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from utils.k_degrade_utils import *
if __name__ == "__main__":
# First STEP
import matplotlib.pyplot as plt
import numpy as np, os
os.makedirs("outputs", exist_ok=True)
os.makedirs("outputs/low-fre-first", exist_ok=True)
os.makedirs("outputs/random-sample", exist_ok=True)
image_size = 256
accelerated_factor = 6
center_fraction = 0.04
time_step = 25
masks = get_ksu_kernel(time_step, image_size, "LogSamplingRate",
accelerated_factor=accelerated_factor, center_fraction=center_fraction) # LogSamplingRate
batch_size = 1
img = plt.imread("./assets/BraTS20_Training_001_86_t1.png")
img = cv2.resize(img, (image_size, image_size), interpolation=cv2.INTER_LINEAR)
img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
print("input img shape: ", img.shape)
# to gray scale
if len(img.shape) == 3 and img.shape[-1] == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# img = np.transpose(img, (2, 0, 1))
# img = img[0]
img = np.expand_dims(img, axis=0)
img = torch.from_numpy(img).unsqueeze(0).float()
original_img = img.clone()
rand_kernels = []
rand_x = torch.randint(0, image_size + 1, (batch_size,)).long()
img = img #* 2 - 1 #
masked_img = []
for m in masks:
m = m.unsqueeze(0)
img = apply_ksu_kernel(img, m)
masked_img.append(img)
save_masks = masks
masks = np.concatenate(masks, axis=-1)[0]
masked_img = torch.concat(masked_img, dim=-1).numpy() #+ 1) * 0.5
masked_img = np.transpose(masked_img, (0, 2, 3, 1))[0, ..., 0]
# masked_img = cv2.cvtColor(masked_img, cv2.COLOR_RGB2GRAY)
img = np.concatenate([masks, masked_img], axis=0)
min_ = masked_img.min()
max_ = masked_img.max()
out = img[image_size: 2 * image_size, : image_size]
fft, _ = apply_tofre(torch.from_numpy(out), torch.from_numpy(out)) # complex
fft = np.abs(fft.numpy())
fft = np.log(fft)
fft = (fft - fft.min()) / (fft.max() - fft.min())
for i in range(time_step+1):
out = img[image_size: 2 * image_size, i * image_size: (i + 1) * image_size]
# out = (out - out.min()) / (out.max() - out.min())
out = (out - min_) / (max_ - min_)
plt.imsave(f"outputs/low-fre-first/{i}_image.png", out, cmap='gray')
if i != 0:
out = img[:image_size, i * image_size:(i + 1) * image_size]
out = (out - out.min()) / (out.max() - out.min())
plt.imsave(f"outputs/low-fre-first/{i}_mask.png", out, cmap='gray')
save_fft = fft * out
plt.imsave(f"outputs/low-fre-first/{i}_fft.png", save_fft, cmap='gray')
else:
diff = np.ones((image_size, image_size, 3), dtype=np.uint8) * 255 # All 255 (White)ve
ones = diff.astype(np.float32) / 255.0
print("ones shape: ", ones.shape, ones.min(), ones.max())
plt.imsave(f"outputs/low-fre-first/{i}_mask.png", ones, cmap='gray')
plt.imsave(f"outputs/low-fre-first/{i}_fft.png", fft, cmap='gray')
try:
diff = img[:image_size, (i-1) * image_size:(i) * image_size] - \
img[:image_size, (i) * image_size:(i + 1) * image_size]
except:
diff = np.zeros_like(img[:image_size, : image_size])
# plt.imsave(f"outputs/low-fre-first/{i}_mask_diff.png", diff, cmap='gray')
# print("diff shape: ", diff.shape, diff.min(), diff.max())
diffsig = diff * fft
# save it as a red img, but the bg is trasparent
alpha_channel = np.full_like(diff, 255, dtype=np.uint8) * diff
alpha_channel = np.expand_dims(alpha_channel, axis=-1)
diff = (diff * 255).astype(np.uint8)
diff = np.stack([diff, np.zeros_like(diff), np.zeros_like(diff)], axis=-1)
# Create an alpha channel (255 for full opacity)
# Concatenate RGB with Alpha channel
diff = np.concatenate([diff, alpha_channel], axis=-1)
diff = diff.astype(np.uint8)
# print("diff shape: ", diff.shape, diff.min(), diff.max())
plt.imsave(f"outputs/low-fre-first/{i}_mask_diff_red.png", diff, cmap='gray')
plt.imsave(f"outputs/low-fre-first/{i}_mask_diffsig.png", diffsig, cmap='gray')
plt.imsave("outputs/masked_img.png", masked_img, cmap='gray')
plt.figure(figsize=(5*time_step, 10))
plt.imshow(img, cmap='gray') # (1, 128, 1280)
plt.show()
print("\n\nSecond stage...")
