def adjust_quantization_and_save(img_name: str, quantization: int):
try:
p = PGMImage(img_name)
print(f"Adjusting quantization of {img_name} to {quantization}")
p.quantization = quantization
for i in range(p.rows):
# Normalize, quantize, and cast to byte string
p.pixels[i] = b"".join([
bytes([int((int(px) / 256) * quantization)])
for px in p.pixels[i]
])
p.save(f"images/quantized-{quantization}-{p.name}")
except IOError:
pass
def equalize_histogram(img_name: str, visualize_results: bool):
p = PGMImage(img_name)
T_r = transformer_of(p)
p2 = PGMImage(img_name)
# Transform the image according to T_r
for i in range(len(p2.pixels)):
p2.pixels[i] = b"".join(bytes([T_r[b]]) for b in p2.pixels[i])
p2.save(f"equalized-{p2.name}")
if visualize_results:
p.show_histogram(title=f"Histogram of {p.name} before equalization")
p2.show_histogram(title=f"Histogram of {p2.name} after equalization")
def specify_histogram(img_name, specified_img_name, visualize_results=True):
p1, p2 = PGMImage(img_name), PGMImage(specified_img_name)
T_r = transformer_of(p1)
G_z = transformer_of(p2)
def inverted_histogram(h: List[int]) -> List[int]:
G_i = [0] * len(h)
for h_i in h:
G_i[h_i] = h_i
# Populate missing values
last_nonzero_intensity = 0
for i in range(len(h)):
if G_i[i] != 0:
last_nonzero_intensity = G_i[i]
else:
G_i[i] = last_nonzero_intensity
return G_i
G_inverse_z = inverted_histogram(G_z)
p3 = PGMImage(img_name)
for i in range(len(p3.pixels)):
p3.pixels[i] = b"".join(
bytes([G_inverse_z[T_r[b]]]) for b in p3.pixels[i])
p3.save(f"specified-to-{p2.name}-{p1.name}")
if visualize_results:
p1.show_histogram()
p2.show_histogram()
p3.show_histogram(
title=f"Histogram of {p1.name}, specified to {p2.name}")