Create app.py

app.py

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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('C:/Users/Matt/Downloads/model_epoch_45.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()