ModelBox实战开发：RK3568实现摄像头虚拟背景

"""
OpenCV 读取摄像头视频视频流，使用原生的onnxruntime推理
"""
# 导入OpenCV
import cv2
import time
import drawUtils
import numpy as np
import onnxruntime
# cap = cv2.VideoCapture(0)
cap = cv2.VideoCapture('test.mp4')

if not cap.isOpened():
 print('文件不存在或编码错误')
else:
 i = 0
 fps = 30
 start_time = time.time()
 font = cv2.FONT_HERSHEY_PLAIN
 image_background = cv2.imread('R-C.jpg')
 width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
 height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
 onnx_model = onnxruntime.InferenceSession('linknet.onnx')
 image_background = cv2.resize(image_background, (width, height))
 writer = cv2.VideoWriter('test_result.mp4',cv2.VideoWriter_fourcc(*'X264'),fps,(width,height))
 image_background = image_background/255.

while cap.isOpened():
 ret,frame = cap.read()
 if ret:
 img = cv2.resize(frame, (256, 256))
 img = img[...,::-1]/255.
 img = img.astype(np.float32)
 img = np.transpose(img, (2, 0, 1))
 data = np.expand_dims(img, axis=0)
 onnx_input ={onnx_model.get_inputs()[0].name: data}
 pred_mask = onnx_model.run(None, onnx_input)
 pred_mask = np.array(pred_mask)
 pred_mask = pred_mask[0][0][0]
 pred_mask = cv2.resize(pred_mask, (width, height))
 pred_mask = pred_mask.reshape((height, width, 1))
 img_multi = pred_mask*frame/255.
 mask_layer = np.ones((height, width)).reshape(height, width, 1)-pred_mask
 mask_layer = image_background*mask_layer
 image_add = img_multi+mask_layer
 image_add = image_add*255
 image_add = image_add.astype(np.uint8)
 # 计算FPS
 i += 1
 now = time.time()
 fps_text = int(1 / ( now - start_time))
 start_time = now
 print('linknet post ' + str(i))
 # 添加中文（首先导入模块）
 img_add = drawUtils.cv2AddChineseText(image_add, '帧率：'+str(fps_text), (20,50), textColor=(0, 255, 0), textSize=30)
 # 显示画面
 # cv2.imshow('demo',img_add)
 writer.write(img_add)
 # 退出条件
 if cv2.waitKey(1) & 0xFF == ord('q'):
 break
 else:
 break

cap.release()
cv2.destroyAllWindows()

# 用于本地mock文件读取任务，脚本中已经配置了IVA_SVC_CONFIG环境变量, 添加了此文件路径
########### 请确定使用linux的路径类型，比如在windows上要用  D:/xxx/xxx  不能用D:\xxx\xxx  ###########
# 任务的参数为一个压缩并转义后的json字符串
# 直接写需要转义双引号， 也可以用 content_file 添加一个json文件，如果content和content_file都存在content会被覆盖
# content_file支持绝对路径或者相对路径，不支持解析环境变量(包括${HILENS_APP_ROOT}、${HILENS_DATA_DIR}等)
[common]
content = "{\"param_str\":\"string param\",\"param_int\":10,\"param_float\":10.5}"
# 任务输入配置，mock模拟目前仅支持一路rtsp或者本地url, 当前支持以下几种输入方式：
# 1. rtsp摄像头或rtsp视频流：type="rtsp", url="rtsp://xxx.xxx"  (type为rtsp的时候，支持视频中断自动重连)
# 2. 设备自带摄像头或者USB摄像头：type="url"，url="摄像头编号,比如 0 或者 1 等" (需配合local_camera功能单元使用)
# 3. 本地视频文件：type="url"，url="视频文件路径" (可以是相对路径 -- 相对这个mock_task.toml文件, 也支持从环境变量${HILENS_APP_ROOT}所在目录文件输入)
# 4. http服务：type="url", url="http://xxx.xxx"(指的是任务作为http服务启动，此处需填写对外暴露的http服务地址，需配合httpserver类的功能单元使用)
# 5. 支持多输入[input] [input1] [input2] ...，对应的输出为[output] [output1] [output2] ...，如果使用videoout功能单元输出，则输入和输出个数必须匹配，同时url不能重名
[input]
type = "url"
url = "0"
#url = "../data/test.mp4"
# 任务输出配置，当前支持以下几种输出方式：
# 1. rtsp视频流：type="local", url="rtsp://xxx.xxx"
# 2. 本地屏幕：type="local", url="0:xxx" (设备需要接显示器，系统需要安装桌面)
# 3. 本地视频文件：type="local"，url="视频文件路径" (可以是相对路径——相对这个mock_task.toml文件, 也支持输出到环境变量${HILENS_DATA_DIR}所在目录或子目录)
# 4. http服务：type="webhook", url="http://xxx.xxx" (指的是任务产生的数据上报给某个http服务，此处需填写上传的http服务地址)
[output]
type = "local"
url = "0"
#url = "../hilens_data_dir/test_result.mp4"

rock@rock-3a:~$ cd /home/rock/modelbox
rock@rock-3a:~/modelbox$ ./create.py -t server -n virtual_background
sdk version is modelbox-rk-aarch64-1.5.3
success: create virtual_background in /home/rock/modelbox/workspace

rock@rock-3a:~/modelbox$ ./create.py -t infer -n linknet_infer -p virtual_background
sdk version is modelbox-rk-aarch64-1.5.3
success: create infer linknet_infer in /home/rock/modelbox/workspace/virtual_background/model/linknet_infer

