def common_transfer(pb_name,export_name):
# 특정 로그를 참조하기위함.
ret = 0
rknn = RKNN()
# 이 단계는 grayscale 이미지를 다룰 때 필요하지 않음.
# rknn.config(channel_mean_value='', reorder_channel='')
print('--> Loading model')
ret = rknn.load_tensorflow(
tf_pb='./mnist_frozen_graph.pb',
inputs=['x'],
outputs=['y_conv'],
input_size_list=[[28,28,1]])
if ret != 0:
print('load_tensorflow error')
rknn.release()
return ret
print('done')
print('--> Building model')
rknn.build(do_quantization=False)
print('done')
# rknn 모델 파일 저장 내보내기
rknn.export_rknn('./mnist.rknn')
# Release RKNN Context
rknn.release()
return ret
def rknn_convert(input_model, output_model, dataset_file, target_platform):
# Create RKNN object
rknn = RKNN()
print('--> config model')
rknn.config(channel_mean_value='127.5 127.5 127.5 127.5', reorder_channel='0 1 2', batch_size=1, target_platform=target_platform)
# Load onnx model
print('--> Loading model')
ret = rknn.load_onnx(model=input_model)
if ret != 0:
print('Load failed!')
exit(ret)
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset=dataset_file, pre_compile=True)
if ret != 0:
print('Build failed!')
exit(ret)
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn(output_model)
if ret != 0:
print('Export .rknn failed!')
exit(ret)
# Release RKNN object
rknn.release()
def transfer(pb_path, rknn_name):
# Create RKNN object
rknn = RKNN()
# init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
# Config for Model Input PreProcess
rknn.config()
# Load TensorFlow Model
print('--> Loading model')
rknn.load_tensorflow(tf_pb=pb_path,
inputs=['Reshape'],
outputs=['probability'],
input_size_list=[[INPUT_WIDTH, INPUT_HEIGHT, 1]])
print('done')
# Build Model
print('--> Building model')
rknn.build(do_quantization=False)
print('done')
# Export RKNN Model
rknn.export_rknn(rknn_name)
# Release RKNN Context
rknn.release()
def test_rknn():
from rknn.api import RKNN
rknn = RKNN()
# Load rknn model
print('--> Load RKNN model')
ret = rknn.load_rknn('lprnet.rknn')
if ret != 0:
print('Export model failed!')
exit(ret)
# init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime(target='rk1808')
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
# Inference
print('--> Running model')
image = cv2.imread('data/eval/000256.png')
outputs = rknn.inference(inputs=[image])
preds = outputs[0]
labels, pred_labels = decode(preds, CHARS)
print(labels)
print('done')
rknn.release()
def main():
minsize = 20
threshold = [0.6, 0.7, 0.7]
factor = 0.709
pnet_rknn_list=init_pnet()
rnet_rknn = RKNN()
onet_rknn = RKNN()
rnet_rknn.load_rknn('./RNet.rknn')
onet_rknn.load_rknn('./ONet.rknn')
ret = rnet_rknn.init_runtime()
if ret != 0:
print('Init rnet runtime environment failed')
exit(ret)
ret = onet_rknn.init_runtime()
if ret != 0:
print('Init onet runtime environment failed')
exit(ret)
#error = []
#f = open(imglistfile, 'r')
sys.stdout = open('/dev/stdout', 'w')
sys.stderr = open('/dev/stderr', 'w')
capture = cv2.VideoCapture(0)
while (True):
tic()
ret, img = capture.read()
if ret == True:
#img = cv2.imread('./test4.jpg')
img = cv2.resize(img,(450,344))
img_matlab = img.copy()
img_matlab = cv2.cvtColor(img_matlab, cv2.COLOR_BGR2RGB)
toc()
print("capture--------------------")
#img_matlab = img.copy()
#tmp = img_matlab[:,:,2].copy()
#img_matlab[:,:,2] = img_matlab[:,:,0]
#img_matlab[:,:,0] = tmp
tic()
# check rgb position
boundingboxes, points = detect_face(img_matlab, minsize, pnet_rknn_list, rnet_rknn, onet_rknn, threshold, False, factor)
toc()
tic()
img = drawBoxes(img, boundingboxes)
