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Unverified Commit c9eb3554 authored by vivek rathod's avatar vivek rathod Committed by GitHub
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Merged commit includes the following changes: (#8829) 



320618558  by rathodv:

    Internal Change.

--
320597532  by ronnyvotel:

    Exposing DensePose visualizations to model_lib_v2.py.

--
320533669  by ronnyvotel:

    Adding utilities to visualize DensePose outputs.

--
320529647  by lzc:

    Fix saved_model issue in object_detection_tutorial notebook.

--
320510127  by aom:

    Internal change.

--
320490236  by derekjchow:

    Update Dockerfiles to use setup.py

--
320443572  by rathodv:

    Update `Tensorflow` to `TensorFlow` in documentation.

--
320426460  by ronnyvotel:

    DensePose proto and model builder.

--
320352611  by rathodv:

    Update documentation to reflect the support for TF1 and TF2. Provide separate sets of instructions to reduce confusion.

--
320350724  by rathodv:

    Internal Change.

--

PiperOrigin-RevId: 320618558
Co-authored-by: default avatarkmindspark <kaushikshiv@google.com>
parent 950ebc45
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research/object_detection/CONTRIBUTING.md
View file @ c9eb3554
# Contributing to the Tensorflow Object Detection API # Contributing to the TensorFlow Object Detection API
Patches to Tensorflow Object Detection API are welcome! Patches to TensorFlow Object Detection API are welcome!
We require contributors to fill out either the individual or corporate We require contributors to fill out either the individual or corporate
Contributor License Agreement (CLA). Contributor License Agreement (CLA).
...@@ -9,5 +9,5 @@ Contributor License Agreement (CLA). ...@@ -9,5 +9,5 @@ Contributor License Agreement (CLA).
* If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](http://code.google.com/legal/corporate-cla-v1.0.html). * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](http://code.google.com/legal/corporate-cla-v1.0.html).
Please follow the Please follow the
[Tensorflow contributing guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md) [TensorFlow contributing guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md)
when submitting pull requests. when submitting pull requests.
research/object_detection/builders/model_builder.py
View file @ c9eb3554
...@@ -870,6 +870,22 @@ def mask_proto_to_params(mask_config): ...@@ -870,6 +870,22 @@ def mask_proto_to_params(mask_config):
heatmap_bias_init=mask_config.heatmap_bias_init) heatmap_bias_init=mask_config.heatmap_bias_init)
def densepose_proto_to_params(densepose_config):
"""Converts CenterNet.DensePoseEstimation proto to parameter namedtuple."""
classification_loss, localization_loss, _, _, _, _, _ = (
losses_builder.build(densepose_config.loss))
return center_net_meta_arch.DensePoseParams(
class_id=densepose_config.class_id,
classification_loss=classification_loss,
localization_loss=localization_loss,
part_loss_weight=densepose_config.part_loss_weight,
coordinate_loss_weight=densepose_config.coordinate_loss_weight,
num_parts=densepose_config.num_parts,
task_loss_weight=densepose_config.task_loss_weight,
upsample_to_input_res=densepose_config.upsample_to_input_res,
heatmap_bias_init=densepose_config.heatmap_bias_init)
def _build_center_net_model(center_net_config, is_training, add_summaries): def _build_center_net_model(center_net_config, is_training, add_summaries):
"""Build a CenterNet detection model. """Build a CenterNet detection model.
...@@ -922,6 +938,11 @@ def _build_center_net_model(center_net_config, is_training, add_summaries): ...@@ -922,6 +938,11 @@ def _build_center_net_model(center_net_config, is_training, add_summaries):
if center_net_config.HasField('mask_estimation_task'): if center_net_config.HasField('mask_estimation_task'):
mask_params = mask_proto_to_params(center_net_config.mask_estimation_task) mask_params = mask_proto_to_params(center_net_config.mask_estimation_task)
densepose_params = None
if center_net_config.HasField('densepose_estimation_task'):
densepose_params = densepose_proto_to_params(