# Copyright (C) 2020 Huawei Technologies Co., Ltd. All rights reserved.
[base]
name = "linknet_infer"
device = "rknpu"
version = "1.0.0"
description = "your description"
entry = "./linknet.rknn" # model file path, use relative path
type = "inference"
virtual_type = "rknpu2" # inference engine type: rockchip now support rknpu, rknpu2(if exist)
group_type = "Inference" # flowunit group attribution, do not change
is_input_contiguous = "false" # rk do not support memory combine, fix, do not change
# Input ports description
[input]
[input.input1]  # input port number, Format is input.input[N]
name = "Input" # input port name
type = "uint8" # input port data type ,e.g. float or uint8
device = "rknpu" # input buffer type: use rknpu for zero-copy, cpu also allow
# Output ports description
[output]
[output.output1] # output port number, Format is output.output[N]
name = "Output" # output port name
type = "float" # output port data type ,e.g. float or uint8

rock@rock-3a:~/modelbox$ ./create.py -t python -n linknet_post -p virtual_background
sdk version is modelbox-rk-aarch64-1.5.3
success: create python linknet_post in /home/rock/modelbox/workspace/virtual_background/etc/flowunit/linknet_post

# Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
# Basic config
[base]
name = "linknet_post" # The FlowUnit name
device = "cpu" # The flowunit runs on cpu
version = "1.0.0" # The version of the flowunit
type = "python" # Fixed value, do not change
description = "description" # The description of the flowunit
entry = "linknet_post@linknet_postFlowUnit" # Python flowunit entry function
group_type = "Generic" # flowunit group attribution, change as Input/Output/Image/Generic ...
# Flowunit Type
stream = false # Whether the flowunit is a stream flowunit
condition = false # Whether the flowunit is a condition flowunit
collapse = false # Whether the flowunit is a collapse flowunit
collapse_all = false # Whether the flowunit will collapse all the data
expand = false #  Whether the flowunit is a expand flowunit
# The default Flowunit config
[config]
mask_h = 256
mask_w = 256
# Input ports description
[input]
[input.input1] # Input port number, the format is input.input[N]
name = "in_mask" # Input port name
type = "float" # Input port type
[input.input2] # Input port number, the format is input.input[N]
name = "in_image" # Input port name
type = "uint8" # Input port type
# Output ports description
[output]
[output.output1] # Output port number, the format is output.output[N]
name = "out_image" # Output port name
type = "uint8" # Output port type

# Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import _flowunit as modelbox
import numpy as np
import cv2
class linknet_postFlowUnit(modelbox.FlowUnit):
 # Derived from modelbox.FlowUnit
 def __init__(self):
 super().__init__()
 self.image_background = cv2.imread('data/R-C.jpg')
 self.image_background = cv2.cvtColor(self.image_background,cv2.COLOR_BGR2RGB)
 self.image_background = self.image_background/255.
 def open(self, config):
 # Open the flowunit to obtain configuration information
 self.mask_h = config.get_int('mask_h', 256)
 self.mask_w = config.get_int('mask_w', 256)
 self.index = 0 
 return modelbox.Status.StatusCode.STATUS_SUCCESS
 def process(self, data_context):
 # Process the data
 in_mask = data_context.input("in_mask")
 in_image = data_context.input("in_image")
 out_image = data_context.output("out_image")
 # linknet_post process code.
 # Remove the following code and add your own code here.
 for buffer_mask, buffer_image in zip(in_mask, in_image):
 # 获取输入Buffer的属性信息
 width = buffer_image.get('width')
 height = buffer_image.get('height')
 channel = buffer_image.get('channel')
 # 将输入Buffer转换为numpy对象
 image_background = cv2.resize(self.image_background, (width, height))
 mask_data = np.array(buffer_mask.as_object(), copy=False)
 mask_data = mask_data.reshape(self.mask_h, self.mask_w)
 mask_data = cv2.resize(mask_data, (width, height))
 mask_data = mask_data.reshape(height, width, 1)
 image_data = np.array(buffer_image.as_object(), dtype=np.uint8, copy=False)
 image_data = image_data.reshape(height, width, channel)
 img_multi = mask_data*image_data/255.
 mask_layer = np.ones((height, width))
 mask_layer = mask_layer.reshape(height, width, 1)
 mask_layer = mask_layer-mask_data
 mask_layer = image_background*mask_layer
 image_add = img_multi+mask_layer
 image_add = image_add*255
 image_add = image_add.astype(np.uint8)
 # frame计数
 self.index += 1
 print("linknet_post " + str(self.index))
 # 将业务处理返回的结果数据转换为Buffer
 add_buffer = modelbox.Buffer(self.get_bind_device(), image_add)
 # 设置输出Buffer的Meta信息，此处直接拷贝输入Buffer的Meta信息
 add_buffer.copy_meta(buffer_image)
 # 将输出Buffer放入输出BufferList中
 out_image.push_back(add_buffer)
 return modelbox.Status.StatusCode.STATUS_SUCCESS
 def close(self):
 # Close the flowunit
 return modelbox.Status()
 def data_pre(self, data_context):
 # Before streaming data starts
 return modelbox.Status()
 def data_post(self, data_context):
 # After streaming data ends
 return modelbox.Status()
 def data_group_pre(self, data_context):
 # Before all streaming data starts
 return modelbox.Status()
 def data_group_post(self, data_context):
 # After all streaming data ends
 return modelbox.Status()