cv2.imshow('img', img)
c = cv2.waitKey(5) & 0xff
if c==27:
break
toc()
print("imshow--------------------")
#if boundingboxes.shape[0] > 0:
# error.append[imgpath]
#print(error)
i=0
for i in range(9):
pnet_rknn_list[i].release()
rnet_rknn.release()
onet_rknn.release()
cv2.destroyAllWindows()
capture.release()
def quantify_transfer(pb_name, dataset_name, export_name):
ret = 0
print(pb_name, dataset_name, export_name)
rknn = RKNN()
rknn.config(channel_mean_value='',
reorder_channel='',
quantized_dtype='dynamic_fixed_point-8')
print('--> Loading model')
ret = rknn.load_tensorflow(tf_pb=pb_name,
inputs=['test/x'],
outputs=['test/hypothesis'],
input_size_list=[[1, 4]])
if ret != 0:
print('load_tensorflow error')
rknn.release()
return ret
print('done')
print('--> Building model')
rknn.build(do_quantization=True, dataset=dataset_name)
print('done')
# rknn 모델 파일 저장 내보내기
rknn.export_rknn(export_name)
# Release RKNN Context
rknn.release()
return ret
def quanlization_darknet_model(model_path,
weight_path,
dataset_txt,
is_quantization=True,
pre_compile=False):
# 创建RKNN执行对象
rknn = RKNN(verbose=True, verbose_file='verbose.log')
rknn.config(channel_mean_value='0 0 0 255',
reorder_channel='0 1 2',
batch_size=4)
flag = rknn.load_darknet(model=model_path, weight=weight_path)
# 后面不用修改
if flag == 0:
print('load_darknet success')
else:
print('load_darknet failure')
print('done')
# 创建解析pb模型
# do_quantization=False指定不进行量化
# 量化会减小模型的体积和提升运算速度，但是会有精度的丢失
print('--> Building model')
print(os.getcwd())
# flag = rknn.build(do_quantization=False)
if is_quantization == True:
flag = rknn.build(do_quantization=True,
dataset=dataset_txt,
pre_compile=pre_compile)
print('do quantization ')
# 导出保存rknn模型文件
save_rknn_path = model_dir + '/' + model_path.split('/')[-1].split(
'.')[0] + '_quan.rknn'
else:
flag = rknn.build(do_quantization=False)
print('not do quantization')
# 导出保存rknn模型文件
save_rknn_path = model_dir + '/' + model_path.split('/')[-1].split(
'.')[0] + '.rknn'
if flag == 0:
print('build success')
else:
print('build failure')
print('done')
flag = rknn.export_rknn(save_rknn_path)
if flag == 0:
print('export success')
else:
print('export failure')
print('done')
print(save_rknn_path)
# Release RKNN Context
rknn.release()
print('save_rknn_path:', save_rknn_path)
def transfer(pb_path, rknn_name):
# 创建RKNN执行对象
#rknn = RKNN(verbose=True, verbose_file='./mini_XCEPTION_build.log')
rknn = RKNN()
# 配置模型输入，用于NPU对数据输入的预处理
# channel_mean_value='0 0 0 255'，那么模型推理时，将会对RGB数据做如下转换
# (R - 0)/255, (G - 0)/255, (B - 0)/255。推理时，RKNN模型会自动做均值和归一化处理
# reorder_channel=’0 1 2’用于指定是否调整图像通道顺序，设置成0 1 2即按输入的图像通道顺序不做调整
# reorder_channel=’2 1 0’表示交换0和2通道，如果输入是RGB，将会被调整为BGR。如果是BGR将会被调整为RGB
#图像通道顺序不做调整
#rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2')
rknn.config(quantized_dtype='dynamic_fixed_point-8')
# 加载TensorFlow模型
# tf_pb='digital_gesture.pb'指定待转换的TensorFlow模型
# inputs指定模型中的输入节点
# outputs指定模型中输出节点
# input_size_list指定模型输入的大小
print('--> Loading model')
ret = rknn.load_tensorflow(tf_pb=pb_path,
inputs=['input_1'],
outputs=['predictions/Softmax'],
input_size_list=[[INPUT_WIDTH, INPUT_HEIGHT, 1]])
if ret != 0:
print('Load Model failed!')
exit(ret)
print('done')
# 创建解析pb模型
# do_quantization=False指定不进行量化
# 量化会减小模型的体积和提升运算速度，但是会有精度的丢失
print('--> Building model')
ret = rknn.build(do_quantization=False)
if ret != 0:
print('Build Model failed!')
exit(ret)
print('done')
# 导出保存rknn模型文件
print('--> Export RKNN model')
ret = rknn.export_rknn(rknn_name)
if ret != 0:
print('Export Model failed!')
