rknn.inference执行问题 - Toybrick

# To use Inference Engine backend, specify location of plugins:
# export LD_LIBRARY_PATH=/opt/intel/deeplearning_deploymenttoolkit/deployment_tools/external/mklml_lnx/lib:$LD_LIBRARY_PATH
import cv2 as cv
import numpy as np
import argparse
import compileall
import time
from rknn.api import RKNN
import pickle
compileall.compile_dir(r'./')
parser = argparse.ArgumentParser()
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--thr', default=0.2, type=float, help='Threshold value for pose parts heat map')
parser.add_argument('--width', default=368, type=int, help='Resize input to specific width.')
parser.add_argument('--height', default=368, type=int, help='Resize input to specific height.')
args = parser.parse_args()
BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
"RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "REye": 14,
"LEye": 15, "REar": 16, "LEar": 17, "Background": 18 }
POSE_PAIRS = [ ["Neck", "RShoulder"], ["Neck", "LShoulder"], ["RShoulder", "RElbow"],
["RElbow", "RWrist"], ["LShoulder", "LElbow"], ["LElbow", "LWrist"],
["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"],
["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"],
["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"] ]
inWidth = args.width
inHeight = args.height
starttime = time.time()
#=====================================================================================
# Create RKNN object
rknn = RKNN()
# Config for Model Input PreProcess
rknn.config(channel_mean_value='128 128 128 128', reorder_channel='0 1 2')
#rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2')
#=====================================================================================
ret = rknn.load_rknn(path='./graph_opt_test.rknn')
#net = cv.dnn.readNetFromTensorflow("graph_opt.pb")
print('--> Init runtime environment')
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
#=====================================================================================
frame = cv.imread('./apink1_crop.jpg')
vid_writer = None
print("[INFO] iamge processed, elapsed time: %f" % (time.time() - starttime))
frameWidth = frame.shape[1]
frameHeight = frame.shape[0]
outlist = rknn.inference(inputs=[cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), (127.5, 127.5, 127.5), swapRB=True, crop=False)],data_type='float')
print("[INFO] one frame processed, elapsed time: %f" % (time.time() - starttime))
#out = out[:, :19, :, :] # MobileNet output [1, 57, -1, -1], we only need the first 19 elements
out1 = outlist[0]
out = out1.reshape(1,57,46,46)
out = out[:, :19, :, :]
data = out
print(data.shape)
print(type(data))
#=====================================================================================
print("[INFO] one frame processed, elapsed time: %f" % (time.time() - starttime))
fw = open('dataFiletest1.txt','wb')
pickle.dump(outlist[0],fw)
fw.close