Added data download button, and incorporating code to use different arrays in the readmme file
716dc6a
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| from Data_Generation.Dataset_Generation_Functions import make_boxes | |
| from Data_Generation.Piecewise_Box_Functions import basic_box_array, forward_slash_array, combine_arrays, add_thickness | |
| ######################################################################################################################## | |
| # User Inputs | |
| image_size = st.slider('Select a value for the image size', min_value=9, max_value=28) | |
| # st.write(x, 'squared is', x * x) | |
| density_selection = st.slider('Select a value for the number of equally spaced density values (0, 1]', min_value=1, max_value=10) | |
| ######################################################################################################################## | |
| # Compute Example Shapes | |
| densities = np.linspace(0, 1, num=density_selection+1)[1:] | |
| sample_basic_box = basic_box_array(image_size, 1) | |
| sample_forward_slash_box = forward_slash_array(image_size, 1) | |
| sample_combined = combine_arrays([sample_forward_slash_box, sample_basic_box]) | |
| # print(sample_combined) | |
| sample_density = np.array([sample_combined * density_value for density_value in densities]) | |
| # copy = sample_combined | |
| sample_thickness = [] | |
| # test = add_thickness(copy, 2) | |
| # sample_thickness.append(test) | |
| # print(sample_thickness) | |
| for i in [1, 2, 3, 4]: | |
| copy = sample_combined | |
| # print(i) | |
| test = add_thickness(copy, i) | |
| # print(test) | |
| sample_thickness.append(test) | |
| # print(sample_thickness) | |
| ######################################################################################################################## | |
| # Output Example Shapes | |
| st.write("Click 'Generate Samples' to show some density values that would exist in your dataset:") | |
| # Show samples of various density values | |
| if st.button('Generate Samples'): # Generate the samples | |
| plt.figure(1) | |
| st.header("Sample Density Figures:") | |
| max_figures = min(density_selection, 5) | |
| for i in range(max_figures): | |
| plt.subplot(1, max_figures+1, i+1), plt.imshow(sample_density[i], cmap='gray', vmin=0, vmax=1) | |
| if i != 0: # Show y-label for only first figure | |
| plt.tick_params(left=False, labelleft=False) | |
| plt.title("Density: " + str(round(densities[i], 4)), fontsize=6) | |
| plt.figure(1) | |
| # cax = plt.axes([0.85, 0.1, 0.075, 0.8]) | |
| # plt.colorbar(cax=cax, shrink=0.1) | |
| st.pyplot(plt.figure(1)) | |
| # Show samples of various thickness values | |
| st.header("Sample Thickness Figures:") | |
| plt.figure(2) | |
| for i in range(len(sample_thickness)): | |
| plt.subplot(1, 5, i+1), plt.imshow(sample_thickness[i], cmap='gray', vmin=0, vmax=1) | |
| if i != 0: # Show y-label for only first figure | |
| plt.tick_params(left=False, labelleft=False) | |
| plt.title("Thickness: " + str(i+1), fontsize=6) | |
| plt.figure(2) | |
| # cax = plt.axes([0.85, 0.1, 0.075, 0.8]) | |
| # plt.colorbar(cax=cax, shrink=0.1) | |
| st.pyplot(plt.figure(2)) | |
| ######################################################################################################################## | |
| # Output Generated Examples | |
| # plt.figure(2) | |
| # for j in range(5): # shows 5 random images to the users to view samples of the dataset | |
| # i = np.random.randint(0, len(result)) | |
| # plt.subplot(550 + 1 + j) | |
| # plt.imshow(result[i], cmap='gray', vmin=0, vmax=1) | |
| # plt.figure(2) | |
| # st.pyplot(plt.figure(2)) | |
| ''' | |
| # Testing | |
| image_size = 100 | |
| densities = [1] | |
| boxes = make_boxes(image_size, densities) | |
| desired_density = 1 | |
| # desired_thickness = 0 | |
| desired_basic_box_thickness = 1 | |
| desired_forward_slash_box_thickness = 2 | |
| desired_back_slash_box_thickness = 0 | |
| desired_hot_dog_box_thickness = 0 | |
| desired_hamburger_box_thickness = 0 | |
