Update tutorials to use kornia.io (#55)

* Use `K.io` in anti-alias with local features examples * Use `K.io` in canny example * Use `K.io` in color conversion example * Use `K.io` in connected components example * Use `K.io` in data augmentation examples * Use `K.io` in descriptor matching example * Use `K.io` in extract patches tutorial * Use `K.io` in face detection tutorial * Use `K.io` in edges tutorial * add `kornia-rs` install line * Use `K.io` in filtering operators tutorial * Use `K.io` in gaussian blur tutorial * Use `K.io` in geometric transform tutorial * Use `K.io` in geometry generate patch * reorder hello world tutorial * Use `K.io` in homography example * Use `K.io` in image enhancement tutorial * Use `K.io` in image matching tutorials * add install kornia and kornia-rs * Use `K.io` in image registration tutorial * Use `K.io` in sold2 tutorial * Use `K.io` in morphology tutorial * Use `K.io` in resize antialias tutorial * Use `K.io` in rotate tutorial * Use `K.io` in denoising tutorial * Use `K.io` in unsharp mask tutorial * Use `K.io` in warp perspective tutorial * fix resize alieased with local features img name * replace kornia install with pypi
kornia · May 8, 2023 · 639356e · 639356e
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diff --git a/nbs/aliased_and_not_aliased_patch_extraction.ipynb b/nbs/aliased_and_not_aliased_patch_extraction.ipynb
diff --git a/nbs/canny.ipynb b/nbs/canny.ipynb
diff --git a/nbs/color_conversions.ipynb b/nbs/color_conversions.ipynb
diff --git a/nbs/connected_components.ipynb b/nbs/connected_components.ipynb
diff --git a/nbs/data_augmentation_2d.ipynb b/nbs/data_augmentation_2d.ipynb
@@ -15,6 +15,17 @@
     "For more information check the docs: https://kornia.readthedocs.io/en/latest/augmentation.module.html"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install kornia\n",
+    "!pip install kornia-rs"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

diff --git a/nbs/data_augmentation_kornia_lightning.ipynb b/nbs/data_augmentation_kornia_lightning.ipynb
@@ -29,7 +29,9 @@
    "outputs": [],
    "source": [
     "%%capture\n",
-    "!pip install kornia pytorch_lightning torchmetrics"
+    "!pip install kornia\n",
+    "!pip install kornia-rs\n",
+    "!pip install pytorch_lightning torchmetrics"
    ]
   },
   {

diff --git a/nbs/data_augmentation_mosiac.ipynb b/nbs/data_augmentation_mosiac.ipynb
@@ -12,6 +12,17 @@
     "**Mosaicing** means taking several input images and combine their random crops into mosaic. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install kornia\n",
+    "!pip install kornia-rs"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -65,7 +76,6 @@
    "outputs": [],
    "source": [
     "import kornia as K\n",
-    "import numpy as np\n",
     "import torch\n",
     "from matplotlib import pyplot as plt\n",
     "from torchvision.transforms import transforms\n",
@@ -78,19 +88,7 @@
     "    img_vis = img.clone()\n",
     "    img_vis = K.utils.draw_rectangle(img_vis, box, color=torch.tensor([255, 0, 0]))\n",
     "    plt.imshow(K.tensor_to_image(img_vis))\n",
-    "\n",
-    "\n",
-    "def load_img(img_path):\n",
-    "    try:\n",
-    "        # not ready on Windows machine\n",
-    "        img = K.io.load_image(img_path, K.io.ImageLoadType.RGB32)\n",
-    "    except:\n",
-    "        import cv2\n",
-    "\n",
-    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n",
-    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
-    "        img = K.image_to_tensor(img).float() / 255.0\n",
-    "    return img"
+    "    plt.show()"
    ]
   },
   {
@@ -111,7 +109,7 @@
     }
    ],
    "source": [
-    "img1 = load_img(\"panda.jpg\")\n",
+    "img1 = K.io.load_image(\"panda.jpg\", K.io.ImageLoadType.RGB32)\n",
     "img2 = K.augmentation.RandomEqualize(p=1.0, keepdim=True)(img1)\n",
     "img3 = K.augmentation.RandomInvert(p=1.0, keepdim=True)(img1)\n",
     "img4 = K.augmentation.RandomChannelShuffle(p=1.0, keepdim=True)(img1)\n",

