diff --git a/main.ipynb b/main.ipynb index 4e855ef..a9116fd 100644 --- a/main.ipynb +++ b/main.ipynb @@ -12,6 +12,7 @@ "from src.metrics import IOU\n", "from src.consts import EPOCHS, STEPS, SEED, RGB_DIR, JPG_IMAGES, MASK_DIR, FC_DIR\n", "from src.helpers import create_folder\n", + "from src.generators import create_generators\n", "from tensorflow.keras.callbacks import ModelCheckpoint\n", "import tensorflow as tf " ] @@ -171,57 +172,28 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 71, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "['wlfr.jpeg', 'tfxr.jpeg', 'yeaw.jpeg']\n" - ] - }, - { - "ename": "ValueError", - "evalue": "in user code:\n\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1801, in predict_function *\n return step_function(self, iterator)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1790, in step_function **\n outputs = model.distribute_strategy.run(run_step, args=(data,))\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1783, in run_step **\n outputs = model.predict_step(data)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1751, in predict_step\n return self(x, training=False)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n raise e.with_traceback(filtered_tb) from None\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 264, in assert_input_compatibility\n raise ValueError(f'Input {input_index} of layer \"{layer_name}\" is '\n\n ValueError: Input 0 of layer \"model\" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(32, 512, 3)\n", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\main.ipynb Cell 7'\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 7\u001b[0m r_img \u001b[39m=\u001b[39m cv2\u001b[39m.\u001b[39mimread(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(\u001b[39m'\u001b[39m\u001b[39m./images/\u001b[39m\u001b[39m'\u001b[39m, RGB_DIR, img_names[\u001b[39m0\u001b[39m]))\n\u001b[0;32m 8\u001b[0m m_img \u001b[39m=\u001b[39m cv2\u001b[39m.\u001b[39mimread(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(\u001b[39m'\u001b[39m\u001b[39m./images/\u001b[39m\u001b[39m'\u001b[39m, MASK_DIR, img_names[\u001b[39m0\u001b[39m]))\n\u001b[1;32m----> 9\u001b[0m pred \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mpredict(r_img)\n\u001b[0;32m 11\u001b[0m fig,ax\u001b[39m=\u001b[39mplt\u001b[39m.\u001b[39msubplots(\u001b[39m1\u001b[39m,\u001b[39m3\u001b[39m,figsize\u001b[39m=\u001b[39m(\u001b[39m16\u001b[39m,\u001b[39m8\u001b[39m))\n\u001b[0;32m 13\u001b[0m ax[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mset_title(\u001b[39m'\u001b[39m\u001b[39mRGB Image\u001b[39m\u001b[39m'\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py:67\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[0;32m 66\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n\u001b[1;32m---> 67\u001b[0m \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[0;32m 68\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m 69\u001b[0m \u001b[39mdel\u001b[39;00m filtered_tb\n", - "File \u001b[1;32mc:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py:1147\u001b[0m, in \u001b[0;36mfunc_graph_from_py_func..autograph_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 1145\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e: \u001b[39m# pylint:disable=broad-except\u001b[39;00m\n\u001b[0;32m 1146\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(e, \u001b[39m\"\u001b[39m\u001b[39mag_error_metadata\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m-> 1147\u001b[0m \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mag_error_metadata\u001b[39m.