Update app.py

app.py

@@ -8,160 +8,7 @@ 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
+from utils import azi_diff
 
 class TextureContrastClassifier(nn.Module):
     def __init__(self, input_shape, num_heads=4, key_dim=64, ff_dim=256, rate=0.1):