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mivisionx_inference_analyzer.py
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__author__ = "Kiriti Nagesh Gowda"
__copyright__ = "Copyright 2019 - 2022, AMD MIVisionX"
__credits__ = ["Mike Schmit; Hansel Yang; Lakshmi Kumar;"]
__license__ = "MIT"
__version__ = "1.0"
__maintainer__ = "Kiriti Nagesh Gowda"
__email__ = "[email protected]"
__status__ = "Shipping"
__script_name__ = "MIVisionX Inference Analyzer"
import argparse
import os
import sys
import ctypes
import cv2
import time
import numpy
import numpy as np
from numpy.ctypeslib import ndpointer
from inference_control import inference_control
from PyQt5 import QtWidgets
# global variables
FP16inference = False
verbosePrint = False
labelNames = None
colors =[
(0,153,0), # Top1
(153,153,0), # Top2
(153,76,0), # Top3
(0,128,255), # Top4
(255,102,102), # Top5
];
# AMD Neural Net python wrapper
class AnnAPI:
def __init__(self,library):
self.lib = ctypes.cdll.LoadLibrary(library)
self.annQueryInference = self.lib.annQueryInference
self.annQueryInference.restype = ctypes.c_char_p
self.annQueryInference.argtypes = []
self.annCreateInference = self.lib.annCreateInference
self.annCreateInference.restype = ctypes.c_void_p
self.annCreateInference.argtypes = [ctypes.c_char_p]
self.annReleaseInference = self.lib.annReleaseInference
self.annReleaseInference.restype = ctypes.c_int
self.annReleaseInference.argtypes = [ctypes.c_void_p]
self.annCopyToInferenceInput = self.lib.annCopyToInferenceInput
self.annCopyToInferenceInput.restype = ctypes.c_int
self.annCopyToInferenceInput.argtypes = [ctypes.c_void_p, ndpointer(ctypes.c_float, flags="C_CONTIGUOUS"), ctypes.c_size_t, ctypes.c_bool]
self.annCopyFromInferenceOutput = self.lib.annCopyFromInferenceOutput
self.annCopyFromInferenceOutput.restype = ctypes.c_int
self.annCopyFromInferenceOutput.argtypes = [ctypes.c_void_p, ndpointer(ctypes.c_float, flags="C_CONTIGUOUS"), ctypes.c_size_t]
self.annRunInference = self.lib.annRunInference
self.annRunInference.restype = ctypes.c_int
self.annRunInference.argtypes = [ctypes.c_void_p, ctypes.c_int]
print('OK: AnnAPI found "' + self.annQueryInference().decode("utf-8") + '" as configuration in ' + library)
# classifier definition
class annieObjectWrapper():
def __init__(self, annpythonlib, weightsfile):
self.api = AnnAPI(annpythonlib)
input_info,output_info,empty = self.api.annQueryInference().decode("utf-8").split(';')
input,name,n_i,c_i,h_i,w_i = input_info.split(',')
outputCount = output_info.split(",")
stringcount = len(outputCount)
if stringcount == 6:
output,opName,n_o,c_o,h_o,w_o = output_info.split(',')
else:
output,opName,n_o,c_o= output_info.split(',')
h_o = '1'; w_o = '1';
self.hdl = self.api.annCreateInference(weightsfile.encode('utf-8'))
self.dim = (int(w_i),int(h_i))
self.outputDim = (int(n_o),int(c_o),int(h_o),int(w_o))
def __del__(self):
self.api.annReleaseInference(self.hdl)
def runInference(self, img, out):
# create input.f32 file
img_r = img[:,:,0]
img_g = img[:,:,1]
img_b = img[:,:,2]
img_t = np.concatenate((img_r, img_g, img_b), 0)
# copy input f32 to inference input
status = self.api.annCopyToInferenceInput(self.hdl, np.ascontiguousarray(img_t, dtype=np.float32), (img.shape[0]*img.shape[1]*3*4), 0)
if(status):
print('ERROR: annCopyToInferenceInput Failed ')
# run inference
status = self.api.annRunInference(self.hdl, 1)
if(status):
print('ERROR: annRunInference Failed ')
# copy output f32
status = self.api.annCopyFromInferenceOutput(self.hdl, np.ascontiguousarray(out, dtype=np.float32), out.nbytes)
if(status):
print('ERROR: annCopyFromInferenceOutput Failed ')
return out
def classify(self, img):
