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schmidt-sebastianschmidt-sebastian
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Fix/interactive segmentation
2 parents c1b7fa6 + 4bd9713 commit 1aa9984

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-33
lines changed

2 files changed

+71
-33
lines changed

‎examples/image_segmentation/python/image_segmentation.ipynb‎

Lines changed: 31 additions & 19 deletions
Original file line numberDiff line numberDiff line change
@@ -54,7 +54,11 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "gxbHBsF-8Y_l"
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "gxbHBsF-8Y_l",
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"outputId": "6077cc85-2400-4970-be66-8a18d61e6151"
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},
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"outputs": [],
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"source": [
@@ -81,7 +85,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"metadata": {
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"id": "OMjuVQiDYJKF"
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},
@@ -104,7 +108,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"metadata": {
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"id": "tzXuqyIBlXer"
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},
@@ -141,7 +145,12 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "8rjHk72-lmHX"
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 514
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},
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"id": "8rjHk72-lmHX",
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"outputId": "188b495f-3de7-41b8-8869-30f6b5ab33bf"
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},
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"outputs": [],
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"source": [
@@ -184,7 +193,12 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "Yl_Oiye4mUuo"
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 514
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},
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"id": "Yl_Oiye4mUuo",
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"outputId": "039d2f20-899d-4cb2-baf3-7c6d368f9675"
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},
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"outputs": [],
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"source": [
@@ -224,8 +238,9 @@
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" bg_image = np.zeros(image_data.shape, dtype=np.uint8)\n",
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" bg_image[:] = BG_COLOR\n",
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"\n",
227-
" condition = np.stack((category_mask.numpy_view(),) * 3, axis=-1) > 0.2\n",
228-
" output_image = np.where(condition, fg_image, bg_image)\n",
241+
" condition = category_mask.numpy_view().squeeze(-1) > 0.2\n",
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"\n",
243+
" output_image = np.where(condition[...,None], fg_image, bg_image)\n",
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"\n",
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" print(f'Segmentation mask of {name}:')\n",
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" resize_and_show(output_image)"
@@ -244,7 +259,12 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "ciCGwCQ3gNDc"
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 514
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},
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"id": "ciCGwCQ3gNDc",
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"outputId": "fe0fc953-af57-4a0e-f7c1-a1727d588862"
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},
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"outputs": [],
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"source": [
@@ -268,21 +288,13 @@
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"\n",
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" # Apply effects\n",
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" blurred_image = cv2.GaussianBlur(image_data, (55,55), 0)\n",
271-
" condition = np.stack((category_mask.numpy_view(),) * 3, axis=-1) > 0.1\n",
272-
" output_image = np.where(condition, image_data, blurred_image)\n",
291+
" condition = category_mask.numpy_view().squeeze(-1) > 0.1\n",
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"\n",
293+
" output_image = np.where(condition[...,None], image_data, blurred_image)\n",
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"\n",
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" print(f'Blurred background of {image_file_name}:')\n",
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" resize_and_show(output_image)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "8evBYbOZk6u4"
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎examples/interactive_segmentation/python/interactive_segmenter.ipynb‎

Lines changed: 40 additions & 14 deletions
Original file line numberDiff line numberDiff line change
@@ -45,7 +45,11 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "gxbHBsF-8Y_l"
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "gxbHBsF-8Y_l",
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"outputId": "6811bf9d-8d70-4311-878e-165a3742b65b"
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},
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"outputs": [],
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"source": [
@@ -72,7 +76,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"metadata": {
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"id": "OMjuVQiDYJKF"
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},
@@ -102,7 +106,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"metadata": {
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"id": "9gYTyXhTKy3H"
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},
@@ -142,7 +146,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"metadata": {
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"id": "tzXuqyIBlXer"
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},
@@ -178,7 +182,12 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "8rjHk72-lmHX"
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 274
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},
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"id": "8rjHk72-lmHX",
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"outputId": "5269c698-f813-445b-b802-eae563a0d270"
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},
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"outputs": [],
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"source": [
@@ -220,7 +229,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 6,
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"metadata": {
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"id": "hl6ojNjl4i6o"
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},
@@ -234,7 +243,12 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "Yl_Oiye4mUuo"
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 274
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},
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"id": "Yl_Oiye4mUuo",
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"outputId": "48829df3-56d8-4766-b012-4f458c11e7a4"
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},
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"outputs": [],
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"source": [
@@ -254,7 +268,7 @@
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"\n",
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"# Create the options that will be used for InteractiveSegmenter\n",
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"base_options = python.BaseOptions(model_asset_path='model.tflite')\n",
257-
"options = vision.ImageSegmenterOptions(base_options=base_options,\n",
271+
"options = vision.InteractiveSegmenterOptions(base_options=base_options,\n",
258272
" output_category_mask=True)\n",
259273
"\n",
260274
"# Create the interactive segmenter\n",
@@ -279,8 +293,8 @@
279293
" bg_image = np.zeros(image_data.shape, dtype=np.uint8)\n",
280294
" bg_image[:] = BG_COLOR\n",
281295
"\n",
282-
" condition = np.stack((category_mask.numpy_view(),) * 3, axis=-1) > 0.1\n",
283-
" output_image = np.where(condition, fg_image, bg_image)\n",
296+
" condition = category_mask.numpy_view().squeeze(-1) > 0.1\n",
297+
" output_image = np.where(condition[..., None], fg_image, bg_image)\n",
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"\n",
285299
" # Draw a white dot with black border to denote the point of interest\n",
286300
" thickness, radius = 6, -1\n",
@@ -305,7 +319,12 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "ciCGwCQ3gNDc"
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 274
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},
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"id": "ciCGwCQ3gNDc",
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"outputId": "8d903a0d-11b6-48a3-f85c-b2ee1c2eea65"
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},
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"outputs": [],
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"source": [
@@ -332,7 +351,8 @@
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" # Apply effects\n",
333352
" blurred_image = cv2.GaussianBlur(image_data, (55,55), 0)\n",
334353
" condition = np.stack((category_mask.numpy_view(),) * 3, axis=-1) > 0.1\n",
335-
" output_image = np.where(condition, image_data, blurred_image)\n",
354+
" condition = category_mask.numpy_view().squeeze(-1) > 0.1\n",
355+
" output_image = np.where(condition[..., None], image_data, blurred_image)\n",
336356
"\n",
337357
" # Draw a white dot with black border to denote the point of interest\n",
338358
" thickness, radius = 6, -1\n",
@@ -357,7 +377,12 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "szFMEFuymrHd"
380+
"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 274
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},
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"id": "szFMEFuymrHd",
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"outputId": "6fdc6af9-dc91-4b67-eea6-d23fee03837a"
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},
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"outputs": [],
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"source": [
@@ -386,7 +411,8 @@
386411
" overlay_image[:] = OVERLAY_COLOR\n",
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"\n",
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" # Create the condition from the category_masks array\n",
389-
" alpha = np.stack((category_mask.numpy_view(),) * 3, axis=-1) > 0.1\n",
414+
" alpha = category_mask.numpy_view().squeeze(-1) > 0.1\n",
415+
" alpha = alpha[..., None]\n",
390416
"\n",
391417
" # Create an alpha channel from the condition with the desired opacity (e.g., 0.7 for 70%)\n",
392418
" alpha = alpha.astype(float) * 0.7\n",

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