import numpy as np import matplotlib.pyplot as plt from py.main import imageLoader fileName = "./trainingData/cy5.bin_img3_1/13227_41_41_1_cy5.bin.npy" allImages = imageLoader(fileName) allImages.shape plt.figure() plt.imshow(allImages[19]) plt.colorbar() plt.grid(False) plt.show()
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.regularizers import l1
import matplotlib.pyplot as plt
from skimage import transform
import random
print("Num GPUs Available: ",
len(tf.config.experimental.list_physical_devices('GPU')))
modelName = 'cy5_2'
imageFileName = "../trainingData/cy5.bin_img3_1/19076_41_41_1_cy5.bin.npy"
labelFileName = "../trainingData/cy5.bin_img3_1/cy5.bin_19076_label.csv"
#traceFileName = "./trainingData/cell_types_R3J/R3J_7238.csv"
# Prepare the image for the LSTM
image = imageLoader(imageFileName)
imageIndex = image.shape[0]
imageIndex = np.random.permutation(range(imageIndex))
imageFeature = image[imageIndex, ...]
# Prepare the labels
labels = pd.read_csv(labelFileName, index_col=0)
labels = labels.iloc[imageIndex, ]
#labels.iloc[:,0] = labels.iloc[:,0] - 1
#labels = labels.iloc[:,0].astype('category')
labels = np.asarray(labels, dtype='uint8')
np.asmatrix(np.unique(labels, return_counts=True))
uniqueLabels = np.unique(labels)
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.regularizers import l1
print("Num GPUs Available: ",
len(tf.config.experimental.list_physical_devices('GPU')))
modelName = 'label'
imageFileName1 = "../trainingData/cell_types_img1_123/7238_41_41_3_cell_types.npy"
labelFileName1 = '../trainingData/cell_types_label.csv'
#imageFileName2 = "../trainingData/binaryData_gfp.bin_cy5.bin_img2_123/8127_41_41_3_gfp.bin_cy5.bin.npy"
#labelFileName2 = "../trainingData/binaryData_gfp.bin_cy5.bin_img2_123/gfp.bin_cy5.bin_8127_label.csv"
#traceFileName = "./trainingData/cell_types_R3J/R3J_7238.csv"
# Prepare the image for the LSTM
image1 = imageLoader(imageFileName1)
imageIndex1 = image1.shape[0]
imageIndex1 = np.random.permutation(range(imageIndex1))
imageFeature = image1[imageIndex1, ...]
# image2 = imageLoader(imageFileName2)
# imageIndex2 = image2.shape[0]
# imageIndex2 = np.random.permutation(range(imageIndex2))
# imageFeature2 = image2[imageIndex2,...]
#imageFeature = np.concatenate((imageFeature1, imageFeature2), axis = 0)
# Prepare the labels
labels = pd.read_csv(labelFileName1, index_col=0)
labels = labels.iloc[imageIndex1, ]
labels.iloc[:, 0] = labels.iloc[:, 0] - 1