# create output.f32 buffer
out_buf = bytearray(self.outputDim[0]*self.outputDim[1]*self.outputDim[2]*self.outputDim[3]*4)
out = np.frombuffer(out_buf, dtype=numpy.float32)
# run inference & receive output
output = self.runInference(img, out)
return output
# process classification output function
def processClassificationOutput(inputImage, modelName, modelOutput):
# post process output file
start = time.time()
softmaxOutput = np.float32(modelOutput)
topIndex = []
topLabels = []
topProb = []
for x in softmaxOutput.argsort()[-5:]:
topIndex.append(x)
topLabels.append(labelNames[x])
topProb.append(softmaxOutput[x])
end = time.time()
if(verbosePrint):
print('%30s' % 'Processed results in ', str((end - start)*1000), 'ms')
# display output
start = time.time()
# initialize the result image
resultImage = np.zeros((250, 525, 3), dtype="uint8")
resultImage.fill(255)
cv2.putText(resultImage, 'MIVisionX Object Classification', (25, 25),cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0), 2)
topK = 1
for i in reversed(range(5)):
txt = topLabels[i]
conf = topProb[i]
txt = 'Top'+str(topK)+':'+txt+' '+str(int(round((conf*100), 0)))+'%'
size = cv2.getTextSize(txt, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
t_height = size[0][1]
textColor = (colors[topK - 1])
cv2.putText(resultImage,txt,(45,t_height+(topK*30+40)),cv2.FONT_HERSHEY_SIMPLEX,0.5,textColor,1)
topK = topK + 1
end = time.time()
if(verbosePrint):
print('%30s' % 'Processed results image in ', str((end - start)*1000), 'ms')
return resultImage, topIndex, topProb
# MIVisionX Classifier
if __name__ == '__main__':
if len(sys.argv) == 1:
app = QtWidgets.QApplication(sys.argv)
panel = inference_control()
app.exec_()
modelFormat = (str)(panel.model_format)
modelName = (str)(panel.model_name)
modelLocation = (str)(panel.model)
modelInputDims = (str)(panel.input_dims)
modelOutputDims = (str)(panel.output_dims)
label = (str)(panel.label)
outputDir = (str)(panel.output)
imageDir = (str)(panel.image)
imageVal = (str)(panel.val)
hierarchy = (str)(panel.hier)
inputAdd = (str)(panel.add)
inputMultiply = (str)(panel.multiply)
fp16 = (str)(panel.fp16)
replaceModel = (str)(panel.replace)
verbose = (str)(panel.verbose)
else:
parser = argparse.ArgumentParser()
parser.add_argument('--model_format', type=str, required=True, help='pre-trained model format, options:caffe/onnx/nnef [required]')
parser.add_argument('--model_name', type=str, required=True, help='model name [required]')
parser.add_argument('--model', type=str, required=True, help='pre_trained model file/folder [required]')
parser.add_argument('--model_input_dims', type=str, required=True, help='c,h,w - channel,height,width [required]')
parser.add_argument('--model_output_dims', type=str, required=True, help='c,h,w - channel,height,width [required]')
parser.add_argument('--label', type=str, required=True, help='labels text file [required]')
parser.add_argument('--output_dir', type=str, required=True, help='output dir to store ADAT results [required]')
parser.add_argument('--image_dir', type=str, required=True, help='image directory for analysis [required]')
parser.add_argument('--image_val', type=str, default='', help='image list with ground truth [optional]')
parser.add_argument('--hierarchy', type=str, default='', help='AMD proprietary hierarchical file [optional]')
parser.add_argument('--add', type=str, default='', help='input preprocessing factor [optional - default:[0,0,0]]')
parser.add_argument('--multiply', type=str, default='', help='input preprocessing factor [optional - default:[1,1,1]]')
parser.add_argument('--fp16', type=str, default='no', help='quantize to FP16 [optional - default:no]')
