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Fictiverse
+66
-30
@@ -192,21 +192,26 @@ class DisplaceImageWithDepth: #Modified version of WAS node : https://github.com
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mask = tensor2pil(Depth[0])
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mask = Tools.resize_and_crop(mask, img.size)
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shakeX = np.random.randint(low=-100, high=100, size=(Frames,))
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shakeY = np.random.randint(low=-100, high=100, size=(Frames,))
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shakeX = 0
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shakeY = 0
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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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for f in range(Frames):
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tx = fX * f + shakeX[f]*(Shake/100)
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ty = fY * f + shakeY[f]*(Shake/100)
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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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shakeY = shakeY + np.random.randint(low=-100, high=100)
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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)
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result_images.append(pil2tensor(combined))
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for layer in layers:
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result_layers.append(pil2tensor(layer))
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if f == 0:
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for layer in layers:
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result_layers.append(pil2tensor(layer))
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result_layers = torch.cat(result_layers, dim=0)
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result_images = torch.cat(result_images, dim=0)
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@@ -439,41 +444,31 @@ class Tools_Class():
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layer_max_depth = min_depth + ((i + 1) / num_layers) * depth_range
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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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#layer_mask = np.logical_and(depth_map >= layer_min_depth, depth_map <= layer_max_depth)
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layer_mask = depth_map >= layer_min_depth
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image_rgb = image[:, :, :3]
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layer_alpha = (layer_mask[:, :, 0] * 255).astype(np.uint8)
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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 = 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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# Calculate the translation for this layer
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tx_offset = int(tx * parallax_factor)
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ty_offset = int(ty * parallax_factor)
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# Determine the new dimensions of the translated image
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new_height = layer_rgba.shape[0]
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new_width = layer_rgba.shape[1]
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translated_mask = self.translate_layer(layer_rgba,tx_offset,ty_offset)
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# Create an empty image with the same shape as merged_image
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translated_mask = np.zeros_like(layer_rgba)
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# Calculate the cropping box
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x1, x2 = max(0, -tx_offset), min(new_width, new_width - tx_offset)
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y1, y2 = max(0, -ty_offset), min(new_height, new_height - ty_offset)
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# Calculate the region to copy from the original image
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src_x1, src_x2 = max(0, tx_offset), min(new_width, new_width + tx_offset)
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src_y1, src_y2 = max(0, ty_offset), min(new_height, new_height + ty_offset)
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# Copy the pixels from the original image to the translated image
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translated_mask[y1:y2, x1:x2] = layer_rgba[src_y1:src_y2, src_x1:src_x2]
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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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layer_image = Image.fromarray(translated_mask)
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layers.append(layer_image)
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@@ -578,9 +573,50 @@ class Tools_Class():
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def translate_layer(self, layer_rgba, tx_offset, ty_offset):
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# Determine the new dimensions of the translated image
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new_height = layer_rgba.shape[0]
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new_width = layer_rgba.shape[1]
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# Create an empty image with the same shape as merged_image
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translated_mask = np.zeros_like(layer_rgba)
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# Calculate the cropping box
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x1, x2 = max(0, -tx_offset), min(new_width, new_width - tx_offset)
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y1, y2 = max(0, -ty_offset), min(new_height, new_height - ty_offset)
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# Calculate the region to copy from the original image
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src_x1, src_x2 = max(0, tx_offset), min(new_width, new_width + tx_offset)
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src_y1, src_y2 = max(0, ty_offset), min(new_height, new_height + ty_offset)
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# Copy the pixels from the original image to the translated image
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translated_mask[y1:y2, x1:x2] = layer_rgba[src_y1:src_y2, src_x1:src_x2]
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return translated_mask
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def edge_padding(self, image, padding_size):
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height, width, channels = image.shape
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# Extraction du canal alpha pour déterminer les bords
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alpha_channel = image[:, :, 3]
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# Création d'un masque autour du contour alpha
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alpha_mask = np.zeros((height, width), dtype=np.uint8)
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alpha_mask[alpha_channel < 255] = 1 # Si le pixel n'est pas complètement opaque (alpha < 255), c'est un bord
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# Dilatation du masque pour ajouter du padding
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kernel = np.ones((padding_size, padding_size), dtype=np.uint8)
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dilated_mask = cv2.dilate(alpha_mask, kernel, iterations=1)
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# Création d'une copie de l'image avec le padding
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padded_image = np.copy(image)
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for c in range(channels): # Appliquer le padding pour chaque canal de couleur
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padded_image[:, :, c][dilated_mask == 1] = 0 # Mettre à zéro les pixels du bord
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return padded_image
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