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import gradio as gr
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import cv2
import PIL.Image
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
def RGB2gray(rgb):
r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
return gray
# Update img_to_patches to handle direct image input
def img_to_patches(img: PIL.Image.Image) -> tuple:
patch_size = 16
img = img.convert('RGB') # Ensure image is in RGB format
grayscale_imgs = []
imgs = []
coordinates = []
for i in range(0, img.height, patch_size):
for j in range(0, img.width, patch_size):
box = (j, i, j + patch_size, i + patch_size)
img_color = np.asarray(img.crop(box))
grayscale_image = cv2.cvtColor(src=img_color, code=cv2.COLOR_RGB2GRAY)
grayscale_imgs.append(grayscale_image.astype(dtype=np.int32))
imgs.append(img_color)
normalized_coord = (i + patch_size // 2, j + patch_size // 2)
coordinates.append(normalized_coord)
return grayscale_imgs, imgs, coordinates, (img.height, img.width)
def get_l1(v):
return np.sum(np.abs(v[:, :-1] - v[:, 1:]))
def get_l2(v):
return np.sum(np.abs(v[:-1, :] - v[1:, :]))
def get_l3l4(v):
l3 = np.sum(np.abs(v[:-1, :-1] - v[1:, 1:]))
l4 = np.sum(np.abs(v[1:, :-1] - v[:-1, 1:]))
return l3 + l4
def get_pixel_var_degree_for_patch(patch: np.array) -> int:
l1 = get_l1(patch)
l2 = get_l2(patch)
l3l4 = get_l3l4(patch)
return l1 + l2 + l3l4
def get_rich_poor_patches(img: PIL.Image.Image, coloured=True):
gray_scale_patches, color_patches, coordinates, img_size = img_to_patches(img)
var_with_patch = []
for i, patch in enumerate(gray_scale_patches):
if coloured:
var_with_patch.append((get_pixel_var_degree_for_patch(patch), color_patches[i], coordinates[i]))
else:
var_with_patch.append((get_pixel_var_degree_for_patch(patch), patch, coordinates[i]))
var_with_patch.sort(reverse=True, key=lambda x: x[0])
mid_point = len(var_with_patch) // 2
r_patch = [(patch, coor) for var, patch, coor in var_with_patch[:mid_point]]
p_patch = [(patch, coor) for var, patch, coor in var_with_patch[mid_point:]]
p_patch.reverse()
return r_patch, p_patch, img_size
def azimuthalAverage(image, center=None):
y, x = np.indices(image.shape)
if not center:
center = np.array([(x.max() - x.min()) / 2.0, (y.max() - y.min()) / 2.0])
r = np.hypot(x - center[0], y - center[1])
ind = np.argsort(r.flat)
r_sorted = r.flat[ind]
i_sorted = image.flat[ind]
r_int = r_sorted.astype(int)
deltar = r_int[1:] - r_int[:-1]
rind = np.where(deltar)[0]
nr = rind[1:] - rind[:-1]
csim = np.cumsum(i_sorted, dtype=float)
tbin = csim[rind[1:]] - csim[rind[:-1]]
radial_prof = tbin / nr
return radial_prof
def azimuthal_integral(img, epsilon=1e-8, N=50):
if len(img.shape) == 3 and img.shape[2] == 3:
img = RGB2gray(img)
f = np.fft.fft2(img)
fshift = np.fft.fftshift(f)
fshift += epsilon
magnitude_spectrum = 20 * np.log(np.abs(fshift))
psd1D = azimuthalAverage(magnitude_spectrum)
points = np.linspace(0, N, num=psd1D.size)
xi = np.linspace(0, N, num=N)
interpolated = griddata(points, psd1D, xi, method='cubic')
interpolated = (interpolated - np.min(interpolated)) / (np.max(interpolated) - np.min(interpolated))
return interpolated.astype(np.float32)
def positional_emb(coor, im_size, N):
img_height, img_width = im_size
center_y, center_x = coor
normalized_y = center_y / img_height
normalized_x = center_x / img_width
pos_emb = np.zeros(N)
indices = np.arange(N)
div_term = 10000 ** (2 * (indices // 2) / N)
pos_emb[0::2] = np.sin(normalized_y / div_term[0::2]) + np.sin(normalized_x / div_term[0::2])
pos_emb[1::2] = np.cos(normalized_y / div_term[1::2]) + np.cos(normalized_x / div_term[1::2])
return pos_emb
def azi_diff(img: PIL.Image.Image, patch_num, N):
r, p, im_size = get_rich_poor_patches(img)
r_len = len(r)
p_len = len(p)
patch_emb_r = np.zeros((patch_num, N))
patch_emb_p = np.zeros((patch_num, N))
positional_emb_r = np.zeros((patch_num, N))
positional_emb_p = np.zeros((patch_num, N))
