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https://github.com/sbrl/research-rainfallradar
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TEST SCRIPT: deeplabv3
ref https://keras.io/examples/vision/deeplabv3_plus/ dataset ref https://drive.google.com/uc?id=1B9A9UCJYMwTL4oBEo4RZfbMZMaZhKJaz (the code is *terrible* spaghetti....!)
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aimodel/src/deeplabv3_plus_test.py
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aimodel/src/deeplabv3_plus_test.py
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# @source https://keras.io/examples/vision/deeplabv3_plus/
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# Required dataset: https://drive.google.com/uc?id=1B9A9UCJYMwTL4oBEo4RZfbMZMaZhKJaz [instance-level-human-parsing.zip]
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from datetime import datetime
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import os
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import cv2
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import numpy as np
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from glob import glob
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from scipy.io import loadmat
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import matplotlib.pyplot as plt
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import tensorflow as tf
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IMAGE_SIZE = 512
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BATCH_SIZE = 4
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NUM_CLASSES = 20
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DATA_DIR = "./instance-level_human_parsing/instance-level_human_parsing/Training"
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NUM_TRAIN_IMAGES = 1000
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NUM_VAL_IMAGES = 50
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DIR_OUTPUT=f"output/{datetime.utcnow().date().isoformat()}_deeplabv3plus_TEST"
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os.makedirs(DIR_OUTPUT)
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train_images = sorted(glob(os.path.join(DATA_DIR, "Images/*")))[:NUM_TRAIN_IMAGES]
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train_masks = sorted(glob(os.path.join(DATA_DIR, "Category_ids/*")))[:NUM_TRAIN_IMAGES]
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val_images = sorted(glob(os.path.join(DATA_DIR, "Images/*")))[
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NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES
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]
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val_masks = sorted(glob(os.path.join(DATA_DIR, "Category_ids/*")))[
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NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES
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]
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def read_image(image_path, mask=False):
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image = tf.io.read_file(image_path)
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if mask:
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image = tf.image.decode_png(image, channels=1)
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image.set_shape([None, None, 1])
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image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
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else:
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image = tf.image.decode_png(image, channels=3)
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image.set_shape([None, None, 3])
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image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
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image = image / 127.5 - 1
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return image
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def load_data(image_list, mask_list):
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image = read_image(image_list)
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mask = read_image(mask_list, mask=True)
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return image, mask
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def data_generator(image_list, mask_list):
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dataset = tf.data.Dataset.from_tensor_slices((image_list, mask_list))
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dataset = dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE)
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dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
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return dataset
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train_dataset = data_generator(train_images, train_masks)
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val_dataset = data_generator(val_images, val_masks)
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print("Train Dataset:", train_dataset)
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print("Val Dataset:", val_dataset)
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# ███ ███ ██████ ██████ ███████ ██
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# ████ ████ ██ ██ ██ ██ ██ ██
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# ██ ████ ██ ██ ██ ██ ██ █████ ██
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# ██ ██ ██ ██ ██ ██ ██ ██ ██
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# ██ ██ ██████ ██████ ███████ ███████
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def convolution_block(
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block_input,
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num_filters=256,
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kernel_size=3,
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dilation_rate=1,
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padding="same",
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use_bias=False,
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):
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x = layers.Conv2D(
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num_filters,
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kernel_size=kernel_size,
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dilation_rate=dilation_rate,
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padding="same",
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use_bias=use_bias,
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kernel_initializer=keras.initializers.HeNormal(),
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)(block_input)
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x = layers.BatchNormalization()(x)
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return tf.nn.relu(x)
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def DilatedSpatialPyramidPooling(dspp_input):
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dims = dspp_input.shape
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x = layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
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x = convolution_block(x, kernel_size=1, use_bias=True)
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out_pool = layers.UpSampling2D(
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size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation="bilinear",
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)(x)
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out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)
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out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)
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out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)
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out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)
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x = layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])
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output = convolution_block(x, kernel_size=1)
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return output
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def DeeplabV3Plus(image_size, num_classes):
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model_input = keras.Input(shape=(image_size, image_size, 3))
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resnet50 = keras.applications.ResNet50(
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weights="imagenet", include_top=False, input_tensor=model_input
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)
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x = resnet50.get_layer("conv4_block6_2_relu").output
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x = DilatedSpatialPyramidPooling(x)
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input_a = layers.UpSampling2D(
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size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),
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interpolation="bilinear",
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)(x)
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input_b = resnet50.get_layer("conv2_block3_2_relu").output
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input_b = convolution_block(input_b, num_filters=48, kernel_size=1)
