def run():
# 系统初始化，参数要与创建技能时填写的检验值保持一致
hilens.init("helmet")
# 读取技能配置
skill_cfg = hilens.get_skill_config()
if skill_cfg is None or 'server_url' not in skill_cfg or 'IPC_address' not in skill_cfg:
hilens.error("skill config not correct")
return
# 获取POST服务器地址和IPC地址，多个IPC地址用分号分隔开
server_url = skill_cfg['server_url']
camera_list = skill_cfg['IPC_address'].split(';')
# 每个IPC启动一个独立的线程
threads_list = []
for camera_address in camera_list:
t = threading.Thread(target=camera_thread,
args=(camera_address, server_url))
t.start()
threads_list.append(t)
for t in threads_list:
t.join()
hilens.terminate()
def camera_thread(camera_address, server_url):
# 初始化IPC
camera = hilens.VideoCapture(camera_address)
# 初始化模型
helmet_model_path = hilens.get_model_dir() + "helmet_template_model.om"
helmet_model = hilens.Model(helmet_model_path)
while True:
# 读取一帧图片(YUV NV21格式)
input_yuv = camera.read()
# 图片预处理：转为RGB格式、缩放为模型输入尺寸
img_rgb = cv2.cvtColor(input_yuv, cv2.COLOR_YUV2RGB_NV21)
img_preprocess, img_w, img_h = preprocess(img_rgb)
# 模型推理，并进行后处理得到检测框
output = helmet_model.infer([img_preprocess.flatten()])
bboxes = get_result(output, img_w, img_h)
# 从检测结果中判断是否有未佩戴安全帽的人
if no_helmet(bboxes):
# 后处理得到检测框，并在RGB图中画框
img_rgb = draw_boxes(img_rgb, bboxes)
# 以POST方式传输数据
try:
post_msg(server_url, img_rgb, bboxes)
except Exception as e:
hilens.error("post data failed!")
print("Reason : ", e)
# 等待5秒后再检测，避免发送数据过多
time.sleep(15)
def sendData(uri, res):
for item in res:
item[5] = '%.3f' % item[5] # Convert posibility from float to str
try:
requests.post(uri, json.dumps(res).encode('UTF-8'))
except ConnectionError as e:
hilens.error('Failed to send data, connection error:{}'.format(e))
except Timeout as e:
hilens.error('Failed to send data, connection timeout:{}'.format(e))
def image_test():
ret = hilens.init("")
if ret != 0:
hilens.error("Failed to initialize HiLens")
return
img_file = './flight_test1.jpg'
test_img(img_file, model_path)
hilens.terminate()
def run(work_space):
hilens.error("start!!!!!!!!!!!!!!")
# 创建两个线程
try:
_thread.start_new_thread(run_inner, (work_space, ))
_thread.start_new_thread(start_listen, ())
except:
print("Error: 无法启动线程")
while 1:
pass
hilens.error("end!!!!!!!!!!!!!!")
def run():
# 配置系统日志级别
hilens.set_log_level(hilens.ERROR)
# 系统初始化，参数要与创建技能时填写的检验值保持一致
hilens.init("gesture")
# 初始化模型
gesture_model_path = hilens.get_model_dir() + "gesture_template_model.om"
gesture_model = hilens.Model(gesture_model_path)
# 初始化本地摄像头与HDMI显示器
camera = hilens.VideoCapture()
display_hdmi = hilens.Display(hilens.HDMI)
# 上一次上传OBS图片的时间与上传间隔
last_upload_time = 0
upload_duration = 5
# 读取技能配置
skill_cfg = hilens.get_skill_config()
if skill_cfg is None or 'server_url' not in skill_cfg:
hilens.error("server_url not configured")
return
while True:
# 读取一帧图片(YUV NV21格式)
input_yuv = camera.read()
# 图片预处理：转为RGB格式、缩放为模型输入尺寸
img_rgb = cv2.cvtColor(input_yuv, cv2.COLOR_YUV2RGB_NV21)
img_preprocess, img_w, img_h = preprocess(img_rgb)
# 模型推理
output = gesture_model.infer([img_preprocess.flatten()])
# 后处理得到手势所在区域与类别，并在RGB图中画框
bboxes = get_result(output, img_w, img_h)
img_rgb = draw_boxes(img_rgb, bboxes)
# 输出处理后的图像到HDMI显示器，必须先转回YUV NV21格式
output_yuv = hilens.cvt_color(img_rgb, hilens.RGB2YUV_NV21)
display_hdmi.show(output_yuv)
# 上传OK手势图片到OBS，为防止OBS数据存储过多，间隔一定的时间才上传图片
if time.time() - last_upload_time > upload_duration:
# 截取出OK手势图片(如果有的话)
img_OK = get_OK(img_rgb, bboxes)
if img_OK is not None:
# 上传OK手势图片到OBS，图片(用当前时间命名)需要先转为BGR格式并按照jpg格式编码
img_OK = cv2.cvtColor(img_OK, cv2.COLOR_RGB2BGR)
img_OK = cv2.imencode('.jpg', img_OK)[1]
filename = time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())
ret = hilens.upload_bufer(filename + "_OK.jpg", img_OK,
"write")
if ret != 0:
hilens.error("upload pic failed!")
