Created base app to generate data and images for paper.

app.py

@@ -1,40 +1,89 @@
-import random
+
 
 import matplotlib.pyplot as plt
 import numpy as np
-import pandas as pd
-from datasets import load_dataset, ClassLabel, Sequence
-import json
-import numpy
-
-# from transformers import AutoImageProcessor
-# from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor
-# from transformers import DefaultDataCollator
-# # import evaluate
-# import numpy as np
-# from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
-# from PIL import Image
-# from matplotlib import cm
+
 
 import streamlit as st
 from Data_Generation.Dataset_Generation_Functions import make_boxes
-from Data_Generation.Piecewise_Box_Functions import basic_box_array
-
-image_size = st.slider('Select a value for the image size')
+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))
 
-densities = pd.DataFrame(np.linspace(0,1, num=density_selection+1)[1:])
-densities.columns = ["Density Values"]
-
-
-st.write("Here are the density values that would exist in your dataset:")
-st.write(densities)
-
-
-example_density = basic_box_array(image_size, 1)
 
 '''
 # Testing