parser.add_argument('--replace', type=str, default='no', help='replace/overwrite model [optional - default:no]')
parser.add_argument('--verbose', type=str, default='no', help='verbose [optional - default:no]')
args = parser.parse_args()
# get arguments
modelFormat = args.model_format
modelName = args.model_name
modelLocation = args.model
modelInputDims = args.model_input_dims
modelOutputDims = args.model_output_dims
label = args.label
outputDir = args.output_dir
imageDir = args.image_dir
imageVal = args.image_val
hierarchy = args.hierarchy
inputAdd = args.add
inputMultiply = args.multiply
fp16 = args.fp16
replaceModel = args.replace
verbose = args.verbose
# set verbose print
if(verbose != 'no'):
verbosePrint = True
# set fp16 inference turned on/off
if(fp16 != 'no'):
FP16inference = True
# set paths
modelCompilerPath = '/opt/rocm/libexec/mivisionx/model_compiler/python'
ADATPath= '/opt/rocm/libexec/mivisionx/toolkit/amd_data_analysis_toolkit/classification'
setupDir = '~/.mivisionx-inference-analyzer'
analyzerDir = os.path.expanduser(setupDir)
modelDir = analyzerDir+'/'+modelName+'_dir'
nnirDir = modelDir+'/nnir-files'
openvxDir = modelDir+'/openvx-files'
modelBuildDir = modelDir+'/build'
adatOutputDir = os.path.expanduser(outputDir)
inputImageDir = os.path.expanduser(imageDir)
trainedModel = os.path.expanduser(modelLocation)
labelText = os.path.expanduser(label)
hierarchyText = os.path.expanduser(hierarchy)
imageValText = os.path.expanduser(imageVal)
pythonLib = modelBuildDir+'/libannpython.so'
weightsFile = openvxDir+'/weights.bin'
finalImageResultsFile = modelDir+'/imageResultsFile.csv'
# get input & output dims
str_c_i, str_h_i, str_w_i = modelInputDims.split(',')
c_i = int(str_c_i); h_i = int(str_h_i); w_i = int(str_w_i)
str_c_o, str_h_o, str_w_o = modelOutputDims.split(',')
c_o = int(str_c_o); h_o = int(str_h_o); w_o = int(str_w_o)
# input pre-processing values
Ax=[0,0,0]
if(inputAdd != ''):
Ax = [float(item) for item in inputAdd.strip("[]").split(',')]
Mx=[1,1,1]
if(inputMultiply != ''):
Mx = [float(item) for item in inputMultiply.strip("[]").split(',')]
# check pre-trained model
if(not os.path.isfile(trainedModel) and modelFormat != 'nnef' ):
print("\nPre-Trained Model not found, check argument --model\n")
quit()
# check for label file
if (not os.path.isfile(labelText)):
print("\nlabels.txt not found, check argument --label\n")
quit()
else:
fp = open(labelText, 'r')
labelNames = fp.readlines()
labelNames = [line.rstrip('\n') for line in labelNames]
fp.close()
# MIVisionX setup
if(os.path.exists(analyzerDir)):
print("\nMIVisionX Inference Analyzer\n")
# replace old model or throw error
if(replaceModel == 'yes'):
os.system('rm -rf '+modelDir)
elif(os.path.exists(modelDir)):
print("OK: Model exists")
else:
print("\nMIVisionX Inference Analyzer Created\n")
os.system('(cd ; mkdir .mivisionx-inference-analyzer)')
# Setup Text File for Demo
if (not os.path.isfile(analyzerDir + "/setupFile.txt")):
f = open(analyzerDir + "/setupFile.txt", "w")
f.write(modelFormat + ';' + modelName + ';' + modelLocation + ';' + modelInputDims + ';' + modelOutputDims + ';' + label + ';' + outputDir + ';' + imageDir + ';' + imageVal + ';' + hierarchy + ';' + str(Ax).strip('[]').replace(" ","") + ';' + str(Mx).strip('[]').replace(" ","") + ';' + fp16 + ';' + replaceModel + ';' + verbose)
f.close()
else:
count = len(open(analyzerDir + "/setupFile.txt").readlines())
if count < 10:
with open(analyzerDir + "/setupFile.txt", "r") as fin:
data = fin.read().splitlines(True)
modelList = []
for i in range(len(data)):
modelList.append(data[i].split(';')[1])
if modelName not in modelList:
f = open(analyzerDir + "/setupFile.txt", "a")
