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-rw-r--r--utils/tuning/libtuning/utils.py43
1 files changed, 43 insertions, 0 deletions
diff --git a/utils/tuning/libtuning/utils.py b/utils/tuning/libtuning/utils.py
index 1e8128ea..f099c0ed 100644
--- a/utils/tuning/libtuning/utils.py
+++ b/utils/tuning/libtuning/utils.py
@@ -123,3 +123,46 @@ def load_images(input_dir: str, config: dict, load_nonlsc: bool, load_lsc: bool)
return None
return images
+
+
+
+"""
+Some code that will save virtual macbeth charts that show the difference between optimised matrices and non optimised matrices
+
+The function creates an image that is 1550 by 1050 pixels wide, and fills it with patches which are 200x200 pixels in size
+Each patch contains the ideal color, the color from the original matrix, and the color from the final matrix
+_________________
+| |
+| Ideal Color |
+|_______________|
+| Old | new |
+| Color | Color |
+|_______|_______|
+
+Nice way of showing how the optimisation helps change the colors and the color matricies
+"""
+def visualise_macbeth_chart(macbeth_rgb, original_rgb, new_rgb, output_filename):
+ image = np.zeros((1050, 1550, 3), dtype=np.uint8)
+ colorindex = -1
+ for y in range(6):
+ for x in range(4): # Creates 6 x 4 grid of macbeth chart
+ colorindex += 1
+ xlocation = 50 + 250 * x # Means there is 50px of black gap between each square, more like the real macbeth chart.
+ ylocation = 50 + 250 * y
+ for g in range(200):
+ for i in range(100):
+ image[xlocation + i, ylocation + g] = macbeth_rgb[colorindex]
+ xlocation = 150 + 250 * x
+ ylocation = 50 + 250 * y
+ for i in range(100):
+ for g in range(100):
+ image[xlocation + i, ylocation + g] = original_rgb[colorindex] # Smaller squares below to compare the old colors with the new ones
+ xlocation = 150 + 250 * x
+ ylocation = 150 + 250 * y
+ for i in range(100):
+ for g in range(100):
+ image[xlocation + i, ylocation + g] = new_rgb[colorindex]
+
+ img = Image.fromarray(image, 'RGB')
+ img.save(str(output_filename) + 'Generated Macbeth Chart.png')
+