{i - 1}相同 -> 继承标签 '{prev_tag_for_next}'") else: # 反转前一帧标签 current_item['mop_pnc_info'][0]['tag'] = 'no' if prev_tag_for_next == 'yes' else 'yes' print( f"场景 {scene_id} 相机 {cam_type}: 帧{i} 与帧{i - 1}不同 -> 反转标签为 '{current_item['mop_pnc_info'][0]['tag']}'") else: # 第四帧及以后:与前一帧比较 if current_points == prev_points: current_item['mop_pnc_info'][0]['tag'] = prev_tag print( f"场景 {scene_id} 相机 {cam_type}: 帧{i} 与帧{i - 1}相同 -> 继承标签 '{prev_tag}'") else: # 反转前一帧标签 current_item['mop_pnc_info'][0]['tag'] = 'no' if prev_tag == 'yes' else 'yes' print( f"场景 {scene_id} 相机 {cam_type}: 帧{i} 与帧{i - 1}不同 -> 反转标签为 '{current_item['mop_pnc_info'][0]['tag']}'") # 更新统计 if current_item['mop_pnc_info'][0]['tag'] == 'yes': cam_stats["yes_count"] += 1 else: cam_stats["no_count"] += 1 # 更新状态(用于下一帧比较) prev_points = current_points prev_tag = current_item['mop_pnc_info'][0]['tag'] else: print(f"警告: scene_id={scene_id}, cam_type={cam_type} 的第{i + 1}帧没有 mop_pnc_info 数据") # 计算相机类型占比 if cam_stats["total_frames"] > 0: cam_stats["yes_ratio"] = cam_stats["yes_count"] / cam_stats["total_frames"] cam_stats["no_ratio"] = cam_stats["no_count"] / cam_stats["total_frames"] else: cam_stats["yes_ratio"] = 0.0 cam_stats["no_ratio"] = 0.0 # 更新场景统计 scene_stats["cam_tags"][cam_type] = cam_stats scene_stats["total_frames"] += cam_stats["total_frames"] scene_stats["yes_count"] += cam_stats["yes_count"] scene_stats["no_count"] += cam_stats["no_count"] # 添加到处理后的数据 processed_data.extend(cam_data) # 计算场景占比 if scene_stats["total_frames"] > 0: scene_stats["yes_ratio"] = scene_stats["yes_count"] / scene_stats["total_frames"] scene_stats["no_ratio"] = scene_stats["no_count"] / scene_stats["total_frames"] else: scene_stats["yes_ratio"] = 0.0 scene_stats["no_ratio"] = 0.0 # 添加到全局统计 stats["scenes"][scene_id] = scene_stats stats["overall"]["total_frames"] += scene_stats["total_frames"] stats["overall"]["yes_count"] += scene_stats["yes_count"] stats["overall"]["no_count"] += scene_stats["no_count"] # 计算全局占比 if stats["overall"]["total_frames"] > 0: stats["overall"]["yes_ratio"] = stats["overall"]["yes_count"] / stats["overall"]["total_frames"] stats["overall"]["no_ratio"] = stats["overall"]["no_count"] / stats["overall"]["total_frames"] else: stats["overall"]["yes_ratio"] = 0.0 stats["overall"]["no_ratio"] = 0.0 # 按scene_id、cam_tag和timestamp降序排序 processed_data.sort(key=lambda x: ( x['scene_id'], x['cam_tag'], -x['timestamp'] )) # 写入输出文件 with jsonlines.open(output_file, 'w') as writer: for item in processed_data: writer.write(item) # 写入统计文件 with open(stats_output_file, 'w', encoding='utf-8') as f: json.dump(stats, f, indent=4, ensure_ascii=False) return stats # 使用示例 if __name__ == "__main__": stats = process_collision_data( "./task4/basicData_historyDepth_v2_img106k_250723__huanshi_6ver_4fish_large_model_900_792874_10frames.jsonl", "processed_106k_0723_1.jsonl", "collision_stats_0723_1.json" ) print(f"处理完成!统计信息已保存到 collision_stats.json") print(f"总场景数: {stats['total_scenes']}") print(f"总帧数: {stats['overall']['total_frames']}") print(f"YES占比: {stats['overall']['yes_ratio']:.2%}") print(f"NO占比: {stats['overall']['no_ratio']:.2%}") 修改mask打标逻辑:每组数据排序后的第一个默认是yes,第二个判断时,取出第二条数据的roadpoint的值在第一个的数据中进行遍历,如果均在数据1 里则为yes,否则为no,每个场景下的每个相机,当mask从yes转变为no,则该组的后续数据全部为no
最新发布
07-30
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