| box_arrays, box_density, basic_box_thickness, forward_slash_box_thickness, back_slash_box_thickness,hot_dog_box_thickness, hamburger_box_thickness\ | |
| = 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] | |
| # print(np.shape(box_arrays)) | |
| # print(np.shape(box_shape)) | |
| # print(np.shape(box_density)) | |
| indices = [i for i in range(len(box_arrays)) if box_density[i] == desired_density | |
| and basic_box_thickness[i] == desired_basic_box_thickness | |
| and forward_slash_box_thickness[i] == desired_forward_slash_box_thickness | |
| and back_slash_box_thickness[i] == desired_back_slash_box_thickness | |
| and hot_dog_box_thickness[i] == desired_hot_dog_box_thickness | |
| and hamburger_box_thickness[i] == desired_hamburger_box_thickness] | |
| plt.imshow(box_arrays[indices[0]], cmap='gray', vmin=0, vmax=1) | |
| plt.show() | |
| ''' | |
| # # Testing | |
| # image_size = 8 | |
| # densities = [1] | |
| if st.button('Generate Dataset'): # Generate the samples | |
| boxes = make_boxes(image_size, densities) | |
| box_arrays, box_density, basic_box_thickness, forward_slash_box_thickness, back_slash_box_thickness,hot_dog_box_thickness, hamburger_box_thickness\ | |
| = 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] | |
| # Create a dataframe to convert the data to a csv file | |
| dataframe = (pd.DataFrame((box_arrays, box_density, basic_box_thickness, forward_slash_box_thickness, back_slash_box_thickness,hot_dog_box_thickness, hamburger_box_thickness)).T).astype(str) | |
| # Rename the columns to the desired outputs | |
| dataframe = dataframe.rename(columns={0: "Array", 1: "Density", 2:"Basic Box Thickness", 3:"Forward Slash Strut Thickness", 4:"Back Slash Strut Thickness", 5:"Vertical Strut Thickness", 6:"Horizontal Strut Thickness"}) | |
| csv = dataframe.to_csv('2D_Lattice.csv') | |
| file = '2D_Lattice.csv' | |
| st.download_button("Download Dataset", file, file_name='2D_Lattice.csv') | |
| ''' | |
| # food = load_dataset("cmudrc/2d-lattices", split="train[:15]") # Loads the training data samples | |
| food = load_dataset("cmudrc/2d-lattices", split="train+test") # Loads all of the data, for use after training | |
| # checks to see if the dataset has been assigned a class label | |
| # if type(food.features["label"]) != 'datasets.features.features.ClassLabel': # Cast to ClassLabel | |
| # food = food.class_encode_column('label') | |
| print(food) | |
| desired_label = 'x_plus_box' | |
| desired_thickness = 3 | |
| desired_density = 1 | |
| data_frame = pd.DataFrame(food) | |
| # print(data_frame) | |
| shape_rows = data_frame['Shape'] == desired_label | |
| # print(shape_rows) | |
| thickness_rows = data_frame['Thickness'] == desired_thickness | |
| # print(thickness_rows) | |
| density_rows = data_frame['Density'] == desired_density | |
| # print(density_rows) | |
| desired_output = data_frame.loc[shape_rows & thickness_rows & density_rows].iloc[0]['Array'] | |
| print(desired_output) | |
| print(type(desired_output)) | |
| example_point = numpy.array(json.loads(desired_output)) | |
| plt.imshow(example_point) | |
| plt.show() | |
| all_shapes = [basic_box, diagonal_box_split, horizontal_vertical_box_split, back_slash_box, forward_slash_box, | |
| back_slash_plus_box, forward_slash_plus_box, hot_dog_box, hamburger_box, x_hamburger_box, | |
| x_hot_dog_box, x_plus_box] | |
| base_shapes = [basic_box, back_slash_box, forward_slash_box, hot_dog_box, hamburger_box] | |
| image_size = 256 | |
| density = [1] | |
| boxes = make_boxes(image_size, density, all_shapes) | |
| box_arrays, box_shape, box_density, box_thickness, = list(zip(*boxes))[0], list(zip(*boxes))[1], list(zip(*boxes))[2], list(zip(*boxes))[3] | |
| # 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] | |
| 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] | |
| print(indices_1) | |
| # indices_1 = random.randint(0, len(box_arrays)) | |
| # plt.imshow(box_arrays[indices_1]) | |
| plt.imshow(box_arrays[indices_1[0]]) | |
| plt.show() | |
| ''' | |
| '''trainer.push_to_hub()''' # Need to figure out how to push the model to the hub |