return
last_upload_time = time.time()
# 以POST方式传输处理后的整张图片
try:
post_msg(skill_cfg['server_url'], img_rgb)
except Exception as e:
hilens.error("post data failed!")
print("Reason : ", e)
hilens.terminate()
def run():
# 系统初始化，参数要与创建技能时填写的检验值保持一致
hilens.init("landmarks")
# 初始化自带摄像头与HDMI显示器
camera = hilens.VideoCapture()
display = hilens.Display(hilens.HDMI)
# 初始化模型：人脸检测模型(centerface)、人脸68个关键点检测模型
centerface_model_path = hilens.get_model_dir() + "centerface_template_model.om"
centerface_model = hilens.Model(centerface_model_path)
landmark_model_path = hilens.get_model_dir() + "landmark68_template_model.om"
landmark_model = hilens.Model(landmark_model_path)
# 本段代码展示如何录制HiLens Kit摄像头拍摄的视频
fps = 10
size = (1280, 720)
format = cv2.VideoWriter_fourcc('M','J','P','G') # 注意视频格式
writer = cv2.VideoWriter("face.avi", format, fps, size)
# 待保存视频的起始帧数，可自行调节或加入更多逻辑
frame_count = 0
frame_start = 100
frame_end = 150
uploaded = False
while True:
# 读取一帧图片(YUV NV21格式)
input_yuv = camera.read()
# 图片预处理：转为RGB格式、缩放为模型输入尺寸
img_rgb = cv2.cvtColor(input_yuv, cv2.COLOR_YUV2RGB_NV21)
img_pre = preprocess(img_rgb)
img_h, img_w = img_rgb.shape[:2]
# 人脸检测模型推理，并进行后处理得到画面中最大的人脸检测框
output = centerface_model.infer([img_pre.flatten()])
face_box = get_largest_face_box(output, img_h, img_w)
# 画面中检测到有人脸且满足一定条件
if face_box is not None:
# 截取出人脸区域并做预处理
img_face = preprocess_landmark(img_rgb, face_box)
# 人脸关键点模型推理，得到68个人脸关键点
output2 = landmark_model.infer([img_face.flatten()])
landmarks = output2[0].reshape(68, 2)
# 将人脸框和人脸关键点画在RGB图中
img_rgb = draw_landmarks(img_rgb, face_box, landmarks)
# 输出处理后的图像到HDMI显示器，必须先转换成YUV NV21格式
output_nv21 = hilens.cvt_color(img_rgb, hilens.RGB2YUV_NV21)
display.show(output_nv21)
# 录制一段视频并发送到OBS中
if not uploaded:
frame_count += 1
if frame_count > frame_end: # 录制结束点
uploaded = True
writer.release() # 先保存在本地
ret = hilens.upload_file("face.avi", "face.avi", "write") # 发送到OBS中
if ret != 0:
hilens.error("upload file failed!")
return
elif frame_count > frame_start: # 录制开始点
# 注意写入的图片格式必须为BGR
writer.write(cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR))
hilens.terminate()