def show_debug_sample(data, label, predictions, data_dim, label_dim, std=1):
img = []
lbl = []
for i in range(len(data)):
prediction_image = from_arr_to_label(predictions[i], label_dim)
label_image = from_arr_to_label(label[i], label_dim)
temp = data[i] * std
min_pixel = np.amin(temp)
temp = temp + min_pixel
data_image = from_arr_to_data(temp, data_dim)
data_image.paste(prediction_image, (24, 24), prediction_image)
data_image = data_image.resize((128, 128))
img.append(data_image)
lbldata_image = from_arr_to_data(temp, data_dim)
lbldata_image.paste(label_image, (24, 24), label_image)
lbldata_image = lbldata_image.resize((128, 128))
lbl.append(lbldata_image)
new_im = Image.new('RGB', (256,len(img)*128))
for j in range(len(img)):
new_im.paste(img[j], (0, j*128))
new_im.paste(lbl[j], (128, j*128))
new_im.show()
def show_debug_sample(data, label, predictions, data_dim, label_dim, std=1):
img = []
lbl = []
for i in range(len(data)):
prediction_image = from_arr_to_label(predictions[i], label_dim)
label_image = from_arr_to_label(label[i], label_dim)
temp = data[i] * std
min_pixel = np.amin(temp)
temp = temp + min_pixel
data_image = from_arr_to_data(temp, data_dim)
data_image.paste(prediction_image, (24, 24), prediction_image)
data_image = data_image.resize((128, 128))
img.append(data_image)
lbldata_image = from_arr_to_data(temp, data_dim)
lbldata_image.paste(label_image, (24, 24), label_image)
lbldata_image = lbldata_image.resize((128, 128))
lbl.append(lbldata_image)
new_im = Image.new('RGB', (256, len(img) * 128))
for j in range(len(img)):
new_im.paste(img[j], (0, j * 128))
new_im.paste(lbl[j], (128, j * 128))
new_im.show()
def debug_input_data(data, label, data_dim, label_dim, delay=0):
label_image = from_arr_to_label(label, label_dim)
data_image= from_arr_to_data(data, data_dim)
data_image.paste(label_image, (24, 24), label_image)
data_image = data_image.resize((128, 128))
data_image.show()
time.sleep(delay)
def debug_input_data(data, label, data_dim, label_dim, delay=0):
label_image = from_arr_to_label(label, label_dim)
data_image = from_arr_to_data(data, data_dim)
data_image.paste(label_image, (24, 24), label_image)
data_image = data_image.resize((128, 128))
data_image.show()
time.sleep(delay)
def show_individual_predictions(self, dataset, predictions, std=1):
print("Show each individual prediction")
images = np.array(dataset[0].eval())
for i in range(images.shape[0]):
min_val = np.amin(images[i])
img =from_arr_to_data((images[i]*std + min_val), 64)
pred = predictions[i]
clip_idx = pred < 0.3
pred[clip_idx] = 0
lab = from_arr_to_label(pred, 16)
img.paste(lab, (24, 24), lab)
img = img.resize((256, 256))
img.show()
user = raw_input('Proceed?')
if user == 'no':
break
