updated predict cytosol

predict_cytosol.py

@@ -2,7 +2,7 @@
 from keras.models import load_model
 from tensorflow.keras.metrics import MeanIoU
 
-model = load_model("tutorial118_mitochondria_25epochs.hdf5", compile=False)
+model = load_model("tutorial118_mitochondria_100epochs.hdf5", compile=False)
 
 import tensorflow as tf
 import datetime
@@ -21,107 +21,6 @@
 from tifffile import imread
 from csbdeep.utils import Path, normalize
 
-image_directory = '/training_data/cytosol/images/'
-mask_directory = '/training_data/cytosol/masks/'
-
-X = sorted(glob('training_data/cytosol/images/*.tif'))
-Y = sorted(glob('training_data/cytosol/masks/*.tif'))
-assert all(Path(x).name==Path(y).name for x,y in zip(X,Y))
-
-image_names = sorted(glob('training_data/cytosol/images/*.tif'))
-
-SIZE = 256
-num_images = len(X)
-
-image_names_subset = image_names[0:num_images]
-
-
-images = [cv2.imread(img, cv2.IMREAD_GRAYSCALE | cv2.IMREAD_ANYDEPTH) for img in image_names_subset]
-
-
-image_dataset = np.array(images)
-image_dataset = np.expand_dims(image_dataset, axis = 3)
-
-mask_names = sorted(glob("training_data/cytosol/masks/*.tif"))
-mask_names_subset = mask_names[0:num_images]
-masks = [cv2.imread(mask, 0) for mask in mask_names_subset]
-
-thresholded_masks = []
-
-for mask in mask_names_subset:
-    # Load the image
-    mask_image = cv2.imread(mask, 0)
-
-    # Threshold the image
-    _, thresholded_mask = cv2.threshold(mask_image, 1, 255, cv2.THRESH_BINARY)
-
-    thresholded_masks.append(thresholded_mask)
-
-mask_dataset = np.array(thresholded_masks)
-mask_dataset = np.expand_dims(mask_dataset, axis = 3)
-
-print("Image data shape is: ", image_dataset.shape)
-print("Mask data shape is: ", mask_dataset.shape)
-print("Max pixel value in image is: ", image_dataset.max())
-print("Labels in the mask are : ", np.unique(mask_dataset))
-
-#Normalize images
-image_dataset = image_dataset /image_dataset.max()  #Can also normalize or scale using MinMax scaler
-#Do not normalize masks, just rescale to 0 to 1.
-mask_dataset = mask_dataset /255.  #PIxel values will be 0 or 1
-
-from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(image_dataset, mask_dataset, test_size = 0.20, random_state = 42)
-
-
-
-cv2_mat = cv2.cvtColor(X_test[0,:,:,:])
-
-
-# Assuming X_test is your NumPy array containing a 64-bit floating-point image
-monochrome_image = X_test[0, :, :, 0]  # Extract the first monochrome image
-
-# Convert the 64-bit floating-point image to 8-bit unsigned integer (0 to 255 range)
-monochrome_image_uint8 = np.clip(monochrome_image * 255.0, 0, 255).astype(np.uint8)
-
-# Convert the 8-bit image to a BGR image
-cv2_mat = cv2.cvtColor(monochrome_image_uint8, cv2.COLOR_GRAY2BGR)
-
-# Assuming you have cv2_mat as your image
-cv2.imshow("Image", cv2_mat)
-cv2.waitKey(0)  # Add this to wait for a key press and then close the window
-cv2.destroyAllWindows()  # Add this to close all OpenCV windows when you're done 
-
-
-imread()  
-
-#IOU
-y_pred=model.predict(X_test)
-
-
-float32_array = y_pred[0,:,:,:]
-
-float32_array = (float32_array > 0.5)*1.00
-
-
-# Assuming you have a NumPy array called 'float32_array'
-# Scale the float32 array to the range [0, 255]
-scaled_array = ((float32_array - float32_array.min()) / (float32_array.max() - float32_array.min()) * 255).astype(np.uint8)
-
-# Create a monochrome OpenCV Mat
-monochrome_mat = cv2.cvtColor(scaled_array, cv2.COLOR_GRAY2BGR)
-
-# Convert the BGR image to grayscale
-monochrome_mat = cv2.cvtColor(monochrome_mat, cv2.COLOR_BGR2GRAY)
-
-
-# Assuming you have cv2_mat as your image
-cv2.imshow("Image2", monochrome_mat)
-cv2.waitKey(0)  # Add this to wait for a key press and then close the window
-cv2.destroyAllWindows()  # Add this to close all OpenCV windows when you're done 
-
-
-
 images = cv2.imread('./training_data/images_cytosol/CMYC_MAX_4.tif', cv2.IMREAD_GRAYSCALE | cv2.IMREAD_ANYDEPTH)