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import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np
from mu_map.dataset.default import MuMapDataset
from mu_map.dataset.mock import MuMapMockDataset
from mu_map.dataset.normalization import norm_max
from mu_map.models.unet import UNet
from mu_map.util import to_grayscale, COLOR_WHITE
torch.set_grad_enabled(False)
dataset = MuMapMockDataset("data/initial/")
model = UNet(in_channels=1, features=[8, 16])
device = torch.device("cpu")
weights = torch.load("tmp/10.pth", map_location=device)
model.load_state_dict(weights)
model = model.eval()
recon, mu_map = dataset[0]
recon = recon.unsqueeze(dim=0)
recon = norm_max(recon)
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output = model(recon)
output = output * 40206.0
diff = ((mu_map - output) ** 2).mean()
print(f"Diff: {diff:.3f}")
output = output.squeeze().numpy()
mu_map = mu_map.squeeze().numpy()
assert output.shape[0] == mu_map.shape[0]
wname = "Dataset"
cv.namedWindow(wname, cv.WINDOW_NORMAL)
cv.resizeWindow(wname, 1600, 900)
space = np.full((1024, 10), 239, np.uint8)
def to_display_image(image, _slice):
_image = to_grayscale(image[_slice], min_val=image.min(), max_val=image.max())
_image = cv.resize(_image, (1024, 1024), cv.INTER_AREA)
_text = f"{str(_slice):>{len(str(image.shape[0]))}}/{str(image.shape[0])}"
_image = cv.putText(
_image, _text, (0, 30), cv.FONT_HERSHEY_SIMPLEX, 1, COLOR_WHITE, 3
)
return _image
def com(image1, image2, _slice):
image1 = to_display_image(image1, _slice)
image2 = to_display_image(image2, _slice)
space = np.full((image1.shape[0], 10), 239, np.uint8)
return np.hstack((image1, space, image2))
i = 0
while True:
x = com(output, mu_map, i)
cv.imshow(wname, x)
key = cv.waitKey(100)
if key == ord("q"):
break
i = (i + 1) % output.shape[0]
# dataset = MuMapDataset("data/initial")
# # print(" Recon || MuMap")
# # print(" Min | Max | Average || Min | Max | Average")
# r_max = []
# r_avg = []
# r_max_p = []
# r_avg_p = []
# r_avg_x = []
# m_max = []
# for recon, mu_map in dataset:
# r_max.append(recon.max())
# r_avg.append(recon.mean())
# recon_p = recon[:, :, 16:112, 16:112]
# r_max_p.append(recon_p.max())
# r_avg_p.append(recon_p.mean())
# r_avg_x.append(recon.sum() / (recon > 0.0).sum())
# # r_min = f"{recon.min():5.3f}"
# # r_max = f"{recon.max():5.3f}"
# # r_avg = f"{recon.mean():5.3f}"
# # m_min = f"{mu_map.min():5.3f}"
# # m_max = f"{mu_map.max():5.3f}"
# # m_avg = f"{mu_map.mean():5.3f}"
# # print(f"{r_min} | {r_max} | {r_avg} || {m_min} | {m_max} | {m_avg}")
# m_max.append(mu_map.max())
# # print(mu_map.max())
# r_max = np.array(r_max)
# r_avg = np.array(r_avg)
# r_max_p = np.array(r_max_p)
# r_avg_p = np.array(r_avg_p)
# r_avg_x = np.array(r_avg_x)
# m_max = np.array(m_max)
# fig, ax = plt.subplots()
# ax.scatter(r_max, m_max)
# # fig, axs = plt.subplots(4, 3, figsize=(16, 12))
# # axs[0, 0].hist(r_max)
# # axs[0, 0].set_title("Max")
# # axs[1, 0].hist(r_avg)
# # axs[1, 0].set_title("Mean")
# # axs[2, 0].hist(r_max / r_avg)
# # axs[2, 0].set_title("Max / Mean")
# # axs[3, 0].hist(recon.flatten())
# # axs[3, 0].set_title("Example Reconstruction")
# # axs[0, 1].hist(r_max_p)
# # axs[0, 1].set_title("Max")
# # axs[1, 1].hist(r_avg_p)
# # axs[1, 1].set_title("Mean")
# # axs[2, 1].hist(r_max_p / r_avg_p)
# # axs[2, 1].set_title("Max / Mean")
# # axs[3, 1].hist(recon_p.flatten())
# # axs[3, 1].set_title("Example Reconstruction")
# # axs[0, 2].hist(r_max_p)
# # axs[0, 2].set_title("Max")
# # axs[1, 2].hist(r_avg_x)
# # axs[1, 2].set_title("Mean")
# # axs[2, 2].hist(r_max_p / r_avg_x)
# # axs[2, 2].set_title("Max / Mean")
# # axs[3, 2].hist(torch.masked_select(recon, (recon > 0.0)))
# # axs[3, 2].set_title("Example Reconstruction")