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f783161 fbb8705 f783161 a1a407f f783161 a1a407f f783161 a1a407f f783161 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | import torch
from PIL import Image
from app_utils import *
import torch.nn.functional as F
import numpy as np
from torchvision import transforms as TF
from scipy.special import i0
from scipy.optimize import curve_fit
from scipy.integrate import trapezoid
from functools import partial
def von_mises_pdf_alpha_numpy(alpha, x, mu, kappa):
normalization = 2 * np.pi
pdf = np.exp(kappa * np.cos(alpha * (x - mu))) / normalization
return pdf
def val_fit_alpha(distribute):
fit_alphas = []
for y_noise in distribute:
x = np.linspace(0, 2 * np.pi, 360)
y_noise /= trapezoid(y_noise, x) + 1e-8
initial_guess = [x[np.argmax(y_noise)], 1]
# support 1,2,4
alphas = [1.0, 2.0, 4.0]
saved_params = []
saved_r_squared = []
for alpha in alphas:
try:
von_mises_pdf_alpha_partial = partial(von_mises_pdf_alpha_numpy, alpha)
params, covariance = curve_fit(von_mises_pdf_alpha_partial, x, y_noise, p0=initial_guess)
residuals = y_noise - von_mises_pdf_alpha_partial(x, *params)
ss_res = np.sum(residuals**2)
ss_tot = np.sum((y_noise - np.mean(y_noise))**2)
r_squared = 1 - (ss_res / (ss_tot+1e-8))
saved_params.append(params)
saved_r_squared.append(r_squared)
if r_squared > 0.8:
break
except:
saved_params.append((0.,0.))
saved_r_squared.append(0.)
max_index = np.argmax(saved_r_squared)
alpha = alphas[max_index]
mu_fit, kappa_fit = saved_params[max_index]
r_squared = saved_r_squared[max_index]
if alpha == 1. and kappa_fit>=0.6 and r_squared>=0.45:
pass
elif alpha == 2. and kappa_fit>=0.4 and r_squared>=0.45:
pass
elif alpha == 4. and kappa_fit>=0.25 and r_squared>=0.45:
pass
else:
alpha=0.
fit_alphas.append(alpha)
return torch.tensor(fit_alphas)
def preprocess_images(image_list, mode="crop"):
# Check for empty list
if len(image_list) == 0:
raise ValueError("At least 1 image is required")
# Validate mode
if mode not in ["crop", "pad"]:
raise ValueError("Mode must be either 'crop' or 'pad'")
images = []
shapes = set()
to_tensor = TF.ToTensor()
target_size = 518
# First process all images and collect their shapes
# for image_path in image_path_list:
for img in image_list:
# If there's an alpha channel, blend onto white background:
if img.mode == "RGBA":
# Create white background
background = Image.new("RGBA", img.size, (255, 255, 255, 255))
# Alpha composite onto the white background
img = Image.alpha_composite(background, img)
# Now convert to "RGB" (this step assigns white for transparent areas)
img = img.convert("RGB")
width, height = img.size
if mode == "pad":
# Make the largest dimension 518px while maintaining aspect ratio
if width >= height:
new_width = target_size
new_height = round(height * (new_width / width) / 14) * 14 # Make divisible by 14
else:
new_height = target_size
new_width = round(width * (new_height / height) / 14) * 14 # Make divisible by 14
else: # mode == "crop"
# Original behavior: set width to 518px
new_width = target_size
# Calculate height maintaining aspect ratio, divisible by 14
new_height = round(height * (new_width / width) / 14) * 14
# Resize with new dimensions (width, height)
try:
img = img.resize((new_width, new_height), Image.Resampling.BICUBIC)
img = to_tensor(img) # Convert to tensor (0, 1)
except Exception as e:
print(e)
print(width, height)
print(new_width, new_height)
assert False
# Center crop height if it's larger than 518 (only in crop mode)
if mode == "crop" and new_height > target_size:
start_y = (new_height - target_size) // 2
img = img[:, start_y : start_y + target_size, :]
# For pad mode, pad to make a square of target_size x target_size
if mode == "pad":
h_padding = target_size - img.shape[1]
w_padding = target_size - img.shape[2]
if h_padding > 0 or w_padding > 0:
pad_top = h_padding // 2
pad_bottom = h_padding - pad_top
pad_left = w_padding // 2
pad_right = w_padding - pad_left
# Pad with white (value=1.0)
img = torch.nn.functional.pad(
img, (pad_left, pad_right, pad_top, pad_bottom), mode="constant", value=1.0
)
shapes.add((img.shape[1], img.shape[2]))
images.append(img)
# Check if we have different shapes
# In theory our model can also work well with different shapes
if len(shapes) > 1:
print(f"Warning: Found images with different shapes: {shapes}")
# Find maximum dimensions
max_height = max(shape[0] for shape in shapes)
max_width = max(shape[1] for shape in shapes)
# Pad images if necessary
padded_images = []
for img in images:
h_padding = max_height - img.shape[1]
w_padding = max_width - img.shape[2]
if h_padding > 0 or w_padding > 0:
pad_top = h_padding // 2
pad_bottom = h_padding - pad_top
pad_left = w_padding // 2
pad_right = w_padding - pad_left
img = torch.nn.functional.pad(
img, (pad_left, pad_right, pad_top, pad_bottom), mode="constant", value=1.0
)
padded_images.append(img)
images = padded_images
images = torch.stack(images) # concatenate images
# Ensure correct shape when single image
if len(image_list) == 1:
# Verify shape is (1, C, H, W)
if images.dim() == 3:
images = images.unsqueeze(0)
return images
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