exit(ret)
print('done')
# Release RKNN Context
rknn.release()
def save_rknn(self,
rknnpath,
verbose=True,
verbose_file=None,
input_mean_value='0 0 0 1',
input_channels='0 1 2',
do_quantization=True,
pre_compile=True):
TMP_PB_PATH = './tmp.pb'
from rknn.api import RKNN
self.save_pb(TMP_PB_PATH)
rknn = RKNN(verbose=verbose, verbose_file=verbose_file)
print('--> config model')
rknn.config(channel_mean_value=input_mean_value,
reorder_channel=input_channels)
print('done')
print('--> Loading pb, input shape = ' + str([self.__input_shape]))
ret = rknn.load_tensorflow(tf_pb=TMP_PB_PATH,
inputs=[self.input.op.name],
outputs=[self.output.op.name],
input_size_list=[list(self.__input_shape)])
if ret != 0:
print('Load pb failed! Ret = {}'.format(ret))
exit(ret)
print('done')
print('--> Building model')
ret = rknn.build(do_quantization=do_quantization,
dataset='./rknn_quantization.txt',
pre_compile=pre_compile)
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
print('--> Export RKNN model')
ret = rknn.export_rknn(rknnpath)
if ret != 0:
print('Export rknn failed!')
exit(ret)
print('done')
rknn.release()
def rknn_convert(input_model, output_model, model_input_shape, output_tensor_num, dataset_file, target_platform):
# Create RKNN object
rknn = RKNN()
print('--> config model')
rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2', batch_size=1, target_platform=target_platform)
# Load tensorflow model
print('--> Loading model')
if output_tensor_num == 1:
output_tensor_names = ['predict_conv/BiasAdd']
elif output_tensor_num == 2:
output_tensor_names = ['predict_conv_1/BiasAdd', 'predict_conv_2/BiasAdd']
elif output_tensor_num == 3:
output_tensor_names = ['predict_conv_1/BiasAdd', 'predict_conv_2/BiasAdd', 'predict_conv_3/BiasAdd']
else:
raise ValueError('invalid output tensor number ', output_tensor_num)
ret = rknn.load_tensorflow(tf_pb=input_model,
inputs=['image_input'],
outputs=output_tensor_names,
input_size_list=[model_input_shape+(3,)],
predef_file=None)
#ret = rknn.load_onnx(model=input_model)
if ret != 0:
print('Load failed!')
exit(ret)
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset=dataset_file, pre_compile=True)
if ret != 0:
print('Build failed!')
exit(ret)
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn(output_model)
if ret != 0:
print('Export .rknn failed!')
exit(ret)
# Release RKNN object
rknn.release()
def load_init_model(rknn_path, device_id):
# 创造一个RKNN对象
rknn = RKNN(verbose=True, verbose_file='verbose.log')
rknn.load_rknn(rknn_path)
# init runtime environment
print('--> Init runtime environment')
# 初始化运行环境,主机，比较慢
if device_id == '':
ret = rknn.init_runtime()
print('device_id')
else:
ret = rknn.init_runtime(target='rk1808',
device_id=device_id) # 计算棒被动模式
# 如果ret不等于0
if ret != 0:
# 输出初始化运行环境失败
print('Init runtime environment failed')
# 直接退出
rknn.release()
return None
return rknn
def rknn_convert(input_model, output_model, model_input_shape, dataset_file,
target_platform):
# Create RKNN object
rknn = RKNN()
print('--> config model')
rknn.config(channel_mean_value='0 0 0 255',
reorder_channel='0 1 2',
batch_size=1,
target_platform=target_platform)
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_tensorflow(tf_pb=input_model,
inputs=['image_input'],
outputs=['dense/Softmax'],
input_size_list=[model_input_shape + (3, )],
predef_file=None)
#ret = rknn.load_onnx(model=input_model)
if ret != 0:
print('Load failed!')
exit(ret)
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True,
dataset=dataset_file,
pre_compile=True)
if ret != 0:
print('Build failed!')
exit(ret)
# Export rknn model
print('--> Export RKNN model')
ret = rknn.export_rknn(output_model)
if ret != 0:
print('Export .rknn failed!')
exit(ret)
# Release RKNN object
rknn.release()
def __deal(self, model, post_func):
rknn = RKNN()
ret = rknn.load_rknn(path=model)
# init runtime environment
logger.debug('--> Init runtime environment')
ret = rknn.init_runtime()
if ret != 0:
logger.error('Init runtime environment failed')
exit(ret)
logger.debug('Init done')
r_list = [self.rfd]
w_list = [self.wfd]
e_list = [self.rfd, self.wfd]
while True:
fd_r_list, fd_w_list, fd_e_list = select.select(
r_list, w_list, e_list, select_timeout)
if not (fd_r_list or fd_w_list or fd_e_list):
continue
for rs in fd_r_list:
if rs is self.rfd:
decimg = self.__recieve_frame()
# logger.debug('__recieve_frame: %d' % (len(decimg)))
if decimg is None:
logger.error('decimg is None')
continue
outputs = rknn.inference(inputs=[decimg])
data = post_func(outputs)
for ws in fd_w_list:
if ws is self.wfd:
self.__send_result(data)
for es in fd_e_list:
logger.error("error fd list: %s" % (es))
rknn.release()
logger.debug('__deal finish')
if ret != 0:
print('Export xception.rknn failed!')