center_net_config.densepose_estimation_task)
return center_net_meta_arch.CenterNetMetaArch( return center_net_meta_arch.CenterNetMetaArch(
is_training=is_training, is_training=is_training,
add_summaries=add_summaries, add_summaries=add_summaries,
...@@ -931,7 +952,8 @@ def _build_center_net_model(center_net_config, is_training, add_summaries): ...@@ -931,7 +952,8 @@ def _build_center_net_model(center_net_config, is_training, add_summaries):
object_center_params=object_center_params, object_center_params=object_center_params,
object_detection_params=object_detection_params, object_detection_params=object_detection_params,
keypoint_params_dict=keypoint_params_dict, keypoint_params_dict=keypoint_params_dict,
mask_params=mask_params) mask_params=mask_params,
densepose_params=densepose_params)
def _build_center_net_feature_extractor( def _build_center_net_feature_extractor(
......
research/object_detection/builders/model_builder_tf2_test.py
View file @ c9eb3554
...@@ -164,6 +164,28 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest): ...@@ -164,6 +164,28 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest):
return text_format.Merge(proto_txt, return text_format.Merge(proto_txt,
center_net_pb2.CenterNet.MaskEstimation()) center_net_pb2.CenterNet.MaskEstimation())
def get_fake_densepose_proto(self):
proto_txt = """
task_loss_weight: 0.5
class_id: 0
loss {
classification_loss {
weighted_softmax {}
}
localization_loss {
l1_localization_loss {
}
}
}
num_parts: 24
part_loss_weight: 1.0
coordinate_loss_weight: 2.0
upsample_to_input_res: true
heatmap_bias_init: -2.0
"""
return text_format.Merge(proto_txt,
center_net_pb2.CenterNet.DensePoseEstimation())
def test_create_center_net_model(self): def test_create_center_net_model(self):
"""Test building a CenterNet model from proto txt.""" """Test building a CenterNet model from proto txt."""
proto_txt = """ proto_txt = """
...@@ -195,6 +217,8 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest): ...@@ -195,6 +217,8 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest):
self.get_fake_label_map_file_path()) self.get_fake_label_map_file_path())
config.center_net.mask_estimation_task.CopyFrom( config.center_net.mask_estimation_task.CopyFrom(
self.get_fake_mask_proto()) self.get_fake_mask_proto())
config.center_net.densepose_estimation_task.CopyFrom(
self.get_fake_densepose_proto())
# Build the model from the configuration. # Build the model from the configuration.
model = model_builder.build(config, is_training=True) model = model_builder.build(config, is_training=True)
...@@ -251,6 +275,21 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest): ...@@ -251,6 +275,21 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest):
self.assertAlmostEqual( self.assertAlmostEqual(
model._mask_params.heatmap_bias_init, -2.0, places=4) model._mask_params.heatmap_bias_init, -2.0, places=4)
# Check DensePose related parameters.
self.assertEqual(model._densepose_params.class_id, 0)
self.assertIsInstance(model._densepose_params.classification_loss,
losses.WeightedSoftmaxClassificationLoss)
self.assertIsInstance(model._densepose_params.localization_loss,
losses.L1LocalizationLoss)
self.assertAlmostEqual(model._densepose_params.part_loss_weight, 1.0)
self.assertAlmostEqual(model._densepose_params.coordinate_loss_weight, 2.0)
self.assertEqual(model._densepose_params.num_parts, 24)
self.assertAlmostEqual(model._densepose_params.task_loss_weight, 0.5)
self.assertTrue(model._densepose_params.upsample_to_input_res)
self.assertEqual(model._densepose_params.upsample_method, 'bilinear')
self.assertAlmostEqual(
model._densepose_params.heatmap_bias_init, -2.0, places=4)
# Check feature extractor parameters. # Check feature extractor parameters.
self.assertIsInstance( self.assertIsInstance(
model._feature_extractor, model._feature_extractor,
......
research/object_detection/colab_tutorials/eager_few_shot_od_training_tf2_colab.ipynb
View file @ c9eb3554
{ {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "interactive_eager_few_shot_od_training_colab.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"accelerator": "GPU"
},
"cells": [ "cells": [