f.write("\n" + modelFormat + ';' + modelName + ';' + modelLocation + ';' + modelInputDims + ';' + modelOutputDims + ';' + label + ';' + outputDir + ';' + imageDir + ';' + imageVal + ';' + hierarchy + ';' + str(Ax).strip('[]').replace(" ","") + ';' + str(Mx).strip('[]').replace(" ","") + ';' + fp16 + ';' + replaceModel + ';' + verbose)
f.close()
else:
with open(analyzerDir + "/setupFile.txt", "r") as fin:
data = fin.read().splitlines(True)
delModelName = data[0].split(';')[1]
delmodelPath = analyzerDir + '/' + delModelName + '_dir'
if(os.path.exists(delmodelPath)):
os.system('rm -rf ' + delmodelPath)
with open(analyzerDir + "/setupFile.txt", "w") as fout:
fout.writelines(data[1:])
with open(analyzerDir + "/setupFile.txt", "a") as fappend:
fappend.write("\n" + modelFormat + ';' + modelName + ';' + modelLocation + ';' + modelInputDims + ';' + modelOutputDims + ';' + label + ';' + outputDir + ';' + imageDir + ';' + imageVal + ';' + hierarchy + ';' + str(Ax).strip('[]').replace(" ","") + ';' + str(Mx).strip('[]').replace(" ","") + ';' + fp16 + ';' + replaceModel + ';' + verbose)
fappend.close()
# Compile Model and generate python .so files
if (replaceModel == 'yes' or not os.path.exists(modelDir)):
os.system('mkdir '+modelDir)
if(os.path.exists(modelDir)):
# convert to NNIR
if(modelFormat == 'caffe'):
os.system('(cd '+modelDir+'; python3 '+modelCompilerPath+'/caffe_to_nnir.py '+trainedModel+' nnir-files --input-dims 1,'+modelInputDims+' )')
elif(modelFormat == 'onnx'):
os.system('(cd '+modelDir+'; python3 '+modelCompilerPath+'/onnx_to_nnir.py '+trainedModel+' nnir-files --input-dims 1,'+modelInputDims+' )')
elif(modelFormat == 'nnef'):
os.system('(cd '+modelDir+'; python3 '+modelCompilerPath+'/nnef_to_nnir.py '+trainedModel+' nnir-files )')
else:
print("ERROR: Neural Network Format Not supported, use caffe/onnx/nnef in arugment --model_format")
quit()
# convert the model to FP16
if(FP16inference):
os.system('(cd '+modelDir+'; python3 '+modelCompilerPath+'/nnir_update.py --convert-fp16 1 --fuse-ops 1 nnir-files nnir-files)')
print("\nModel Quantized to FP16\n")
# convert to openvx
if(os.path.exists(nnirDir)):
os.system('(cd '+modelDir+'; python3 '+modelCompilerPath+'/nnir_to_openvx.py nnir-files openvx-files)')
else:
print("ERROR: Converting Pre-Trained model to NNIR Failed")
quit()
# build model
if(os.path.exists(openvxDir)):
os.system('mkdir '+modelBuildDir)
else:
print("ERROR: Converting NNIR to OpenVX Failed")
quit()
os.system('(cd '+modelBuildDir+'; cmake ../openvx-files; make; ./anntest ../openvx-files/weights.bin )')
print("\nSUCCESS: Converting Pre-Trained model to MIVisionX Runtime successful\n")
#else:
#print("ERROR: MIVisionX Inference Analyzer Failed")
#quit()
#logos
AMD_Epyc_Black_resize = cv2.imread("../../samples/images/amd-epyc-black-resize.png")
AMD_ROCm_Black_resize = cv2.imread("../../samples/images/rocm-black-resize.png")
# opencv display window
windowInput = "MIVisionX Inference Analyzer - Input Image"
windowResult = "MIVisionX Inference Analyzer - Results"
windowProgress = "MIVisionX Inference Analyzer - Progress"
cv2.namedWindow(windowInput, cv2.WINDOW_GUI_EXPANDED)
cv2.resizeWindow(windowInput, 800, 800)
# create inference classifier
classifier = annieObjectWrapper(pythonLib, weightsFile)
# check for image val text
totalImages = 0;
if(imageVal == ''):
print("\nFlow without Image Validation Text..Creating a file with no ground truths\n")
imageList = os.listdir(inputImageDir)
imageList.sort()
imageValText = os.getcwd() + '/imageValTxt.txt'
fp = open(imageValText , 'w')
for imageFile in imageList:
fp.write(imageFile + " -1" + "\n")
if (not os.path.isfile(imageValText)):
print("\nImage Validation Text not found, check argument --image_val\n")
quit()
else:
fp = open(imageValText, 'r')
imageValidation = fp.readlines()
fp.close()
totalImages = len(imageValidation)
# original std out location
orig_stdout = sys.stdout
# setup results output file
sys.stdout = open(finalImageResultsFile,'w')
print('Image File Name,Ground Truth Label,Output Label 1,Output Label 2,Output Label 3,\
Output Label 4,Output Label 5,Prob 1,Prob 2,Prob 3,Prob 4,Prob 5')
sys.stdout = orig_stdout
# process images
correctTop5 = 0; correctTop1 = 0; wrong = 0; noGroundTruth = 0;
for x in range(totalImages):
imageFileName,grountTruth = imageValidation[x].split(' ')
groundTruthIndex = int(grountTruth)
imageFile = os.path.expanduser(inputImageDir+'/'+imageFileName)
if (not os.path.isfile(imageFile)):
print('Image File - '+imageFile+' not found')
quit()
else:
# read image
start = time.time()
frame = cv2.imread(imageFile)
end = time.time()
if(verbosePrint):
print('%30s' % 'Read Image in ', str((end - start)*1000), 'ms')
# resize and process frame
start = time.time()
resizedFrame = cv2.resize(frame, (w_i,h_i))
RGBframe = cv2.cvtColor(resizedFrame, cv2.COLOR_BGR2RGB)
if(inputAdd != '' or inputMultiply != ''):
pFrame = np.zeros(RGBframe.shape).astype('float32')
for i in range(RGBframe.shape[2]):
pFrame[:,:,i] = RGBframe.copy()[:,:,i] * Mx[i] + Ax[i]
RGBframe = pFrame
end = time.time()
if(verbosePrint):
print('%30s' % 'Input pre-processed in ', str((end - start)*1000), 'ms')
# run inference
start = time.time()
output = classifier.classify(RGBframe)
end = time.time()
if(verbosePrint):
print('%30s' % 'Executed Model in ', str((end - start)*1000), 'ms')
# process output and display
resultImage, topIndex, topProb = processClassificationOutput(resizedFrame, modelName, output)
# copy logos to output window
frameWindow = cv2.resize(frame, (800,800))
frameWindow[0:AMD_ROCm_Black_resize.shape[0], 0:AMD_ROCm_Black_resize.shape[1]] = AMD_ROCm_Black_resize
frameWindow[0:AMD_Epyc_Black_resize.shape[0], 607: 607 + AMD_Epyc_Black_resize.shape[1]] = AMD_Epyc_Black_resize
start = time.time()
cv2.imshow(windowInput, frameWindow)
cv2.imshow(windowResult, resultImage)
end = time.time()
if(verbosePrint):
print('%30s' % 'Processed display in ', str((end - start)*1000), 'ms\n')
# write image results to a file
start = time.time()
sys.stdout = open(finalImageResultsFile,'a')
print(imageFileName+','+str(groundTruthIndex)+','+str(topIndex[4])+
','+str(topIndex[3])+','+str(topIndex[2])+','+str(topIndex[1])+','+str(topIndex[0])+','+str(topProb[4])+
','+str(topProb[3])+','+str(topProb[2])+','+str(topProb[1])+','+str(topProb[0]))
sys.stdout = orig_stdout
end = time.time()
if(verbosePrint):
print('%30s' % 'Image result saved in ', str((end - start)*1000), 'ms')
# create progress image
start = time.time()
progressImage = np.zeros((400, 500, 3), dtype="uint8")
progressImage.fill(255)
cv2.putText(progressImage, 'Inference Analyzer Progress', (25, 25),cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0), 2)
size = cv2.getTextSize(modelName, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
t_width = size[0][0]
t_height = size[0][1]
headerX_start = int(250 -(t_width/2))
cv2.putText(progressImage,modelName,(headerX_start,t_height+(20+40)),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,0,0),2)
txt = 'Processed: '+str(x+1)+' of '+str(totalImages)
size = cv2.getTextSize(txt, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)
cv2.putText(progressImage,txt,(50,t_height+(60+40)),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),1)
# progress bar
cv2.rectangle(progressImage, (50,150), (450,180), (192,192,192), -1)
progressWidth = int(50+ ((400*(x+1))/totalImages))
cv2.rectangle(progressImage, (50,150), (progressWidth,180), (255,204,153), -1)
