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import matplotlib.pyplot as plt |
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import numpy as np |
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import streamlit as st |
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from Data_Generation.Dataset_Generation_Functions import make_boxes |
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from Data_Generation.Piecewise_Box_Functions import basic_box_array, forward_slash_array, combine_arrays, add_thickness |
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image_size = st.slider('Select a value for the image size', min_value=9, max_value=28) |
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density_selection = st.slider('Select a value for the number of equally spaced density values (0, 1]', min_value=1, max_value=10) |
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densities = np.linspace(0, 1, num=density_selection+1)[1:] |
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sample_basic_box = basic_box_array(image_size, 1) |
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sample_forward_slash_box = forward_slash_array(image_size, 1) |
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sample_combined = combine_arrays([sample_forward_slash_box, sample_basic_box]) |
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print(sample_combined) |
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sample_density = np.array([sample_combined * density_value for density_value in densities]) |
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sample_thickness = [] |
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for i in [1, 2, 3, 4]: |
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copy = sample_combined |
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print(i) |
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test = add_thickness(copy, i) |
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print(test) |
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sample_thickness.append(test) |
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st.write("Click 'Generate Samples' to show some density values that would exist in your dataset:") |
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if st.button('Generate Samples'): |
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plt.figure(1) |
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st.header("Sample Density Figures:") |
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max_figures = min(density_selection, 5) |
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for i in range(max_figures): |
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plt.subplot(1, max_figures+1, i+1), plt.imshow(sample_density[i], cmap='gray', vmin=0, vmax=1) |
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if i != 0: |
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plt.tick_params(left=False, labelleft=False) |
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plt.title("Density: " + str(round(densities[i], 4)), fontsize=6) |
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plt.figure(1) |
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st.pyplot(plt.figure(1)) |
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st.header("Sample Thickness Figures:") |
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plt.figure(2) |
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for i in range(len(sample_thickness)): |
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plt.subplot(1, 5, i+1), plt.imshow(sample_thickness[i], cmap='gray', vmin=0, vmax=1) |
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if i != 0: |
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plt.tick_params(left=False, labelleft=False) |
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plt.title("Thickness: " + str(i+1), fontsize=6) |
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plt.figure(2) |
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st.pyplot(plt.figure(2)) |
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''' |
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# Testing |
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image_size = 100 |
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densities = [1] |
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boxes = make_boxes(image_size, densities) |
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desired_density = 1 |
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# desired_thickness = 0 |
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desired_basic_box_thickness = 1 |
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desired_forward_slash_box_thickness = 2 |
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desired_back_slash_box_thickness = 0 |
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desired_hot_dog_box_thickness = 0 |
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desired_hamburger_box_thickness = 0 |
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box_arrays, box_density, basic_box_thickness, forward_slash_box_thickness, back_slash_box_thickness,hot_dog_box_thickness, hamburger_box_thickness\ |
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= list(zip(*boxes))[0], list(zip(*boxes))[1], list(zip(*boxes))[2], list(zip(*boxes))[3], list(zip(*boxes))[4], list(zip(*boxes))[5], list(zip(*boxes))[6] |
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# print(np.shape(box_arrays)) |
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# print(np.shape(box_shape)) |
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# print(np.shape(box_density)) |
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indices = [i for i in range(len(box_arrays)) if box_density[i] == desired_density |
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and basic_box_thickness[i] == desired_basic_box_thickness |
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and forward_slash_box_thickness[i] == desired_forward_slash_box_thickness |
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and back_slash_box_thickness[i] == desired_back_slash_box_thickness |
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and hot_dog_box_thickness[i] == desired_hot_dog_box_thickness |
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and hamburger_box_thickness[i] == desired_hamburger_box_thickness] |
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plt.imshow(box_arrays[indices[0]], cmap='gray', vmin=0, vmax=1) |
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plt.show() |
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''' |
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''' |
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# food = load_dataset("cmudrc/2d-lattices", split="train[:15]") # Loads the training data samples |
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food = load_dataset("cmudrc/2d-lattices", split="train+test") # Loads all of the data, for use after training |
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# checks to see if the dataset has been assigned a class label |
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# if type(food.features["label"]) != 'datasets.features.features.ClassLabel': # Cast to ClassLabel |
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# food = food.class_encode_column('label') |
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print(food) |
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desired_label = 'x_plus_box' |
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desired_thickness = 3 |
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desired_density = 1 |
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data_frame = pd.DataFrame(food) |
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# print(data_frame) |
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shape_rows = data_frame['Shape'] == desired_label |
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# print(shape_rows) |
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thickness_rows = data_frame['Thickness'] == desired_thickness |
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# print(thickness_rows) |
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density_rows = data_frame['Density'] == desired_density |
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# print(density_rows) |
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desired_output = data_frame.loc[shape_rows & thickness_rows & density_rows].iloc[0]['Array'] |
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print(desired_output) |
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print(type(desired_output)) |
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example_point = numpy.array(json.loads(desired_output)) |
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plt.imshow(example_point) |
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plt.show() |
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all_shapes = [basic_box, diagonal_box_split, horizontal_vertical_box_split, back_slash_box, forward_slash_box, |
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back_slash_plus_box, forward_slash_plus_box, hot_dog_box, hamburger_box, x_hamburger_box, |
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x_hot_dog_box, x_plus_box] |
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base_shapes = [basic_box, back_slash_box, forward_slash_box, hot_dog_box, hamburger_box] |
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image_size = 256 |
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density = [1] |
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boxes = make_boxes(image_size, density, all_shapes) |
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box_arrays, box_shape, box_density, box_thickness, = list(zip(*boxes))[0], list(zip(*boxes))[1], list(zip(*boxes))[2], list(zip(*boxes))[3] |
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# indices_1 = [i for i in range(len(boxes)) if boxes[1][i] == str(base_shapes[0]) and boxes[2][i] == density[0] and boxes[3][i] == desired_thickness] |
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indices_1 = [i for i in range(len(box_arrays)) if box_shape[i] == desired_label and box_density[i] == desired_density and box_thickness[i] == desired_thickness] |
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print(indices_1) |
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# indices_1 = random.randint(0, len(box_arrays)) |
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# plt.imshow(box_arrays[indices_1]) |
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plt.imshow(box_arrays[indices_1[0]]) |
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plt.show() |
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''' |
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'''trainer.push_to_hub()''' |