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -29,7 +15,7 @@ ...@@ -29,7 +15,7 @@
"To run this colab, you will need to connect to a borg runtime (we recommend\n", "To run this colab, you will need to connect to a borg runtime (we recommend\n",
"Tensorflow GPU with Python 3).\n", "Tensorflow GPU with Python 3).\n",
"\n", "\n",
"Estimated time to run through this colab: < 5 minutes." "Estimated time to run through this colab: \u003c 5 minutes."
] ]
}, },
{ {
...@@ -44,24 +30,26 @@ ...@@ -44,24 +30,26 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"id": "LBZ9VWZZFUCT", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "LBZ9VWZZFUCT"
}, },
"outputs": [],
"source": [ "source": [
"!pip install -U --pre tensorflow==\"2.2.0\"" "!pip install -U --pre tensorflow==\"2.2.0\""
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"id": "oi28cqGGFWnY", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "oi28cqGGFWnY"
}, },
"outputs": [],
"source": [ "source": [
"import os\n", "import os\n",
"import pathlib\n", "import pathlib\n",
...@@ -72,17 +60,17 @@ ...@@ -72,17 +60,17 @@
" os.chdir('..')\n", " os.chdir('..')\n",
"elif not pathlib.Path('models').exists():\n", "elif not pathlib.Path('models').exists():\n",
" !git clone --depth 1 https://github.com/tensorflow/models" " !git clone --depth 1 https://github.com/tensorflow/models"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"id": "NwdsBdGhFanc", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "NwdsBdGhFanc"
}, },
"outputs": [],
"source": [ "source": [
"# Install the Object Detection API\n", "# Install the Object Detection API\n",
"%%bash\n", "%%bash\n",
...@@ -90,17 +78,17 @@ ...@@ -90,17 +78,17 @@
"protoc object_detection/protos/*.proto --python_out=.\n", "protoc object_detection/protos/*.proto --python_out=.\n",
"cp object_detection/packages/tf2/setup.py .\n", "cp object_detection/packages/tf2/setup.py .\n",
"python -m pip install ." "python -m pip install ."
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "uZcqD4NLdnf4", "id": "uZcqD4NLdnf4"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"import matplotlib\n", "import matplotlib\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
...@@ -126,9 +114,7 @@ ...@@ -126,9 +114,7 @@
"from object_detection.builders import model_builder\n", "from object_detection.builders import model_builder\n",
"\n", "\n",
"%matplotlib inline" "%matplotlib inline"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -142,11 +128,13 @@ ...@@ -142,11 +128,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "-y9R0Xllefec", "id": "-y9R0Xllefec"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"def load_image_into_numpy_array(path):\n", "def load_image_into_numpy_array(path):\n",
" \"\"\"Load an image from file into a numpy array.\n", " \"\"\"Load an image from file into a numpy array.\n",
...@@ -156,7 +144,7 @@ ...@@ -156,7 +144,7 @@
" (height, width, channels), where channels=3 for RGB.\n", " (height, width, channels), where channels=3 for RGB.\n",
"\n", "\n",
" Args:\n", " Args:\n",
" path: a file path (this can be local or on colossus)\n", " path: a file path.\n",
"\n", "\n",
" Returns:\n", " Returns:\n",
" uint8 numpy array with shape (img_height, img_width, 3)\n", " uint8 numpy array with shape (img_height, img_width, 3)\n",
...@@ -171,7 +159,7 @@ ...@@ -171,7 +159,7 @@
" boxes,\n", " boxes,\n",
" classes,\n", " classes,\n",
" scores,\n", " scores,\n",
" category_index, \n", " category_index,\n",
" figsize=(12, 16),\n", " figsize=(12, 16),\n",
" image_name=None):\n", " image_name=None):\n",
" \"\"\"Wrapper function to visualize detections.\n", " \"\"\"Wrapper function to visualize detections.\n",
...@@ -186,6 +174,8 @@ ...@@ -186,6 +174,8 @@
" boxes and plot all boxes as black with no classes or scores.\n", " boxes and plot all boxes as black with no classes or scores.\n",
" category_index: a dict containing category dictionaries (each holding\n", " category_index: a dict containing category dictionaries (each holding\n",
" category index `id` and category name `name`) keyed by category indices.\n", " category index `id` and category name `name`) keyed by category indices.\n",
" figsize: size for the figure.\n",