percentage = int(((x+1)/float(totalImages))*100)
pTxt = 'progress: '+str(percentage)+'%'
cv2.putText(progressImage,pTxt,(175,170),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),1)
if(groundTruthIndex == topIndex[4]):
correctTop1 = correctTop1 + 1
correctTop5 = correctTop5 + 1
elif(groundTruthIndex == topIndex[3] or groundTruthIndex == topIndex[2] or groundTruthIndex == topIndex[1] or groundTruthIndex == topIndex[0]):
correctTop5 = correctTop5 + 1
elif(groundTruthIndex == -1):
noGroundTruth = noGroundTruth + 1
else:
wrong = wrong + 1
# top 1 progress
cv2.rectangle(progressImage, (50,200), (450,230), (192,192,192), -1)
progressWidth = int(50 + ((400*correctTop1)/totalImages))
cv2.rectangle(progressImage, (50,200), (progressWidth,230), (0,153,0), -1)
percentage = int((correctTop1/float(totalImages))*100)
pTxt = 'Top1: '+str(percentage)+'%'
cv2.putText(progressImage,pTxt,(195,220),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),1)
# top 5 progress
cv2.rectangle(progressImage, (50,250), (450,280), (192,192,192), -1)
progressWidth = int(50+ ((400*correctTop5)/totalImages))
cv2.rectangle(progressImage, (50,250), (progressWidth,280), (0,255,0), -1)
percentage = int((correctTop5/float(totalImages))*100)
pTxt = 'Top5: '+str(percentage)+'%'
cv2.putText(progressImage,pTxt,(195,270),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),1)
# wrong progress
cv2.rectangle(progressImage, (50,300), (450,330), (192,192,192), -1)
progressWidth = int(50+ ((400*wrong)/totalImages))
cv2.rectangle(progressImage, (50,300), (progressWidth,330), (0,0,255), -1)
percentage = int((wrong/float(totalImages))*100)
pTxt = 'Mismatch: '+str(percentage)+'%'
cv2.putText(progressImage,pTxt,(175,320),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),1)
# no ground truth progress
cv2.rectangle(progressImage, (50,350), (450,380), (192,192,192), -1)
progressWidth = int(50+ ((400*noGroundTruth)/totalImages))
cv2.rectangle(progressImage, (50,350), (progressWidth,380), (0,255,255), -1)
percentage = int((noGroundTruth/float(totalImages))*100)
pTxt = 'Ground Truth unavailable: '+str(percentage)+'%'
cv2.putText(progressImage,pTxt,(125,370),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),1)
cv2.imshow(windowProgress, progressImage)
end = time.time()
if(verbosePrint):
print('%30s' % 'Progress image created in ', str((end - start)*1000), 'ms')
# exit on ESC
key = cv2.waitKey(2)
if key == 27:
break
# Inference Analyzer Successful
print("\nSUCCESS: Images Inferenced with the Model\n")
cv2.destroyWindow(windowInput)
cv2.destroyWindow(windowResult)
# Create ADAT folder and file
print("\nADAT tool called to create the analysis toolkit\n")
if(not os.path.exists(adatOutputDir)):
os.system('mkdir ' + adatOutputDir)
if(hierarchy == ''):
os.system('python '+ADATPath+'/generate-visualization.py --inference_results '+finalImageResultsFile+
' --image_dir '+inputImageDir+' --label '+labelText+' --model_name '+modelName+' --output_dir '+adatOutputDir+' --output_name '+modelName+'-ADAT')
else:
os.system('python '+ADATPath+'/generate-visualization.py --inference_results '+finalImageResultsFile+
' --image_dir '+inputImageDir+' --label '+labelText+' --hierarchy '+hierarchyText+' --model_name '+modelName+' --output_dir '+adatOutputDir+' --output_name '+modelName+'-ADAT')
print("\nSUCCESS: Image Analysis Toolkit Created\n")
print("Press ESC to exit or close progess window\n")
# Wait to quit
while True:
key = cv2.waitKey(2)
if key == 27:
cv2.destroyAllWindows()
break
if cv2.getWindowProperty(windowProgress,cv2.WND_PROP_VISIBLE) < 1:
break
outputHTMLFile = os.path.expanduser(adatOutputDir+'/'+modelName+'-ADAT-toolKit/index.html')
os.system('firefox '+outputHTMLFile)