predict_masks_and_save_wings.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7845,"status":"ok","timestamp":1698953744657,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"fCTSbwqbD8L-","outputId":"4f687bc2-df0c-4b3c-9116-1a50439093c7"},"outputs":[{"name":"stderr","output_type":"stream","text":["2023-12-05 10:09:34.625069: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n","2023-12-05 10:09:34.625138: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n","2023-12-05 10:09:34.808476: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","2023-12-05 10:09:35.084524: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n","To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n","2023-12-05 10:09:41.623191: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"]}],"source":["from keras.utils import normalize\n","import os\n","import glob\n","import cv2\n","import numpy as np\n","from matplotlib import pyplot as plt\n","import pandas as pd\n","\n","# from google.colab import drive\n","# drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":14,"metadata":{"executionInfo":{"elapsed":136,"status":"ok","timestamp":1698954080929,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"MG3yBCMROuw2"},"outputs":[],"source":["DATASET_PATH = '/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/*'\n","\n","# MODEL_SAVE_PATH = '/content/drive/MyDrive/annotation_data/unet_butterflies_256_256.hdf5'\n","# MODEL_SAVE_PATH = '/content/drive/MyDrive/annotation_data/unet_butterflies_256_256_damaged_classes.hdf5'\n","# MODEL_SAVE_PATH = '/content/drive/MyDrive/annotation_data/unet_models/unet_butterflies_256_256_corrected_orientation_cat_focal_loss.hdf5'\n","# MODEL_SAVE_PATH = '/content/drive/MyDrive/annotation_data/unet_models/unet_butterflies_256_256_corrected_orientation_data_augmentation_class_weights_cat_cross_entropy_loss_RGB.hdf5'\n","MODEL_SAVE_PATH = '/content/drive/MyDrive/annotation_data/unet_models/unet_butterflies_256_256_corrected_orientation_data_augmentation_class_weights_cat_cross_entropy_loss_grayscale.hdf5'\n","\n","SIZE_X = 256\n","SIZE_Y = 256\n","\n","# n_classes=16 #Number of classes for segmentation - damaged version\n","n_classes=11 #Number of classes for segmentation\n","\n","# IMG_CHANNELS = 3 #for color images\n","IMG_CHANNELS = 1 #for grayscale\n","\n","MODEL_LOSS = 'categorical_crossentropy'\n","#MODEL_LOSS = 'categorical_focal_crossentropy'\n","\n","GRAY_SCALE = True\n","# GRAY_SCALE = False"]},{"cell_type":"markdown","metadata":{"id":"p0i6cv9QHcZT"},"source":["## Load in Dataset Images"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23425,"status":"ok","timestamp":1698953813789,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"YMG_3uJ-Hfha","outputId":"ce652234-1d99-45b6-d61c-f7e0547f203e"},"outputs":[{"name":"stdout","output_type":"stream","text":["/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. amalfreda\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius melpomene ssp. malleti\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. cyrbia\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. dignus\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius melpomene ssp. rosina\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. phyllis\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. reductimacula\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. notabilis\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius melpomene ssp. thelxiopeia\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. hydara\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius melpomene ssp. aglaope\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. venus\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius melpomene ssp. melpomene\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius melpomene ssp. plesseni\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius melpomene ssp. ecuadorensis\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. demophoon\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. chestertonii\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. lativitta\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius melpomene ssp. vulcanus\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. petiverana\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. etylus\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato ssp. erato\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius melpomene\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius melpomene ssp. meriana\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius melpomene ssp. amaryllis\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius melpomene ssp. cythera\n","/content/drive/MyDrive/annotation_data/jiggins/jiggins_data_256_256/Heliconius erato\n"]}],"source":["#file types\n","file_extensions = [\"png\"] #[\"jpg\", \"JPG\", \"jpeg\", \"png\"]\n","\n","#Get training images and mask paths then sort\n","image_filepaths = []\n","for directory_path in glob.glob(DATASET_PATH):\n","  print(directory_path)\n","  for ext in file_extensions:\n","    for img_path in glob.glob(os.path.join(directory_path, f\"*.{ext}\")):\n","      image_filepaths.append(img_path)\n","\n","#sort image and mask fps to ensure we have the same order to index\n","image_filepaths.sort()\n","\n","#get actual masks and images\n","dataset_images = []\n","\n","for img_path in image_filepaths:\n","  if GRAY_SCALE:\n","    img = cv2.imread(img_path, 0)\n","  else:\n","    img = cv2.imread(img_path)\n","  dataset_images.append(img)\n","\n","#Convert list to array for machine learning processing\n","dataset_images = np.array(dataset_images)"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":127,"status":"ok","timestamp":1698953867131,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"sS830TgcPWXi","outputId":"c04b21cd-ec86-458b-fb3a-7db73a9843bf"},"outputs":[{"data":{"text/plain":["4686"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["len(image_filepaths)"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":179,"status":"ok","timestamp":1698953868644,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"sABc2JEZmShm","outputId":"9fb7bbf9-cef4-45a7-a22b-86e44281a9da"},"outputs":[{"data":{"text/plain":["4686"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["len(dataset_images)"]},{"cell_type":"markdown","metadata":{"id":"8OOVNCXBRGN7"},"source":["#Load in our model"]},{"cell_type":"code","execution_count":15,"metadata":{"executionInfo":{"elapsed":113,"status":"ok","timestamp":1698954103461,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"7HI17FjWNAhf"},"outputs":[],"source":["# https://youtu.be/XyX5HNuv-xE\n","# https://youtu.be/q-p8v1Bxvac\n","\"\"\"\n","Standard Unet\n","Model not compiled here, instead will be done externally to make it\n","easy to test various loss functions and optimizers.\n","\"\"\"\n","\n","from keras.models import Model\n","from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda\n","\n","def multi_unet_model(n_classes=4, IMG_HEIGHT=256, IMG_WIDTH=256, IMG_CHANNELS=1):\n","  #comment\n","  # Build the model\n","  inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))\n","  #s = Lambda(lambda x: x / 255)(inputs)   #No need for this if we normalize our inputs beforehand\n","  s = inputs\n","\n","  #Contraction path\n","  c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s)\n","  c1 = Dropout(0.1)(c1)\n","  c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)\n","  p1 = MaxPooling2D((2, 2))(c1)\n","\n","  c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)\n","  c2 = Dropout(0.1)(c2)\n","  c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)\n","  p2 = MaxPooling2D((2, 2))(c2)\n","\n","  c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)\n","  c3 = Dropout(0.2)(c3)\n","  c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)\n","  p3 = MaxPooling2D((2, 2))(c3)\n","\n","  c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)\n","  c4 = Dropout(0.2)(c4)\n","  c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)\n","  p4 = MaxPooling2D(pool_size=(2, 2))(c4)\n","\n","  c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)\n","  c5 = Dropout(0.3)(c5)\n","  c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)\n","\n","  #Expansive path\n","  u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)\n","  u6 = concatenate([u6, c4])\n","  c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)\n","  c6 = Dropout(0.2)(c6)\n","  c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)\n","\n","  u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)\n","  u7 = concatenate([u7, c3])\n","  c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)\n","  c7 = Dropout(0.2)(c7)\n","  c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)\n","\n","  