def main():
#load/create model
dataFolderPath = os.getcwd() + '/data_test_orient'
modelFolder = dataFolderPath + '/Model'
if hasattr(args, 'load_path') and args.load_path is not None:
print("Loading Model from: " + args.load_path)
fileName = args.load_path
model = e.load_model(fileName)
print("Model Loaded")
else:
print("Creating new model")
model = e.create_model_seg()
#set training parameters
sgd = opt.SGD(lr=0.0001, decay=0.0005, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer='sgd')
topResults, bottomResults, im, label = e.evaluate_top_and_bottom_k(
model=model, dataFolderPath=dataFolderPath, k=1)
#Display image, label and predicted output for the image with highest error
top1Fig = plt.figure(1, figsize=(15, 8))
plt.title('Input Image', loc='left')
plt.title('Actual Label', loc='center')
plt.title('Predicted Label', loc='right')
plt.axis('off')
disp_images(top1Fig, topResults[0, :, :, :], 1, 3, pad=1, cmap=cm.binary)
#save the results figure
resultsFolderName = modelFolder + '/results'
if not os.path.exists(resultsFolderName):
create_results_folder(resultsFolderName)
resultFigFileName = resultsFolderName + '/' + 'top1' + '.png'
plt.savefig(resultFigFileName)
#Display image, label and predicted output for the image with lowest error
bottom1Fig = plt.figure(2, figsize=(15, 8))
plt.title('Input Image', loc='left')
plt.title('Actual Label', loc='center')
plt.title('Predicted Label', loc='right')
plt.axis('off')
disp_images(bottom1Fig,
bottomResults[0, :, :, :],
1,
3,
pad=1,
cmap=cm.binary)
#save the results figure
resultFigFileName = resultsFolderName + '/' + 'bottom1' + '.png'
plt.savefig(resultFigFileName)
plt.show()
def test_conv(fig, bias):
if bias:
weights = np.zeros((3, 3, 1, 5))
weights[:, :, 0, 0] = getSobelMask()
weights[:, :, 0, 1] = getPrewitMask()
weights = [weights, np.ones(5)]
else:
weights = np.zeros(1, 3, 3, 1, 5)
weights[0, :, :, 0, 0] = getSobelMask()
weights[0, :, :, 0, 1] = getPrewitMask()
#weights = getSobelMask();
#print weights.shape;
# create model with single layers 5 filters each of size 3 X 3
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=(20, 20, 1)))
layer = Convolution2D(5,
3,
3,
dim_ordering='tf',
weights=weights,
bias=bias)
model.add(layer)
#create image with a horizontal line
im = np.zeros(400)
im.shape = (1, 20, 20, 1)
im[0, 10, :, 0] = 1
predicted = model.predict(im)
#Display image, and output for each of the five filters
print 'test_conv: Image Shape' + str(im.shape)
print 'test_conv: Predi Shape' + str(predicted.shape)
im1 = im[0, :, :, 0]
pr1 = predicted[0, :, :, 0]
pr2 = predicted[0, :, :, 1]
pr3 = predicted[0, :, :, 2]
pr4 = predicted[0, :, :, 3]
pr5 = predicted[0, :, :, 4]
im1.shape = (20, 20, 1)
pr1.shape = (20, 20, 1)
pr2.shape = (20, 20, 1)
pr3.shape = (20, 20, 1)
pr4.shape = (20, 20, 1)
pr5.shape = (20, 20, 1)
imagesToDisplay = np.concatenate((im1, pr1, pr2, pr3, pr4), axis=2)
disp_images(fig, imagesToDisplay, 2, 3, pad=1, cmap=cm.binary)
return model
from lib_image_display import disp_images; import numpy as np; import matplotlib.pyplot as plt; ##def disp_images(fig): ## grid = ImageGrid(fig, 111, nrows_ncols=(2, 3),axes_pad=0.1,label_mode="1",share_all=True,cbar_location="right",cbar_mode="each",cbar_size="7%",cbar_pad="2%"); ## for i in range(6) : ## imFromImShow = grid[i].imshow(im); ## grid.cbar_axes[i].colorbar( imFromImShow ) im = np.arange(1000); im.shape = 10, 10,10; fig = plt.figure(1, (15, 15)); disp_images(fig,im,2,3); plt.show();
def main():
dataFolder = os.getcwd() + '/data_prev'
modelFolder = dataFolder + '/latestModel'
#load first model
if hasattr(args, 'model1_path') and args.model1_path is not None:
print("Loading Model from: " + args.model1_path)
model1Folder = args.model1_path
fileName = modelFolder + '/Keras_model_weights.h5'
model1 = e.load_model(fileName)
print("Model Loaded")
else:
print("Creating new model")
model = e.create_model_seg()
#set training parameters
sgd = opt.SGD(lr=0.0001, decay=0.0005, momentum=0.9, nesterov=True)
model1.compile(loss='mean_squared_error', optimizer='sgd')
#load second model
if hasattr(args, 'model2_path') and args.model2_path is not None:
print("Loading Model from: " + args.model2_path)
model2Folder = args.model2_path
fileName = model2Folder + '/Keras_model_weights.h5'
model2 = e.load_model(fileName)
print("Model Loaded")
else:
print("Creating new model")
model2 = e.create_model_seg()
#set training parameters
sgd = opt.SGD(lr=0.0001, decay=0.0005, momentum=0.9, nesterov=True)
model2.compile(loss='mean_squared_error', optimizer='sgd')
topResults, bottomResults, im, label = e.evaluate_top_and_bottom_k(
model1, dataFolder)
topResults2, bottomResults2, im2, label2 = e.evaluate_top_and_bottom_k(
model2, dataFolder)
#Display image, label and predicted output for the image with highest error
imagesToshowTop = np.zeros((400, 200, 3))
imagesToshowTop[:, :, 0] = topResults[:, :, 1]
imagesToshowTop[:, :, 1] = topResults[:, :, 2]
imagesToshowTop[:, :, 2] = topResults2[:, :, 2]
top1Fig = plt.figure(1, figsize=(15, 8))
plt.title('Label', loc='left')
plt.title('Model-1 Result', loc='center')
plt.title('Model-2 Result', loc='right')
plt.axis('off')
disp_images(top1Fig, imagesToshowTop, 1, 3, pad=1, cmap=cm.binary)
#save the results figure
resultsFolderName = dataFolder + '/results'
if not os.path.exists(resultsFolderName):
create_results_folder(resultsFolderName)
resultFigFileName = resultsFolderName + '/' + 'top1' + '.png'
plt.savefig(resultFigFileName)
#Display image, label and predicted output for the image with lowest error
imagesToshowBottom = np.zeros((400, 200, 3))
imagesToshowBottom[:, :, 0] = bottomResults[:, :, 1]
imagesToshowBottom[:, :, 1] = bottomResults[:, :, 2]
imagesToshowBottom[:, :, 2] = bottomResults2[:, :, 2]
bottom1Fig = plt.figure(2, figsize=(15, 8))
plt.title('Label', loc='left')
plt.title('Model-1 Result', loc='center')
plt.title('Model-2 Result', loc='right')
plt.axis('off')
disp_images(bottom1Fig, imagesToshowBottom, 1, 3, pad=1, cmap=cm.binary)
#save the results figure
resultFigFileName = resultsFolderName + '/' + 'bottom1' + '.png'
plt.savefig(resultFigFileName)
plt.show()