\u001b[39mto_exception(e)\n\u001b[0;32m 1148\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 1149\u001b[0m \u001b[39mraise\u001b[39;00m\n", - "\u001b[1;31mValueError\u001b[0m: in user code:\n\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1801, in predict_function *\n return step_function(self, iterator)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1790, in step_function **\n outputs = model.distribute_strategy.run(run_step, args=(data,))\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1783, in run_step **\n outputs = model.predict_step(data)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1751, in predict_step\n return self(x, training=False)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n raise e.with_traceback(filtered_tb) from None\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 264, in assert_input_compatibility\n raise ValueError(f'Input {input_index} of layer \"{layer_name}\" is '\n\n ValueError: Input 0 of layer \"model\" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(32, 512, 3)\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "import random, os\n", - "import cv2\n", - "import numpy as np\n", - "\n", + "from src.utils import plot_predictions_grid\n", + "import os, random\n", "img_names = [random.choice(os.listdir('./images/rgb/img')) for _ in range(3)]\n", - "print(img_names)\n", - "\n", - "r_img = cv2.imread(os.path.join('./images/', RGB_DIR, img_names[0]))\n", - "m_img = cv2.imread(os.path.join('./images/', MASK_DIR, img_names[0]))\n", - "img = np.expand_dims(r_img,axis=0)\n", - "pred = model.predict(r_img)\n", - "fig,ax=plt.subplots(1,3,figsize=(16,8))\n", - "\n", - "ax[0].set_title('RGB Image')\n", - "ax[0].imshow(r_img)\n", - "ax[0].axis('off')\n", - "\n", - "ax[1].set_title('Original Mask')\n", - "ax[1].imshow(m_img)\n", - "ax[1].axis('off')\n", - "\n", - "# ax[2].set_title('Predicted Mask')\n", - "# ax[2].axis('off')\n", - "# ax[2].imshow(tf.keras.preprocessing.image.array_to_img(pred[0]>0.5),cmap='gray')\n", - "\n", - "plt.show()\n" + "loaded_model = tf.keras.models.load_model(\"./\" + model_name, compile=False)\n", + "plot_predictions_grid(img_names, loaded_model)" ] } ], diff --git a/main.py b/main.py index d5f61c4..4d97503 100644 --- a/main.py +++ b/main.py @@ -5,38 +5,48 @@ from src.metrics import IOU from src.consts import EPOCHS, STEPS, SEED from src.generators import create_generators from src.helpers import create_folder +from src.utils import plot_predictions_grid from tensorflow.keras.callbacks import ModelCheckpoint import tensorflow as tf - +import sys +import random +import os if __name__ == "__main__": - model = Unet(num_classes=1).build_model() - - compile_params ={ - 'loss':jaccard_loss(smooth=90), - 'optimizer':'rmsprop', - 'metrics':[IOU] - } - - model.compile(**compile_params) - model_name = "models/unet.h5" - modelcheckpoint = ModelCheckpoint(model_name, - monitor='val_loss', - mode='auto', - verbose=1, - save_best_only=True) + if(len(sys.argv) <= 1): + model = Unet(num_classes=1).build_model() + + compile_params ={ + 'loss':jaccard_loss(smooth=90), + 'optimizer':'rmsprop', + 'metrics':[IOU] + } + + model.compile(**compile_params) + + modelcheckpoint = ModelCheckpoint(model_name, + monitor='val_loss', + mode='auto', + verbose=1, + save_best_only=True) - train_gen = create_generators('training', SEED) - val_gen = create_generators('validation', SEED) + train_gen = create_generators('training', SEED) + val_gen = create_generators('validation', SEED) - history = model.fit_generator(train_gen, - validation_data=val_gen, - epochs=EPOCHS, - steps_per_epoch=STEPS, - validation_steps = STEPS, - shuffle=True, - ) - create_folder('models', '.') - model.save(filepath=model_name) \ No newline at end of file + history = model.fit_generator(train_gen, + validation_data=val_gen, + epochs=EPOCHS, + steps_per_epoch=STEPS, + validation_steps = STEPS, + shuffle=True, + ) + create_folder('models', '.') + model.save(filepath=model_name) + + elif(sys.argv[1] == '--predictions'): + + img_names = [random.choice(os.listdir('./images/rgb/img')) for _ in range(3)] + loaded_model = tf.keras.models.load_model("./" + model_name, compile=False) + plot_predictions_grid(img_names, loaded_model) \ No newline at end of file diff --git a/research.ipynb b/research/research.ipynb similarity index 100% rename from research.ipynb rename to research/research.ipynb diff --git a/unet.ipynb b/research/unet.ipynb similarity index 100% rename from unet.ipynb rename to research/unet.ipynb diff --git a/src/setup.py b/setup.py similarity index 80% rename from src/setup.py rename to setup.py index 33ca1bc..822a8a9 100644 --- a/src/setup.py +++ b/setup.py @@ -5,13 +5,12 @@ import matplotlib.pyplot as plt import time import sys import random -from tqdm import tqdm import cv2 -from consts import SEED, JPG_IMAGES, FC_DIR, FEATURES, MASK_DIR, LABELS, RGB_DIR -from helpers import create_folder, convert_tif_to_jpg, progress_bar -from utils import plot_image_grid +from src.consts import SEED, JPG_IMAGES, FC_DIR, FEATURES, MASK_DIR, LABELS, RGB_DIR +from src.helpers import create_folder, convert_tif_to_jpg, progress_bar +from src.utils import plot_image_grid if __name__ == "__main__": dirs = os.listdir(FEATURES) diff --git a/src/consts.py b/src/consts.py index 56d9c92..33b74ec 100644 --- a/src/consts.py +++ b/src/consts.py @@ -1,6 +1,6 @@ import pandas as pd -FEATURES ='../data/train_features' -LABELS = '../data/train_labels' +FEATURES ='./data/train_features' +LABELS = './data/train_labels' JPG_IMAGES = 'images' RGB_DIR = "rgb/img" FC_DIR = "fc/img" @@ -11,5 +11,5 @@ BATCH = 8 IMG_SIZE = (512,512) SEED = 7 -EPOCHS = 10 +EPOCHS = 5 STEPS = 10 \ No newline at end of file diff --git a/src/generators.py b/src/generators.py index 95339c3..2c586e3 100644 --- a/src/generators.py +++ b/src/generators.py @@ -12,7 +12,7 @@ def create_generators(mode='training', seed=1): train_datagen = ImageDataGenerator(rescale=1 / 255.0, horizontal_flip=True, vertical_flip=True, - validation_split=0.2) + validation_split=0.2) # Provide the same seed and keyword arguments to the fit and flow methods diff --git a/src/utils.py b/src/utils.py index d672497..88d45fc 100644 --- a/src/utils.py +++ b/src/utils.py @@ -1,7 +1,9 @@ import matplotlib.pyplot as plt import cv2 import os -from consts import JPG_IMAGES, RGB_DIR, MASK_DIR +import numpy as np +from src.consts import JPG_IMAGES, RGB_DIR, MASK_DIR +import tensorflow as tf def plot_image_grid(image_names): @@ -23,5 +25,35 @@ def plot_image_grid(image_names): plt.imshow(m_img, cmap=None) #show + plt.tight_layout() + plt.show() + + +def plot_predictions_grid(image_names, model): + #number of img rows + n_row= len(image_names) + + plt.subplots(n_row, 3, figsize=(6, 3*n_row)) + for i, img in enumerate(image_names): + r_img = cv2.imread(os.path.join(JPG_IMAGES, RGB_DIR, img)) + m_img = cv2.imread(os.path.join(JPG_IMAGES, MASK_DIR, img)) + r_img_ext = np.expand_dims(r_img /255.0, axis=0) + pred = model.predict(r_img_ext) + + plt.subplot(n_row, 3, i*3+1) + plt.title(f'RGB - {img}') + plt.axis('off') + plt.imshow(r_img, cmap=None) + + plt.subplot(n_row, 3, i*3+2) + plt.title(f'Mask - {img}') + plt.axis('off') + plt.imshow(m_img, cmap='gray') + + plt.subplot(n_row, 3, i*3+3) + plt.title(f'Predicted Mask - {img}') + plt.axis('off') + plt.imshow(tf.keras.preprocessing.image.array_to_img(pred[0]>0.5), cmap='gray') + plt.tight_layout() plt.show() \ No newline at end of file