u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)\n","  u8 = concatenate([u8, c2])\n","  c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)\n","  c8 = Dropout(0.1)(c8)\n","  c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)\n","\n","  u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)\n","  u9 = concatenate([u9, c1], axis=3)\n","  c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)\n","  c9 = Dropout(0.1)(c9)\n","  c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)\n","\n","  outputs = Conv2D(n_classes, (1, 1), activation='softmax')(c9)\n","\n","  model = Model(inputs=[inputs], outputs=[outputs])\n","\n","  #NOTE: Compile the model in the main program to make it easy to test with various loss functions\n","  #model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n","\n","  #model.summary()\n","\n","  return model\n","\n","def get_model():\n","    return multi_unet_model(n_classes=n_classes, IMG_HEIGHT=256, IMG_WIDTH=256, IMG_CHANNELS=IMG_CHANNELS)"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1433,"status":"ok","timestamp":1698954108517,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"i7u7ZyYmRH8E","outputId":"d49614df-6433-490e-f87a-b0ddcf1add5a"},"outputs":[{"name":"stdout","output_type":"stream","text":["/content/drive/MyDrive/annotation_data/unet_models/unet_butterflies_256_256_corrected_orientation_data_augmentation_class_weights_cat_cross_entropy_loss_grayscale.hdf5\n"]}],"source":["# Load in trained model\n","print(MODEL_SAVE_PATH)\n","model_save_path = MODEL_SAVE_PATH\n","model = get_model()\n","model.compile(optimizer='adam', loss= MODEL_LOSS, metrics=['accuracy'])\n","model.load_weights(model_save_path)"]},{"cell_type":"markdown","metadata":{"id":"L3yA1xa8Hfr2"},"source":["# Preprocess Images"]},{"cell_type":"code","execution_count":17,"metadata":{"executionInfo":{"elapsed":124,"status":"ok","timestamp":1698954112449,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"sLPBtxXLO8Eq"},"outputs":[],"source":["classes = {\n","  0: 'background',\n","  1: 'generic',\n","  2: 'right_forewing',\n","  3: 'left_forewing',\n","  4: 'right_hindwing',\n","  5: 'left_hindwing',\n","  6: 'ruler',\n","  7: 'white_balance',\n","  8: 'label',\n","  9: 'color_card',\n","  10: 'body'}"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1488,"status":"ok","timestamp":1698954118230,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"CLlI13arNuu5","outputId":"c12b12da-c8a8-41bd-d52d-92cd63840b30"},"outputs":[{"name":"stdout","output_type":"stream","text":["expanding dimensions for images\n"]}],"source":["#preprocess all images so we can use our model on them\n","if IMG_CHANNELS == 1:\n","  print('expanding dimensions for images')\n","  normalized_dataset_images = np.expand_dims(dataset_images, axis=3)\n","  normalized_dataset_images = normalize(normalized_dataset_images, axis=1)\n","else:\n","  normalized_dataset_images = normalize(dataset_images, axis=1)"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":151,"status":"ok","timestamp":1698954121313,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"-dbQbD_HhEwt","outputId":"0a5721ab-1c86-4dab-9d75-e5902db5b2e4"},"outputs":[{"data":{"text/plain":["(4686, 256, 256, 1)"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["normalized_dataset_images.shape"]},{"cell_type":"markdown","metadata":{"id":"ERIQQHeRHiSi"},"source":["## Predict Masks, and Extract and Save Wings for Each Image"]},{"cell_type":"code","execution_count":20,"metadata":{"executionInfo":{"elapsed":115,"status":"ok","timestamp":1698954125884,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"PNh7zwtCPNYw"},"outputs":[],"source":["def save_segmented_wings(test_img, predicted_img, cropped_dim=(256, 256)):\n","  cropped_wings = dict()\n","  cropped_wings_resized = dict()\n","\n","  #only saving non-damaged wings when you predict with the model trained on damaged classes\n","  for wing_class in [2,3,4,5]:\n","    img = test_img[:,:, 0]\n","    mask = np.asarray(predicted_img==wing_class, dtype=int) #0s and 1s\n","\n","    # cropped = img * mask\n","    # plt.imshow(cropped, cmap='gray')\n","    # plt.show()\n","\n","    y_coords = []\n","    x_coords = []\n","    for y in range(0, mask.shape[0]):\n","      for x in range(0, mask.shape[1]):\n","        if mask[y,x]==1:\n","          x_coords.append(x)\n","          y_coords.append(y)\n","\n","    #our mask is empty - therefore no existing mask for that wing\n","    if len(x_coords) == 0 and len(y_coords) == 0:\n","      continue\n","\n","    x_coords.sort()\n","    y_coords.sort()\n","\n","    miny = y_coords[0]\n","    maxy = y_coords[-1]\n","    minx = x_coords[0]\n","    maxx= x_coords[-1]\n","\n","    #get boundaries with some extra room\n","    if y_coords[0] > 10 and x_coords[0] > 10:\n","      miny -= 10\n","      maxy += 10\n","      minx -= 10\n","      maxx += 10\n","\n","    cropped_result = img[miny:maxy, minx:maxx]\n","    cropped_result_resized = cv2.resize(cropped_result, cropped_dim, interpolation=cv2.INTER_CUBIC)\n","\n","    cropped_wings[wing_class] = cropped_result #not resizing yet\n","    cropped_wings_resized[wing_class] = cropped_result_resized\n","\n","  return cropped_wings, cropped_wings_resized\n"]},{"cell_type":"code","execution_count":21,"metadata":{"executionInfo":{"elapsed":201,"status":"ok","timestamp":1698954132414,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"CMw7IkFoRCl1"},"outputs":[],"source":["classes = {0: 'background',\n","  1: 'generic',\n","  2: 'right_forewing',\n","  3: 'left_forewing',\n","  4: 'right_hindwing',\n","  5: 'left_hindwing',\n","  6: 'ruler',\n","  7: 'white_balance',\n","  8: 'label',\n","  9: 'color_card',\n","  10: 'body'}\n","\n","dataset_segmented = pd.DataFrame(columns = ['image', 'background',\n","                                            'generic', 'right_forewing',\n","                                            'left_forewing', 'right_hindwing',\n","                                            'left_hindwing', 'ruler', 'white_balance',\n","                                            'label', 'color_card', 'body', 'damaged'])"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":4,"status":"aborted","timestamp":1698701230482,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"VMmVvp1cawe_"},"outputs":[],"source":["# folder = '/content/drive/MyDrive/annotation_data/jiggins_masks/jiggins_masks_data_256_256/Heliconius erato ssp. lativitta'\n","# os.makedirs(folder, exist_ok=True)"]},{"cell_type":"markdown","metadata":{"id":"XxlHL02yvJuO"},"source":["### Predict masks and save forewings with model NOT trained on damaged classes"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1975723,"status":"ok","timestamp":1698956120808,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"oq-aR3fsHiDM","outputId":"a4d66065-929c-4ee5-af0c-5a18e9da1b6f"},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 1s 743ms/step\n","1/1 [==============================] - 0s 200ms/step\n","1/1 [==============================] - 0s 194ms/step\n","1/1 [==============================] - 0s 207ms/step\n","1/1 [==============================] - 0s 117ms/step\n","1/1 [==============================] - 0s 106ms/step\n","1/1 [==============================] - 0s 104ms/step\n","1/1 [==============================] - 0s 108ms/step\n","1/1 [==============================] - 0s 115ms/step\n","1/1 [==============================] - 0s 105ms/step\n","1/1 [==============================] - 0s 121ms/step\n","1/1 [==============================] - 0s 110ms/step\n","1/1 [==============================] - 0s 117ms/step\n","1/1 [==============================] - 0s 106ms/step\n","1/1 [==============================] - 0s 108ms/step\n","1/1 [==============================] - 0s 104ms/step\n","1/1 [==============================] - 0s 119ms/step\n","1/1 [==============================] - 0s 116ms/step\n","1/1 [==============================] - 0s 106ms/step\n","1/1 [==============================] - 0s 106ms/step\n","1/1 [==============================] - 0s 108ms/step\n","1/1 [==============================] - 0s 108ms/step\n","1/1 [==============================] - 0s 171ms/step\n","1/1 [==============================] - 0s 163ms/step\n","1/1 [==============================] - 0s 166ms/step\n","1/1 [==============================] - 0s 153ms/step\n","1/1 [==============================] - 0s 171ms/step\n","1/1 [==============================] - 0s 109ms/step\n","1/1 [==============================] - 0s 109ms/step\n","1/1 [==============================] - 0s 122ms/step\n","1/1 [==============================] - 0s 106ms/step\n","1/1 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fp.replace('jiggins', 'jiggins_masks')\n","  mask_path = mask_path.replace('.png', '_mask.png')\n","\n","  mask_fn = \"/\" + mask_path.split('/')[-1]\n","  mask_folder = mask_path.replace(mask_fn, \"\")\n","  os.makedirs(mask_folder, exist_ok=True)\n","\n","  plt.imsave(mask_path, predicted_img)\n","\n","  #enter relevant segmentation data for the image\n","  classes_in_image = np.unique(predicted_img)\n","  classes_not_in_image = set(classes.keys()) ^ set(classes_in_image)\n","  dataset_segmented.loc[i, 'image'] = fp\n","\n","  for val in classes_in_image:\n","    pred_class = classes[val]\n","    dataset_segmented.loc[i, pred_class] = 1 #class exists in segmentation\n","\n","  for not_pred_val in classes_not_in_image:\n","    not_pred_class = classes[not_pred_val]\n","    dataset_segmented.loc[i, not_pred_class] = 0 #class does not exist in segmentation\n","\n","  #crop + extract any existing wings\n","  cropped_wings, cropped_wings_resized = save_segmented_wings(test_img, predicted_img)\n","\n","  #save each individual wing with a traceable name to the source image (ex. erato_0001_right_forewing.png)\n","  for wing_idx in cropped_wings.keys():\n","    cropped_wing = cropped_wings[wing_idx]\n","    cropped_wing_resized = cropped_wings_resized[wing_idx]\n","\n","    cropped_wing_path = fp.replace('/jiggins/', '/cropped_wings/jiggins/').replace('.png', f'_wing_{wing_idx}.png')\n","    resized_cropped_wing_path = fp.replace('/jiggins/', '/cropped_wings_256_256/jiggins/').replace('.png', f'_wing_{wing_idx}.png')\n","\n","    n = \"/\" + cropped_wing_path.split('/')[-1]\n","    cropped_wing_folder = cropped_wing_path.replace(n, \"\")\n","    os.makedirs(cropped_wing_folder, exist_ok=True)\n","\n","    r = \"/\" + resized_cropped_wing_path.split('/')[-1]\n","    resized_cropped_wing_folder = resized_cropped_wing_path.replace(r, \"\")\n","    os.makedirs(resized_cropped_wing_folder, exist_ok=True)\n","\n","    try:\n","      plt.imsave(cropped_wing_path, cropped_wing)\n","      plt.imsave(resized_cropped_wing_path, cropped_wing_resized)\n","    except FileNotFoundError:\n","      errors.append(cropped_wing_path)\n","\n","    i += 1"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":442},"executionInfo":{"elapsed":148,"status":"ok","timestamp":1698956401451,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"4wCwVSUAn9R4","outputId":"aaad9f5c-5e26-4ded-a615-c01f746526ba"},"outputs":[{"name":"stdout","output_type":"stream","text":["Errors saving:  []\n"]},{"data":{"text/html":["\n","  <div id=\"df-f725072f-d0a6-4353-8475-ad646e4a26b8\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n"," 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<td>/content/drive/MyDrive/annotation_data/jiggins...</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>/content/drive/MyDrive/annotation_data/jiggins...</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>/content/drive/MyDrive/annotation_data/jiggins...</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      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<td>/content/drive/MyDrive/annotation_data/jiggins...</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>18380</th>\n","      <td>/content/drive/MyDrive/annotation_data/jiggins...</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>4686 rows × 13 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f725072f-d0a6-4353-8475-ad646e4a26b8')\"\n","            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1             1     1   \n","18376              1             1              1             1     1   \n","18380              1             1              1             1     1   \n","\n","      white_balance label color_card body damaged  \n","0                 0     1          0    0     NaN  \n","4                 0     1          0    0     NaN  \n","8                 0     1          0    0     NaN  \n","12                0     1          0    0     NaN  \n","16                0     1          0    0     NaN  \n","...             ...   ...        ...  ...     ...  \n","18364             0     1          0    0     NaN  \n","18368             0     1          0    0     NaN  \n","18372             0     1          0    0     NaN  \n","18376             0     1          0    0     NaN  \n","18380             0     1          0    0     NaN  \n","\n","[4686 rows x 13 columns]"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["print('Errors saving: ', errors)\n","dataset_segmented"]},{"cell_type":"code","execution_count":24,"metadata":{"executionInfo":{"elapsed":163,"status":"ok","timestamp":1698956403544,"user":{"displayName":"Michelle Ramirez","userId":"09308492985362697976"},"user_tz":240},"id":"oH2IaMJWZOjI"},"outputs":[],"source":["dataset_segmented.to_csv('/content/drive/MyDrive/annotation_data/jiggins_masks/segmented_jiggins_dataset_metadata.csv', index=False)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualize Masks"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[],"source":["def load_dataset_images(dataset_path):\n","    '''Load in actual images from filepaths from all subfolders in the provided dataset_path'''\n","    #file types\n","    file_extensions = [\"png\"] #[\"jpg\", \"JPG\", \"jpeg\", \"png\"]\n","\n","    #Get training image paths then sort\n","    image_filepaths = []\n","    for directory_path in glob.glob(dataset_path):\n","        if os.path.isfile(directory_path):\n","            image_filepaths.append(directory_path)\n","        elif os.path.isdir(directory_path):\n","            for ext in file_extensions:\n","                for img_path in glob.glob(os.path.join(directory_path, f\"*.{ext}\")):\n","                    image_filepaths.append(img_path)\n","\n","    #sort image and mask fps to ensure we have the same order to index\n","    image_filepaths.sort()\n","\n","    #get actual masks and images\n","    dataset_images = []\n","\n","    for img_path in image_filepaths:\n","        img = cv2.imread(img_path, 0)\n","        dataset_images.append(img)\n","\n","    #Convert list to array for machine learning processing\n","    dataset_images = np.array(dataset_images)\n","    return dataset_images, image_filepaths"]},{"cell_type":"code","execution_count":44,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x2aac61f04a90>"]},"execution_count":44,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["dataset_images, image_filepaths = load_dataset_images(\"/fs/ess/PAS2136/Butterfly/butterfly_image_segmentation/neil_butterfly_data_256_256\")\n","\n","normalized_dataset_images = np.expand_dims(dataset_images, axis=3)\n","normalized_dataset_images = normalize(normalized_dataset_images, axis=1)\n","\n","test_img = normalized_dataset_images[0]\n","fp = image_filepaths[0]\n","# for test_img, fp in zip(normalized_dataset_images, image_filepaths):\n","    #use the unet model to predict the mask on the image\n","test_img_norm = test_img[:,:,0][:,:,None]\n","test_image_input = np.expand_dims(test_img_norm, 0) #add batch dimension\n","\n","test_img_norm.shape\n","plt.imshow(test_img_norm, cmap='gray')\n","\n","\n","# prediction = (model.predict(test_image_input))\n","# predicted_img = np.argmax(prediction, axis=3)[0,:,:]"]},{"cell_type":"code","execution_count":46,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["/fs/ess/PAS2136/Butterfly/butterfly_image_segmentation/neil_butterfly_data_256_256_masks/b3_6_720_mask.png\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["dataset_images, image_filepaths = load_dataset_images(\"/fs/ess/PAS2136/Butterfly/butterfly_image_segmentation/neil_butterfly_data_256_256_masks\")\n","predicted_img = dataset_images[6]\n","plt.imshow(predicted_img)\n","\n","print(image_filepaths[6])"]},{"cell_type":"code","execution_count":36,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","Predicted label\n","pixel value:           0\n","category   :           background\n"]}],"source":["id2label = {0: 'background',\n","  1: 'generic',\n","  2: 'right_forewing',\n","  3: 'left_forewing',\n","  4: 'right_hindwing',\n","  5: 'left_hindwing',\n","  6: 'ruler',\n","  7: 'white_balance',\n","  8: 'label',\n","  9: 'color_card',\n","  10: 'body'}\n","\n","# y = 75\n","# x = 175\n","\n","# y = 200\n","# x = 175\n","\n","y = 50\n","x = 50\n","\n","print('\\nPredicted label')\n","print(f'pixel value:           {predicted_img[y, x]}')\n","print(f'category   :           {id2label[predicted_img[y,x]]}')"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"data":{"text/plain":["'/fs/ess/PAS2136/Butterfly/butterfly_image_segmentation/neil_butterfly_data_256_256_masks/b2_1_720_mask.png'"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["fps[0]"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyOW/g69xvySgrIdBMwOV56/","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.4"}},"nbformat":4,"nbformat_minor":0}