" image_name: a name for the image file.\n",
" \"\"\"\n", " \"\"\"\n",
" image_np_with_annotations = image_np.copy()\n", " image_np_with_annotations = image_np.copy()\n",
" viz_utils.visualize_boxes_and_labels_on_image_array(\n", " viz_utils.visualize_boxes_and_labels_on_image_array(\n",
...@@ -196,13 +186,11 @@ ...@@ -196,13 +186,11 @@
" category_index,\n", " category_index,\n",
" use_normalized_coordinates=True,\n", " use_normalized_coordinates=True,\n",
" min_score_thresh=0.8)\n", " min_score_thresh=0.8)\n",
" if (image_name):\n", " if image_name:\n",
" plt.imsave(image_name, image_np_with_annotations)\n", " plt.imsave(image_name, image_np_with_annotations)\n",
" else:\n", " else:\n",
" plt.imshow(image_np_with_annotations)\n" " plt.imshow(image_np_with_annotations)\n"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -219,11 +207,13 @@ ...@@ -219,11 +207,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "SQy3ND7EpFQM", "id": "SQy3ND7EpFQM"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"# Load images and visualize\n", "# Load images and visualize\n",
"train_image_dir = 'models/research/object_detection/test_images/ducky/train/'\n", "train_image_dir = 'models/research/object_detection/test_images/ducky/train/'\n",
...@@ -242,12 +232,10 @@ ...@@ -242,12 +232,10 @@
"plt.rcParams['figure.figsize'] = [14, 7]\n", "plt.rcParams['figure.figsize'] = [14, 7]\n",
"\n", "\n",
"for idx, train_image_np in enumerate(train_images_np):\n", "for idx, train_image_np in enumerate(train_images_np):\n",
" plt.subplot(2,3, idx+1)\n", " plt.subplot(2, 3, idx+1)\n",
" plt.imshow(train_image_np)\n", " plt.imshow(train_image_np)\n",
"plt.show()" "plt.show()"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -266,17 +254,17 @@ ...@@ -266,17 +254,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "-nEDRoUEcUgL", "id": "-nEDRoUEcUgL"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"gt_boxes = []\n", "gt_boxes = []\n",
"colab_utils.annotate(train_images_np, box_storage_pointer=gt_boxes)" "colab_utils.annotate(train_images_np, box_storage_pointer=gt_boxes)"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -294,11 +282,13 @@ ...@@ -294,11 +282,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"id": "wIAT6ZUmdHOC", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "wIAT6ZUmdHOC"
}, },
"outputs": [],
"source": [ "source": [
"# gt_boxes = [\n", "# gt_boxes = [\n",
"# np.array([[0.436, 0.591, 0.629, 0.712]], dtype=np.float32),\n", "# np.array([[0.436, 0.591, 0.629, 0.712]], dtype=np.float32),\n",
...@@ -307,9 +297,7 @@ ...@@ -307,9 +297,7 @@
"# np.array([[0.313, 0.308, 0.648, 0.526]], dtype=np.float32),\n", "# np.array([[0.313, 0.308, 0.648, 0.526]], dtype=np.float32),\n",
"# np.array([[0.256, 0.444, 0.484, 0.629]], dtype=np.float32)\n", "# np.array([[0.256, 0.444, 0.484, 0.629]], dtype=np.float32)\n",
"# ]" "# ]"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -326,11 +314,13 @@ ...@@ -326,11 +314,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "HWBqFVMcweF-", "id": "HWBqFVMcweF-"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"\n", "\n",
"# By convention, our non-background classes start counting at 1. Given\n", "# By convention, our non-background classes start counting at 1. Given\n",
...@@ -360,9 +350,7 @@ ...@@ -360,9 +350,7 @@
" gt_classes_one_hot_tensors.append(tf.one_hot(\n", " gt_classes_one_hot_tensors.append(tf.one_hot(\n",
" zero_indexed_groundtruth_classes, num_classes))\n", " zero_indexed_groundtruth_classes, num_classes))\n",
"print('Done prepping data.')\n" "print('Done prepping data.')\n"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -376,26 +364,26 @@ ...@@ -376,26 +364,26 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "YBD6l-E4N71y", "id": "YBD6l-E4N71y"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"dummy_scores = np.array([1.0], dtype=np.float32) # give boxes a score of 100%\n", "dummy_scores = np.array([1.0], dtype=np.float32) # give boxes a score of 100%\n",
"\n", "\n",
"plt.figure(figsize=(30, 15))\n", "plt.figure(figsize=(30, 15))\n",
"for idx in range(5):\n", "for idx in range(5):\n",