exit(ret)
print('done')
# ret = rknn.load_rknn('./xception.rknn')
# Set inputs
img = cv2.imread(IMG_PATH)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime()
#ret = rknn.init_runtime(target='rk1808')
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
# Inference
print('--> Running model')
outputs = rknn.inference(inputs=[img])
#outputs[0].tofile('out.txt', '\n')
show_outputs(outputs)
print('done')
rknn.release()
def main(folder="test"):
folder = folder
files = os.listdir(folder)
for i in range(len(files)):
img = cv2.imread("{}/{}".format(folder, files[i]))
img = (img - 127.5) / 127.5
h, w = img.shape[:2]
print("w, h = ", w, h)
input = cv2.resize(img, (PRESET, PRESET),
interpolation=cv2.INTER_CUBIC)
input = input.reshape(PRESET, PRESET, 3)
input = np.array(input, dtype=np.float32)
rknn = RKNN()
print('--> Loading model')
#rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2')
# Load TensorFlow Model
print('--> Loading model')
rknn.load_tensorflow(tf_pb='pretrained/SR_freeze.pb',
inputs=['ESRGAN_g/Conv2D'],
outputs=['output_image'],
input_size_list=[[PRESET, PRESET, 3]])
print('done')
# Build Model
print('--> Building model')
rknn.build(do_quantization=False)
print('done')
# Export RKNN Model
rknn.export_rknn('./sr_rknn.rknn')
# Direct Load RKNN Model
rknn.load_rknn('./sr_rknn.rknn')
# init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed')
# Inference
print('--> Running model')
output_image = rknn.inference(inputs=[input])
print('complete')
out = np.array(output_image, dtype=np.float64)
print("output_image = ", out.shape)
out = np.squeeze(out)
Y_ = out.reshape(PRESET * 4, PRESET * 4, 3)
Y_ = cv2.resize(Y_, (w * 4, h * 4), interpolation=cv2.INTER_CUBIC)
print("output shape is ", Y_.shape)
#후처리 과정
Y_ = (Y_ + 1) * 127.5
cv2.imwrite("{}/{}_yval.png".format(OUT_DIR, i), Y_)
# Evaluate Perf on Simulator
#rknn.eval_perf()
# Release RKNN Context
rknn.release()
def run(self): #把要执行的代码写到run函数里面 线程在创建后会直接运行run函数
minsize = 20
threshold = [0.8, 0.9, 0.95]
factor = 0.709
pnet_rknn_list = init_pnet()
rnet_rknn = RKNN()
onet_rknn = RKNN()
rnet_rknn.load_rknn('./RNet.rknn')
onet_rknn.load_rknn('./ONet.rknn')
ret = rnet_rknn.init_runtime()
if ret != 0:
#print('Init rnet runtime environment failed')
exit(ret)
ret = onet_rknn.init_runtime()
if ret != 0:
#print('Init onet runtime environment failed')
exit(ret)
sys.stdout = open('/dev/stdout', 'w')
sys.stderr = open('/dev/stderr', 'w')
global proflag
global IMAGE_list
global boundingbox_list
nonfacecount = 0
#wrongimg = 1
while (proflag == 1):
imreadLock.acquire()
img0 = IMAGE_list[0].copy()
img = IMAGE_list[1].copy()
imreadLock.release()
#tic()
score_cmp = compare_image(img0, img)
#print("score_cmp",score_cmp)
#toc()
if score_cmp < 0.98:
#imreadLock.release()
#print("detect face start")
#cv2.imwrite("aa.jpg",img)
tic()
boundingboxes, points = detect_face(img, minsize,
pnet_rknn_list, rnet_rknn,
onet_rknn, threshold,
False, factor)
#print("boundingboxes shape",boundingboxes.shape)
print("total cost")
toc()
if boundingboxes.shape[0] != 0:
if len(boundingbox_list) != 0:
boundingbox_list.clear()
boundingbox_list.append(boundingboxes)
else:
#path = str(wrongimg)+".jpg"
#cv2.imwrite(path,img)
#wrongimg += 1
nonfacecount += 1
if nonfacecount >= 3:
boundingbox_list.clear()
nonfacecount = 0
for i in range(2):
pnet_rknn_list[i].release()
rnet_rknn.release()
onet_rknn.release()