coor_r = []
coor_p = []
if r_len != 0:
for idx in range(patch_num):
tmp_patch1 = r[idx % r_len][0]
tmp_coor1 = r[idx % r_len][1]
patch_emb_r[idx] = azimuthal_integral(tmp_patch1, N=N)
positional_emb_r[idx] = positional_emb(tmp_coor1, im_size, N)
coor_r.append(tmp_coor1)
if p_len != 0:
for idx in range(patch_num):
tmp_patch2 = p[idx % p_len][0]
tmp_coor2 = p[idx % p_len][1]
patch_emb_p[idx] = azimuthal_integral(tmp_patch2, N=N)
positional_emb_p[idx] = positional_emb(tmp_coor2, im_size, N)
coor_p.append(tmp_coor2)
output = {"total_emb": [patch_emb_r + positional_emb_r / 5, patch_emb_p + positional_emb_p / 5],
"positional_emb": [positional_emb_r / 5, positional_emb_p / 5], "coor": [coor_r, coor_p],
"image_size": im_size}
return output
class AttentionBlock(nn.Module):
def __init__(self, input_dim, num_heads, ff_dim, rate=0.1):
super(AttentionBlock, self).__init__()
self.attention = nn.MultiheadAttention(embed_dim=input_dim, num_heads=num_heads)
self.dropout1 = nn.Dropout(rate)
self.layer_norm1 = nn.LayerNorm(input_dim)
self.ffn = nn.Sequential(
nn.Linear(input_dim, ff_dim),
nn.ReLU(),
nn.Dropout(rate),
nn.Linear(ff_dim, input_dim),
nn.Dropout(rate)
)
self.layer_norm2 = nn.LayerNorm(input_dim)
def forward(self, x):
attn_output, _ = self.attention(x, x, x)
attn_output = self.dropout1(attn_output)
out1 = self.layer_norm1(attn_output + x)
ffn_output = self.ffn(out1)
out2 = self.layer_norm2(ffn_output + out1)
return out2
class TextureContrastClassifier(nn.Module):
def __init__(self, input_shape, num_heads=4, key_dim=64, ff_dim=256, rate=0.1):
super(TextureContrastClassifier, self).__init__()
input_dim = input_shape[1]
self.rich_attention_block = AttentionBlock(input_dim, num_heads, ff_dim, rate)
self.rich_dense = nn.Sequential(
nn.Linear(input_dim, 128),
nn.ReLU(),
nn.Dropout(0.5)
)
self.poor_attention_block = AttentionBlock(input_dim, num_heads, ff_dim, rate)
self.poor_dense = nn.Sequential(
nn.Linear(input_dim, 128),
nn.ReLU(),
nn.Dropout(0.5)
)
self.fc = nn.Sequential(
nn.Linear(128 * input_shape[0], 256),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(256, 128),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(128, 64),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(64, 32),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(32, 16),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(16, 1),
nn.Sigmoid()
)
def forward(self, rich_texture, poor_texture):
rich_texture = rich_texture.permute(1, 0, 2)
poor_texture = poor_texture.permute(1, 0, 2)
rich_attention = self.rich_attention_block(rich_texture)
rich_attention = rich_attention.permute(1, 0, 2)
rich_features = self.rich_dense(rich_attention)
poor_attention = self.poor_attention_block(poor_texture)
poor_attention = poor_attention.permute(1, 0, 2)
poor_features = self.poor_dense(poor_attention)
difference = rich_features - poor_features
difference = difference.view(difference.size(0), -1)
output = self.fc(difference)
return output
input_shape = (128, 256)
model = TextureContrastClassifier(input_shape)
model.load_state_dict(torch.load('./model_epoch_36.pth', map_location=torch.device('cpu')))
def inference(image, model):
predictions = []
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
tmp = azi_diff(image, patch_num=128, N=256)
rich = tmp["total_emb"][0]
poor = tmp["total_emb"][1]
rich_texture_tensor = torch.tensor(rich, dtype=torch.float32).unsqueeze(0).to(device)
poor_texture_tensor = torch.tensor(poor, dtype=torch.float32).unsqueeze(0).to(device)
with torch.no_grad():
output = model(rich_texture_tensor, poor_texture_tensor)
prediction = output.cpu().numpy().flatten()[0]
return prediction
# Gradio Interface
def predict(image):
prediction = inference(image, model)
return f"{prediction * 100:.2f}% chance AI-generated"
gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs="text").launch()
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