Temporary main

calculate_accuracy_qualitative.bat

@@ -1,31 +1,31 @@
 python calculate_accuracy_qualitative.py ^
---ground_truth_data_dir data/qualnoholes ^
---generated_samples_dir results/256/qualnoholes183 ^
---num_plates 100 ^
+--ground_truth_data_dir data/testdata ^
+--generated_samples_dir results/donkey256prev ^
+--num_plates 200 ^
 --num_conditions_per_plate 1 ^
 --num_steps_per_condition 2 ^
 --image_size 256
 
-python calculate_accuracy_qualitative.py ^
---ground_truth_data_dir data/qualholes ^
---generated_samples_dir results/256/qualholes183 ^
---num_plates 100 ^
---num_conditions_per_plate 1 ^
---num_steps_per_condition 2 ^
---image_size 256
+@REM python calculate_accuracy_qualitative.py ^
+@REM --ground_truth_data_dir data/qualholes ^
+@REM --generated_samples_dir results/256/qualholes183 ^
+@REM --num_plates 100 ^
+@REM --num_conditions_per_plate 1 ^
+@REM --num_steps_per_condition 2 ^
+@REM --image_size 256
 
-python calculate_accuracy_qualitative.py ^
---ground_truth_data_dir data/qualforceramp ^
---generated_samples_dir results/256/qualforceramp183 ^
---num_plates 100 ^
---num_conditions_per_plate 1 ^
---num_steps_per_condition 11 ^
---image_size 256
+@REM python calculate_accuracy_qualitative.py ^
+@REM --ground_truth_data_dir data/qualforceramp ^
+@REM --generated_samples_dir results/256/qualforceramp183 ^
+@REM --num_plates 100 ^
+@REM --num_conditions_per_plate 1 ^
+@REM --num_steps_per_condition 11 ^
+@REM --image_size 256
 
-python calculate_accuracy_qualitative.py ^
---ground_truth_data_dir data/qualitativesample ^
---generated_samples_dir results/256/256ckpt183 ^
---num_plates 200 ^
---num_conditions_per_plate 1 ^
---num_steps_per_condition 2 ^
---image_size 256
+@REM python calculate_accuracy_qualitative.py ^
+@REM --ground_truth_data_dir data/qualitativesample ^
+@REM --generated_samples_dir results/256/256ckpt183 ^
+@REM --num_plates 200 ^
+@REM --num_conditions_per_plate 1 ^
+@REM --num_steps_per_condition 2 ^
+@REM --image_size 256

calculate_accuracy_qualitative.py

@@ -0,0 +1,49 @@
+from metrics.calculate_accuracy_qualitative import calculate_accuracy, calculate_accuracy_per_step
+import argparse
+import numpy as np
+parser = argparse.ArgumentParser(description="Calculate accuracy.")
+parser.add_argument(
+    "--ground_truth_data_dir", type=str, default="data/sample_data", help="Data directory."
+)
+parser.add_argument(
+    "--generated_samples_dir", type=str, default="results/generated_data", help="Sample data directory."
+)
+parser.add_argument(
+    "--num_plates", type=int, default=1, help="Number of plates in sample dataset."
+)
+parser.add_argument(
+    "--num_conditions_per_plate",
+    type=int,
+    default=1,
+    help="Number of conditions per plate in sample dataset.",
+)
+parser.add_argument(
+    "--num_steps_per_condition",
+    type=int,
+    default=3,
+    help="Number of steps per condition in sample dataset.",
+)
+parser.add_argument("--image_size", type=int, default=256, help="Image size.")
+
+args = parser.parse_args()
+
+MAE, MSE, RMSE, SSIM, mean_absolute_error_values, mean_squared_error_values, root_mean_squared_error_values, ssim = calculate_accuracy(
+    num_plates=args.num_plates,
+    num_conditions_per_plate=args.num_conditions_per_plate,
+    num_steps=args.num_steps_per_condition,
+    image_size=args.image_size,
+    ground_truth_data_dir=args.ground_truth_data_dir,
+    generated_samples_dir=args.generated_samples_dir,
+)
+
+def round_off(value):
+    return np.around(value, decimals=4)
+
+print("Mean absolute error: {}".format(round_off(MAE)))
+print("Mean squared error: {}".format(round_off(MSE)))
+print("RMSE: {}".format(round_off(RMSE)))
+print("SSIM: {}".format(round_off(SSIM)))
+print("Mean absolute error: {}".format(round_off(mean_absolute_error_values)))
+print("Mean squared error: {}".format(round_off(mean_squared_error_values)))
+print("Root mean squared error: {}".format(round_off(root_mean_squared_error_values)))
+print("SSIM: {}".format(round_off(ssim)))

generate_data_GT.bat

@@ -1,9 +1,9 @@
 python generate_data.py ^
---num_plates 2500 ^
---data_dir data/2000to2500 ^
---start_plate 2324 ^
---conditions_per_plate 4 ^
---steps_per_condition 6 ^
+--num_plates 200 ^
+--data_dir data/testdata ^
+--start_plate 1 ^
+--conditions_per_plate 1 ^
+--steps_per_condition 2 ^
 --image_size 512 ^
---save_meshes ^
---save_displacement 
+--save_displacement
+@REM --save_meshes ^ 

metrics/accuracy_function_qualitative.py

@@ -0,0 +1,55 @@
+import numpy as np
+import pyvista as pv
+from PIL import Image, ImageOps
+import matplotlib.pyplot as plt
+from SSIM_PIL import compare_ssim
+
+def calculate_accuracy_for_one_sample(
+    displacement_x_file, displacement_y_file, gt_x_file, gt_y_file, image_size
+):
+    predicted_displacement_x = np.array(
+        ImageOps.grayscale(
+            Image.open(displacement_x_file).resize((image_size, image_size))
+        )
+    ).squeeze()/255.0
+
+    predicted_displacement_y = np.array(
+        ImageOps.grayscale(
+            Image.open(displacement_y_file).resize((image_size, image_size))
+        )
+    ).squeeze()/255.0
+
+    gt_displacement_x = np.array(
+        ImageOps.grayscale(Image.open(gt_x_file).resize((image_size, image_size)))
+    ).squeeze()/255.0
+
+    gt_displacement_y = np.array(
+        ImageOps.grayscale(Image.open(gt_y_file).resize((image_size, image_size)))
+    ).squeeze()/255.0
+
+    mean_absolute_error = np.mean(
+        [
+            np.mean(np.abs(predicted_displacement_x - gt_displacement_x)),
+            np.mean(np.abs(predicted_displacement_y - gt_displacement_y)),
+        ]
+    )
+
+    mean_squared_error = np.mean(
+        [
+            np.mean(np.square(predicted_displacement_x - gt_displacement_x)),
+            np.mean(np.square(predicted_displacement_y - gt_displacement_y)),
+        ]
+    )
+
+
+    ssimx = compare_ssim((ImageOps.grayscale(Image.open(displacement_x_file).resize((image_size,image_size)))), 
+                            (ImageOps.grayscale(Image.open(gt_x_file).resize((image_size,image_size)))), GPU=False)
+
+    ssimy = compare_ssim((ImageOps.grayscale(Image.open(displacement_y_file).resize((image_size,image_size)))), 
+                        (ImageOps.grayscale(Image.open(gt_y_file).resize((image_size,image_size)))), GPU=False)
+
+    ssim = np.mean([ssimx, ssimy])
+
+    root_mean_squared_error = np.sqrt(mean_squared_error)
+
+    return mean_absolute_error, mean_squared_error, root_mean_squared_error, ssim