更新时间:2022-08-18 GMT+08:00
示例-难例上传
难例上传示例如下所示:
import hilens import cv2 import numpy as np def run(): # 构造摄像头 cap = hilens.VideoCapture() disp = hilens.Display(hilens.HDMI) hard_sample = hilens.HardSample(0.5,0.5,1) # 1,2为检测模型使用的算法 #hard_sample = hilens.HardSample(0.5,0.5,0) # 0为分类模型使用的算法 hard_sample_flag = False # 是否存在难例上传配置 hard_sample_config = hilens.get_hard_sample_config() # 获取难例配置 if not hard_sample_config: hilens.warning("hardSampleConfig is empty") else: hard_sample_flag = True data_count = hard_sample_config["hard_sample_setting"][0]["data_count"] data_current_count = hard_sample_config["hard_sample_setting"][0]["datacur_count"] upload_jpeg_url = hard_sample_config["hard_sample_setting"][0]["dataset_path"] model_name = hard_sample_config["hard_sample_setting"][0]["model_name"] camera_name = "default" if data_count > data_current_count: upload_flag = True # upload_flag是否继续上传 else: upload_flag = False while True: # 获取一帧画面 frame = cap.read() if hard_sample_flag: if upload_flag: if hard_sample.hard_sample_detection_filter([[0.,0.,1280.,720.,0.4,1]]): # 检测算法的输入为后处理之后的检测框,每个检测框包括[xmin, ymin, xmax, ymax, conf, label](包括置信度和类别标签) #if hard_sample.hard_sample_classification_filter([0.2, 0.2, 0.2, 0.2, 0.2],5): # 分类算法的输入为各类别的概率,即模型的输出 hard_sample.upload_jpeg(upload_jpeg_url, data_current_count, model_name, camera_name, frame) data_current_count += 1 hard_sample_config["hard_sample_setting"][0]["datacur_count"] = data_current_count if data_current_count == 1 or data_current_count == data_count: if data_current_count == data_count: upload_flag = False hilens.set_hard_sample_config(hard_sample_config) # 更新端侧难例配置文件 #输出到HDMI disp.show(frame) if __name__ == '__main__': #参数【hello】要与基本信息的【检验值】一致。详情请查看开发指南 hilens.init("hello") run() hilens.terminate()
父主题: 难例上传模块