# Copyright (C) 2020 Huawei Technologies Co., Ltd. All rights reserved.
[driver]
dir = ["${HILENS_APP_ROOT}/etc/flowunit",
"${HILENS_APP_ROOT}/etc/flowunit/cpp",
"${HILENS_APP_ROOT}/model",
"${HILENS_MB_SDK_PATH}/flowunit"]
skip-default = true
[profile]
profile=true
trace=true
dir="${HILENS_DATA_DIR}/mb_profile"
[graph]
format = "graphviz"
graphconf = """digraph virtual_background {
    node [shape=Mrecord]
 queue_size = 1
 batch_size = 1
    # 定义节点，即功能单元及其属性
    input1[type=input, flowunit=input, device=cpu, deviceid=0]
 data_source_parser[type=flowunit, flowunit=data_source_parser, device=cpu, deviceid=0]
 video_demuxer[type=flowunit, flowunit=video_demuxer, device=cpu, deviceid=0]
 video_decoder[type=flowunit, flowunit=video_decoder, device=rknpu, deviceid=0, pix_fmt="rgb"]
 image_resize[type=flowunit, flowunit=resize, device=rknpu, deviceid=0, image_width=256, image_height=256]
 linknet_infer[type=flowunit, flowunit=linknet_infer, device=rknpu, deviceid=0]
 linknet_post[type=flowunit, flowunit=linknet_post, device=cpu, deviceid=0]
 video_out[type=flowunit, flowunit=video_out, device=rknpu, deviceid=0]
    # 定义边，即功能间的数据传递关系
    input1:input -> data_source_parser:in_data
 data_source_parser:out_video_url -> video_demuxer:in_video_url
 video_demuxer:out_video_packet -> video_decoder:in_video_packet
 video_decoder:out_video_frame -> image_resize:in_image
 image_resize:out_image -> linknet_infer:Input
 linknet_infer:Output -> linknet_post:in_mask
 video_decoder:out_video_frame -> linknet_post:in_image
 linknet_post:out_image -> video_out:in_video_frame
}"""
[flow]
desc = "virtual_background run in modelbox-rk-aarch64"

# Copyright (C) 2020 Huawei Technologies Co., Ltd. All rights reserved.
[driver]
dir = ["${HILENS_APP_ROOT}/etc/flowunit",
"${HILENS_APP_ROOT}/etc/flowunit/cpp",
"${HILENS_APP_ROOT}/model",
"${HILENS_MB_SDK_PATH}/flowunit"]
skip-default = true
[profile]
profile=false
trace=false
dir="${HILENS_DATA_DIR}/mb_profile"
[graph]
format = "graphviz"
graphconf = """digraph virtual_background {
    node [shape=Mrecord]
 queue_size = 1
 batch_size = 1
    # 定义节点，即功能单元及其属性
    input1[type=input, flowunit=input, device=cpu, deviceid=0]
 data_source_parser[type=flowunit, flowunit=data_source_parser, device=cpu, deviceid=0]
 local_camera[type=flowunit, flowunit=local_camera, device=rknpu, deviceid=0, pix_fmt="rgb", cam_width=1280, cam_height=720]
 image_resize[type=flowunit, flowunit=resize, device=rknpu, deviceid=0, image_width=256, image_height=256]
 linknet_infer[type=flowunit, flowunit=linknet_infer, device=rknpu, deviceid=0]
 linknet_post[type=flowunit, flowunit=linknet_post, device=cpu, deviceid=0]
 video_out[type=flowunit, flowunit=video_out, device=rknpu, deviceid=0]
    # 定义边，即功能间的数据传递关系
    input1:input -> data_source_parser:in_data
 data_source_parser:out_video_url -> local_camera:in_camera_packet
 local_camera:out_camera_frame -> image_resize:in_image
 image_resize:out_image -> linknet_infer:Input
 linknet_infer:Output -> linknet_post:in_mask
 local_camera:out_camera_frame -> linknet_post:in_image
 linknet_post:out_image -> video_out:in_video_frame
}"""
[flow]
desc = "virtual_background run in modelbox-rk-aarch64"