# ------------------------------- ------------------------------- -------------------------------
# ------------------------------- ------------------------------- -------------------------------
# Second STEP completely Random
import matplotlib.pyplot as plt
import numpy as np
final_mask = save_masks[-1][0].numpy()
new_masks = []
plt.imshow(final_mask, cmap='gray')
plt.show()
height, width = final_mask.shape
print("final_mask shape: ", final_mask.shape)
# Count ones and zeros
ones = np.sum(final_mask[0] == 1)
zeros = np.sum(final_mask[0] == 0)
print("Initial ones count:", ones)
print("Initial zeros count:", zeros)
# Identify initially filled and empty strips
initial_filled_indices = np.where(final_mask[0] == 1)[0]
remaining_indices = np.where(final_mask[0] == 0)[0]
# Shuffle remaining indices to randomize filling order
np.random.shuffle(remaining_indices)
# Split remaining indices into `time_step` parts
fills_per_step = np.array_split(remaining_indices, time_step)
masked_img = [] # Store masks at each step
# Copy initial mask
current_mask = final_mask.copy()
new_masks.append(current_mask.copy()) # Store initial state
# Fill remaining strips over time
for i in range(time_step):
current_mask[:, fills_per_step[i - 1]] = 1 # Fill new strips
new_masks.append(current_mask.copy()) # Store new mask
# current_mask.append(final_mask) # Store new mask
new_masks = new_masks[::-1] # Reverse list to get correct order
masked_img = []
for m in new_masks:
m = torch.from_numpy(m) #.unsqueeze(0)
img = apply_ksu_kernel(original_img, m)
masked_img.append(img)
masks = np.concatenate(new_masks, axis=-1)
masked_img = torch.concat(masked_img, dim=-1).numpy() #+ 1) * 0.5
masked_img = np.transpose(masked_img, (0, 2, 3, 1))[0, ..., 0]
print("masked_img shape: ", masked_img.shape)
# masked_img = cv2.cvtColor(masked_img, cv2.COLOR_RGB2GRAY)
# masked_img = (masked_img - masked_img.min()) / (masked_img.max() - masked_img.min())
img = np.concatenate([masks, masked_img], axis=0)
min_ = masked_img.min()
max_ = masked_img.max()
out = img[image_size: 2 * image_size, : image_size]
fft, _ = apply_tofre(torch.from_numpy(out), torch.from_numpy(out)) # complex
fft = np.abs(fft.numpy())
fft = np.log(fft)
fft = (fft - fft.min()) / (fft.max() - fft.min())
for i in range(time_step+1):
# if i % 3 != 0:
# continue
out = img[image_size : 2*image_size, i * image_size : (i + 1) * image_size]
# out = (out - out.min()) / (out.max() - out.min())
out = (out - min_) / (max_ - min_)
plt.imsave(f"outputs/random-sample/{i}_image.png", out, cmap='gray')
if i != 0:
out = img[:image_size, i * image_size:(i + 1) * image_size]
out = (out - out.min()) / (out.max() - out.min())
plt.imsave(f"outputs/random-sample/{i}_mask.png", out, cmap='gray')
save_fft = fft * out
plt.imsave(f"outputs/random-sample/{i}_fft.png", save_fft, cmap='gray')
noise = np.random.normal(0, 0.2*np.log((time_step-i)+1), out.shape) * fft
save_fft = fft + noise * (1-out) # Sigma
plt.imsave(f"outputs/random-sample/{i}_fft_reverse.png", save_fft, cmap='gray')
else:
ones = np.ones_like(out) * 255
plt.imsave(f"outputs/random-sample/{i}_mask.png", ones, cmap='gray')
plt.imsave(f"outputs/random-sample/{i}_fft.png", fft, cmap='gray')
try:
diff = img[:image_size, (i-1) * image_size:(i) * image_size] - \
img[:image_size, (i) * image_size:(i + 1) * image_size]
except:
diff = np.zeros_like(img[:image_size, : image_size])
plt.imsave(f"outputs/random-sample/{i}_mask_diff.png", diff, cmap='gray')
# print("diff shape: ", diff.shape, diff.min(), diff.max())
# save it as a red img, but the bg is trasparent
alpha_channel = np.full_like(diff, 255, dtype=np.uint8) * diff
alpha_channel = np.expand_dims(alpha_channel, axis=-1)
diff = (diff * 255).astype(np.uint8)
diff = np.stack([diff, np.zeros_like(diff), np.zeros_like(diff)], axis=-1)
# Create an alpha channel (255 for full opacity)
# Concatenate RGB with Alpha channel
diff = np.concatenate([diff, alpha_channel], axis=-1)
diff = diff.astype(np.uint8)
print("diff shape: ", diff.shape, diff.min(), diff.max())
plt.imsave(f"outputs/random-sample/{i}_mask_diff_red.png", diff, cmap='gray')
plt.imsave("outputs/img.png", img, cmap='gray')
# plt.figure(figsize=(5*time_step, 10))
plt.imshow(img, cmap='gray') # (1, 128, 1280)
plt.tight_layout()
plt.show()
print("\n\nSecond stage...")
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