diff --git a/nbs/data_augmentation_segmentation.ipynb b/nbs/data_augmentation_segmentation.ipynb
diff --git a/nbs/data_augmentation_sequential.ipynb b/nbs/data_augmentation_sequential.ipynb
diff --git a/nbs/data_patch_sequential.ipynb b/nbs/data_patch_sequential.ipynb
diff --git a/nbs/descriptors_matching.ipynb b/nbs/descriptors_matching.ipynb
diff --git a/nbs/extract_combine_patches.ipynb b/nbs/extract_combine_patches.ipynb
diff --git a/nbs/face_detection.ipynb b/nbs/face_detection.ipynb
diff --git a/nbs/filtering_edges.ipynb b/nbs/filtering_edges.ipynb
@@ -19,7 +19,8 @@
    "outputs": [],
    "source": [
     "%%capture\n",
-    "!pip install kornia"
+    "!pip install kornia\n",
+    "!pip install kornia-rs"
    ]
   },
   {
@@ -74,10 +75,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We use OpenCV to load an image to memory represented in a numpy.ndarray"
+    "We use Kornia to load an image to memory represented in a torch.tensor"
    ]
   },
   {
@@ -86,26 +88,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "img_bgr: np.ndarray = cv2.imread(\"doraemon.png\", cv2.IMREAD_COLOR)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Convert the numpy array to torch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_bgr: torch.Tensor = K.utils.image_to_tensor(img_bgr)  # CxHxWx\n",
-    "x_bgr = x_bgr[None, ...].float() / 255.0\n",
+    "x_rgb: torch.Tensor = K.io.load_image(\"doraemon.png\", K.io.ImageLoadType.RGB32)[None, ...]  # BxCxHxW\n",
     "\n",
-    "x_rgb: torch.Tensor = K.color.bgr_to_rgb(x_bgr)\n",
     "x_gray = K.color.rgb_to_grayscale(x_rgb)"
    ]
   },

diff --git a/nbs/filtering_operators.ipynb b/nbs/filtering_operators.ipynb
diff --git a/nbs/gaussian_blur.ipynb b/nbs/gaussian_blur.ipynb
diff --git a/nbs/geometric_transforms.ipynb b/nbs/geometric_transforms.ipynb
diff --git a/nbs/geometry_generate_patch.ipynb b/nbs/geometry_generate_patch.ipynb
diff --git a/nbs/hello_world_tutorial.ipynb b/nbs/hello_world_tutorial.ipynb
diff --git a/nbs/homography.ipynb b/nbs/homography.ipynb
diff --git a/nbs/image_enhancement.ipynb b/nbs/image_enhancement.ipynb
diff --git a/nbs/image_histogram.ipynb b/nbs/image_histogram.ipynb
@@ -24,7 +24,8 @@
    "outputs": [],
    "source": [
     "%%capture\n",
-    "!pip install kornia kornia-rs"
+    "!pip install kornia\n",
+    "!pip install kornia-rs"
    ]
   },
   {

diff --git a/nbs/image_matching.ipynb b/nbs/image_matching.ipynb
diff --git a/nbs/image_matching_adalam.ipynb b/nbs/image_matching_adalam.ipynb
diff --git a/nbs/image_matching_disk.ipynb b/nbs/image_matching_disk.ipynb
diff --git a/nbs/image_points_transforms.ipynb b/nbs/image_points_transforms.ipynb
@@ -27,7 +27,8 @@
    "outputs": [],
    "source": [
     "%%capture\n",
-    "!pip install kornia kornia_rs matplotlib"
+    "!pip install kornia\n",
+    "!pip install kornia-rs matplotlib"
    ]
   },
   {

diff --git a/nbs/image_prompter.ipynb b/nbs/image_prompter.ipynb
@@ -53,6 +53,17 @@
     "## Setup"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install kornia\n",
+    "!pip install kornia-rs"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",

diff --git a/nbs/image_registration.ipynb b/nbs/image_registration.ipynb
diff --git a/nbs/image_stitching.ipynb b/nbs/image_stitching.ipynb
@@ -23,7 +23,8 @@
    "outputs": [],
    "source": [
     "%%capture\n",
-    "!pip install kornia"
+    "!pip install kornia\n",
+    "!pip install kornia-rs"
    ]
   },
   {

diff --git a/nbs/line_detection_and_matching_sold2.ipynb b/nbs/line_detection_and_matching_sold2.ipynb
diff --git a/nbs/morphology_101.ipynb b/nbs/morphology_101.ipynb