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x = layers.Concatenate(axis=-1)([input_a, input_b])
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x = convolution_block(x)
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x = convolution_block(x)
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x = layers.UpSampling2D(
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size=(image_size // x.shape[1], image_size // x.shape[2]),
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interpolation="bilinear",
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)(x)
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model_output = layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
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return keras.Model(inputs=model_input, outputs=model_output)
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model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES)
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model.summary()
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# ████████ ██████ █████ ██ ███ ██ ██ ███ ██ ██████
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# ██ ██ ██ ██ ██ ██ ████ ██ ██ ████ ██ ██
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# ██ ██████ ███████ ██ ██ ██ ██ ██ ██ ██ ██ ██ ███
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# ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██
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# ██ ██ ██ ██ ██ ██ ██ ████ ██ ██ ████ ██████
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loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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model.compile(
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optimizer=keras.optimizers.Adam(learning_rate=0.001),
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loss=loss,
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metrics=["accuracy"],
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)
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history = model.fit(train_dataset,
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validation_data=val_dataset,
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epochs=25,
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callbacks=[
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tf.keras.callbacks.CSVLogger(
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filename=os.path.join(DIR_OUTPUT, "metrics.tsv"),
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separator="\t"
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)
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],
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)
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plt.plot(history.history["loss"])
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plt.title("Training Loss")
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plt.ylabel("loss")
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plt.xlabel("epoch")
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plt.savefig(os.path.join(DIR_OUTPUT, "loss.png"))
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plt.plot(history.history["accuracy"])
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plt.title("Training Accuracy")
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plt.ylabel("accuracy")
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plt.xlabel("epoch")
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plt.savefig(os.path.join(DIR_OUTPUT, "acc.png"))
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plt.plot(history.history["val_loss"])
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plt.title("Validation Loss")
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plt.ylabel("val_loss")
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plt.xlabel("epoch")
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plt.savefig(os.path.join(DIR_OUTPUT, "val_loss.png"))
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plt.plot(history.history["val_accuracy"])
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plt.title("Validation Accuracy")
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plt.ylabel("val_accuracy")
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plt.xlabel("epoch")
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plt.savefig(os.path.join(DIR_OUTPUT, "val_acc.png"))
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# ██ ███ ██ ███████ ███████ ██████ ███████ ███ ██ ██████ ███████
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# ██ ████ ██ ██ ██ ██ ██ ██ ████ ██ ██ ██
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# ██ ██ ██ ██ █████ █████ ██████ █████ ██ ██ ██ ██ █████
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# ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██
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# ██ ██ ████ ██ ███████ ██ ██ ███████ ██ ████ ██████ ███████
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# Loading the Colormap
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colormap = loadmat(
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"./instance-level_human_parsing/instance-level_human_parsing/human_colormap.mat"
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)["colormap"]
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colormap = colormap * 100
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colormap = colormap.astype(np.uint8)
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def infer(model, image_tensor):
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predictions = model.predict(np.expand_dims((image_tensor), axis=0))
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predictions = np.squeeze(predictions)
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predictions = np.argmax(predictions, axis=2)
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return predictions
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def decode_segmentation_masks(mask, colormap, n_classes):
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r = np.zeros_like(mask).astype(np.uint8)
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g = np.zeros_like(mask).astype(np.uint8)
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b = np.zeros_like(mask).astype(np.uint8)
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for l in range(0, n_classes):
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idx = mask == l
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r[idx] = colormap[l, 0]
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g[idx] = colormap[l, 1]
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b[idx] = colormap[l, 2]
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rgb = np.stack([r, g, b], axis=2)
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return rgb
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def get_overlay(image, colored_mask):
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image = tf.keras.preprocessing.image.array_to_img(image)
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image = np.array(image).astype(np.uint8)
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overlay = cv2.addWeighted(image, 0.35, colored_mask, 0.65, 0)
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return overlay
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def plot_samples_matplotlib(filepath, display_list, figsize=(5, 3)):
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_, axes = plt.subplots(nrows=1, ncols=len(display_list), figsize=figsize)
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for i in range(len(display_list)):
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if display_list[i].shape[-1] == 3:
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axes[i].imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))
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else:
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axes[i].imshow(display_list[i])
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plt.savefig(filepath)
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def plot_predictions(filepath, images_list, colormap, model):
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for image_file in images_list:
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image_tensor = read_image(image_file)
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prediction_mask = infer(image_tensor=image_tensor, model=model)
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prediction_colormap = decode_segmentation_masks(prediction_mask, colormap, 20)
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overlay = get_overlay(image_tensor, prediction_colormap)
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plot_samples_matplotlib(
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filepath,
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[image_tensor, overlay, prediction_colormap],
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figsize=(18, 14)
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)
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plot_predictions(os.path.join(DIR_OUTPUT, "predict_train.png"), train_images[:4], colormap, model=model)
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plot_predictions(os.path.join(DIR_OUTPUT, "predict_validate.png"), val_images[:4], colormap, model=model)
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