Never gonna give you up

scripts/main.py

@@ -127,20 +127,20 @@ unet_model.compile(optimizer='adam', loss=focal_loss(), metrics=['accuracy', 'me
 
 print("model compiled")
 
-train_image_list = os.listdir('./image_resized/image')
-test_image_list = os.listdir('./image_resized/masks')
+train_image_list = os.listdir('../image_resized/image')
+test_image_list = os.listdir('../image_resized/masks')
 
 number_of_image = sys.argv[1]
 if number_of_image == 'all':
-    train_image_list = os.listdir('./image_resized/image')
-    test_image_list = os.listdir('./image_resized/masks')
+    train_image_list = os.listdir('../image_resized/image')
+    test_image_list = os.listdir('../image_resized/masks')
 else:
-    train_image_list = os.listdir('./image_resized/image')[:int(number_of_image)]
-    test_image_list = os.listdir('./image_resized/masks')[:int(number_of_image)]
+    train_image_list = os.listdir('../image_resized/image')[:int(number_of_image)]
+    test_image_list = os.listdir('../image_resized/masks')[:int(number_of_image)]
 
 # np load the images and masks
-train_image_list = [np.load('./image_resized/image/' + image) for image in train_image_list]
-test_image_list = [np.load('./image_resized/masks/' + image) for image in test_image_list]
+train_image_list = [np.load('../image_resized/image/' + image) for image in train_image_list]
+test_image_list = [np.load('../image_resized/masks/' + image) for image in test_image_list]
 
 
 model_saver = keras.callbacks.ModelCheckpoint(model_save_path, verbose=1, save_best_only=False, save_freq='epoch')

scripts/prepare.py

@@ -7,8 +7,8 @@ import shutil
 import sys
 
 # load dataset in AED/training_images
-train_path = 'AED/training_images'
-test_path = 'AED/test_images'
+train_path = '../AED/training_images'
+test_path = '../AED/test_images'
 
 train_image_list = os.listdir(train_path)
 test_image_list = os.listdir(test_path)
@@ -28,8 +28,8 @@ train_masks = []
 test_masks = []
 
 # get the point for each image where elephant are located put in a mask
-df_train_csv = pd.read_csv('AED/training_elephants.csv')
-df_test_csv = pd.read_csv('AED/test_elephants.csv')
+df_train_csv = pd.read_csv('../AED/training_elephants.csv')
+df_test_csv = pd.read_csv('../AED/test_elephants.csv')
 
 def getCoordinatesFromImage(imageId):
     imageId = imageId.split('.')[0]
@@ -90,25 +90,28 @@ def prepare_data(image_list, path):
                 # count the number of white pixel in the mask => keep only the image with elephant
                 nb_white_pixel = np.count_nonzero(mask_crop)
                 if nb_white_pixel > 0:
-                    np.save('./image_resized/image/' + str(image) + "_" + str(cpt), image_crop)
-                    np.save('./image_resized/masks/' + str(image) + "_" + str(cpt), mask_crop)
+                    np.save('../image_resized/image/' + str(image) + "_" + str(cpt), image_crop)
+                    np.save('../image_resized/masks/' + str(image) + "_" + str(cpt), mask_crop)
                     if firstTimeForImage:
                         next_background = True
                         firstTimeForImage = False
                 elif next_background:
-                    np.save('./image_resized/image/' + str(image) + "_" + str(cpt), image_crop)
-                    np.save('./image_resized/masks/' + str(image) + "_" + str(cpt), mask_crop)
+                    np.save('../image_resized/image/' + str(image) + "_" + str(cpt), image_crop)
+                    np.save('../image_resized/masks/' + str(image) + "_" + str(cpt), mask_crop)
                     next_background = False
                 cpt += 1
 
         print("Image " + str(image_counter) + " done")
         image_counter += 1
 
-shutil.rmtree('./image_resized/image')
-shutil.rmtree('./image_resized/masks')
+if os.path.exists('../image_resized/image'):
+    shutil.rmtree('../image_resized/image')
 
-os.mkdir('./image_resized/image')
-os.mkdir('./image_resized/masks')
+if os.path.exists('../image_resized/masks'):
+    shutil.rmtree('../image_resized/masks')
+
+os.mkdir('../image_resized/image')
+os.mkdir('../image_resized/masks')
 
 
 # prepare data

scripts/pres.py

@@ -9,7 +9,7 @@ import cv2
 # plot the image
 
 
-train_path = 'AED/training_images'
+train_path = '../AED/training_images'
 train_image_list = os.listdir(train_path)
 
 for image in train_image_list:

scripts/testModel.py

@@ -16,18 +16,18 @@ def focal_loss(gamma=2., alpha=.25):
 
 
 # model = keras.models.load_model('model_1000')
-model = keras.models.load_model('model_1000', custom_objects={'focal_loss_fixed': focal_loss()})
+model = keras.models.load_model('../model_1000', custom_objects={'focal_loss_fixed': focal_loss()})
 
 # test the model on an image and show the result
 # open the 
 
 # load all image with numpy
-image_list = os.listdir("./image_resized/image")
-mask_list = os.listdir("./image_resized/masks")
+image_list = os.listdir("../image_resized/image")
+mask_list = os.listdir("../image_resized/masks")
 
 for i in range(len(image_list)):
-    image_list[i] = np.load(os.path.join("./image_resized/image", image_list[i]))
-    mask_list[i] = np.load(os.path.join("./image_resized/masks", mask_list[i]))
+    image_list[i] = np.load(os.path.join("../image_resized/image", image_list[i]))
+    mask_list[i] = np.load(os.path.join("../image_resized/masks", mask_list[i]))
 
     mask = model.predict(np.expand_dims(image_list[i], axis=0))