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Fictiverse
+67
-13
@@ -8,6 +8,7 @@ import torch
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import torch.nn.functional as F
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from torchvision import transforms
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from random import randint
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from PIL import ImageFilter
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# PIL to Tensor
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def pil2tensor(image):
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@@ -198,7 +199,8 @@ class DisplaceImageWithDepth: #Modified version of WAS node : https://github.com
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fX = X/Frames
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fY = Y/Frames
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fZ = Zoom/Frames
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fR = Rotation/Frames
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for f in range(Frames):
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shakeX = shakeX + np.random.randint(low=-100, high=100)
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@@ -207,7 +209,9 @@ class DisplaceImageWithDepth: #Modified version of WAS node : https://github.com
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tx = fX * f + shakeX*(Shake/100)
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ty = fY * f + shakeY*(Shake/100)
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z = fZ * f
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layers, combined = Tools.apply_perspective_transformation(img, mask, tx, ty, z, LayerCount, Fill)
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r = fR * f
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layers, combined = Tools.apply_perspective_transformation(img, mask, tx, ty, z, r, LayerCount, Fill)
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result_images.append(pil2tensor(combined))
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if f == 0:
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@@ -412,7 +416,7 @@ class Tools_Class():
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return imageLayers
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def apply_perspective_transformation(self, image_pil, depth_map_pil, tx, ty, zoom, num_layers, Fill):
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def apply_perspective_transformation(self, image_pil, depth_map_pil, tx, ty, zoom, rot, num_layers, Fill):
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# Convert PIL images to NumPy arrays
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image = np.array(image_pil)
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@@ -438,6 +442,10 @@ class Tools_Class():
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width, height = image_pil.size
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imagesCombined = Image.new("RGBA", (width, height), (0, 0, 0, 0))
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# Créer une version floutée de l'image
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image_pil_blurred = image_pil.filter(ImageFilter.GaussianBlur(radius=10)) # Ajustez le rayon de flou selon vos besoins
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image_blurred = np.array(image_pil_blurred)
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# Créer les couches en fonction du nombre spécifié
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for i in range(num_layers):
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# Déterminer les valeurs de profondeur minimale et maximale pour cette couche
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@@ -446,21 +454,36 @@ class Tools_Class():
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# Sélectionner les pixels de la depth map qui appartiennent à cette couche
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layer_mask = np.logical_and(depth_map >= layer_min_depth, depth_map <= layer_max_depth)
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if (Fill):
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layer_mask = depth_map >= layer_min_depth
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if Fill:
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layer_mask = depth_map >= layer_min_depth-10
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fill_mask = depth_map <= layer_max_depth
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image_rgb = image[:, :, :3]
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if Fill:
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image_rgb = np.where(fill_mask, image_rgb, image_blurred)
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layer_alpha = (layer_mask[:, :, 0] * 255).astype(np.uint8)
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# Dilate le masque
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kernel = np.ones((5, 5), np.uint8) # Ajustez la taille du noyau selon vos besoins
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layer_alpha_dilated = cv2.erode(layer_alpha, kernel, iterations=1)
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#layer_alpha_pil = Image.fromarray(layer_alpha_dilated)
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#layer_alpha_pil_blurred = layer_alpha_pil.filter(ImageFilter.GaussianBlur(radius=5)) # Ajustez le rayon de flou selon vos besoins
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#layer_alpha_blurred = np.array(layer_alpha_pil_blurred)
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# Create an RGBA image by stacking the RGB channels with the alpha channel
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layer_rgba = np.dstack((image_rgb, layer_alpha))
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layer_rgba = np.dstack((image_rgb, layer_alpha_dilated))
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#layer_rgba = self.edge_padding(layer_rgba, 20)
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# Calculate the parallax_factor for this layer
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parallax_factor = i / (num_layers - 1)
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# Normalize i to be in the range [0, 1]
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t = i / (num_layers - 1)
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# Apply the easing function
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parallax_factor = self.ease_in_out_cubic(t)
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# Calculate the translation for this layer
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tx_offset = int(tx * parallax_factor)
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@@ -470,7 +493,9 @@ class Tools_Class():
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z = i*zoom/num_layers + 1
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translated_mask = self.cv2_clipped_zoom(translated_mask, z)
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# Apply rotation
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translated_mask = self.rotate_image(translated_mask, rot)
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layer_image = Image.fromarray(translated_mask)
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layers.append(layer_image)
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@@ -481,6 +506,30 @@ class Tools_Class():
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return layers, imagesCombined
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def ease_in_out_cubic(self, x):
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"""Cubic ease-in-out function."""
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if x < 0.5:
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return 4 * x * x * x
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else:
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return 1 - pow(-2 * x + 2, 3) / 2
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def alpha_composite(self, foreground, background):
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if foreground.shape != background.shape:
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raise ValueError("Les images doivent avoir la même taille pour la composition alpha")
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fg_rgb = foreground[:, :, :3]
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fg_alpha = foreground[:, :, 3] / 255.0
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bg_rgb = background[:, :, :3]
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bg_alpha = background[:, :, 3] / 255.0
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out_alpha = fg_alpha + bg_alpha * (1 - fg_alpha)
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out_rgb = np.zeros_like(fg_rgb)
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for c in range(3):
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out_rgb[:, :, c] = (fg_rgb[:, :, c] * fg_alpha + bg_rgb[:, :, c] * bg_alpha * (1 - fg_alpha))
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out_rgba = np.dstack((out_rgb, out_alpha * 255)).astype(np.uint8)
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return out_rgba
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def parallax_zoom(self, image, depth_map):
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@@ -525,7 +574,12 @@ class Tools_Class():
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def rotate_image(self, image, angle):
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(h, w) = image.shape[:2]
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(cx, cy) = (w // 2, h // 2)
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M = cv2.getRotationMatrix2D((cx, cy), angle, 1.0)
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rotated_image = cv2.warpAffine(image, M, (w, h))
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return rotated_image
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