" plt.subplot(2,3, idx+1)\n", " plt.subplot(2, 3, idx+1)\n",
" plot_detections(\n", " plot_detections(\n",
" train_images_np[idx],\n", " train_images_np[idx],\n",
" gt_boxes[idx],\n", " gt_boxes[idx],\n",
" np.ones(shape=[gt_boxes[idx].shape[0]], dtype=np.int32),\n", " np.ones(shape=[gt_boxes[idx].shape[0]], dtype=np.int32),\n",
" dummy_scores, category_index)\n", " dummy_scores, category_index)\n",
"plt.show()" "plt.show()"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -413,28 +401,30 @@ ...@@ -413,28 +401,30 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"id": "9J16r3NChD-7", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "9J16r3NChD-7"
}, },
"outputs": [],
"source": [ "source": [
"# Download the checkpoint and put it into models/research/object_detection/test_data/\n", "# Download the checkpoint and put it into models/research/object_detection/test_data/\n",
"\n", "\n",
"!wget http://download.tensorflow.org/models/object_detection/tf2/20200710/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.tar.gz\n", "!wget http://download.tensorflow.org/models/object_detection/tf2/20200710/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.tar.gz\n",
"!tar -xf ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.tar.gz\n", "!tar -xf ssd_resnet50_v1_fpn_640x640_coco17_tpu-8.tar.gz\n",
"!mv ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/checkpoint models/research/object_detection/test_data/" "!mv ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/checkpoint models/research/object_detection/test_data/"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "RyT4BUbaMeG-", "id": "RyT4BUbaMeG-"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"tf.keras.backend.clear_session()\n", "tf.keras.backend.clear_session()\n",
"\n", "\n",
...@@ -477,9 +467,7 @@ ...@@ -477,9 +467,7 @@
"prediction_dict = detection_model.predict(image, shapes)\n", "prediction_dict = detection_model.predict(image, shapes)\n",
"_ = detection_model.postprocess(prediction_dict, shapes)\n", "_ = detection_model.postprocess(prediction_dict, shapes)\n",
"print('Weights restored!')" "print('Weights restored!')"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -494,11 +482,13 @@ ...@@ -494,11 +482,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "nyHoF4mUrv5-", "id": "nyHoF4mUrv5-"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"tf.keras.backend.set_learning_phase(True)\n", "tf.keras.backend.set_learning_phase(True)\n",
"\n", "\n",
...@@ -588,9 +578,7 @@ ...@@ -588,9 +578,7 @@
" + ', loss=' + str(total_loss.numpy()), flush=True)\n", " + ', loss=' + str(total_loss.numpy()), flush=True)\n",
"\n", "\n",
"print('Done fine-tuning!')" "print('Done fine-tuning!')"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -604,11 +592,13 @@ ...@@ -604,11 +592,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "WcE6OwrHQJya", "id": "WcE6OwrHQJya"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"test_image_dir = 'models/research/object_detection/test_images/ducky/test/'\n", "test_image_dir = 'models/research/object_detection/test_images/ducky/test/'\n",
"test_images_np = []\n", "test_images_np = []\n",
...@@ -651,17 +641,17 @@ ...@@ -651,17 +641,17 @@
" + label_id_offset,\n", " + label_id_offset,\n",
" detections['detection_scores'][0].numpy(),\n", " detections['detection_scores'][0].numpy(),\n",
" category_index, figsize=(15, 20), image_name=\"gif_frame_\" + ('%02d' % i) + \".jpg\")" " category_index, figsize=(15, 20), image_name=\"gif_frame_\" + ('%02d' % i) + \".jpg\")"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"id": "RW1FrT2iNnpy", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "RW1FrT2iNnpy"
}, },
"outputs": [],
"source": [ "source": [
"imageio.plugins.freeimage.download()\n", "imageio.plugins.freeimage.download()\n",
"\n", "\n",
...@@ -671,16 +661,28 @@ ...@@ -671,16 +661,28 @@
"filenames = sorted(filenames)\n", "filenames = sorted(filenames)\n",
"last = -1\n", "last = -1\n",
"images = []\n", "images = []\n",
"for i,filename in enumerate(filenames):\n", "for filename in filenames:\n",
" image = imageio.imread(filename)\n", " image = imageio.imread(filename)\n",
" images.append(image)\n", " images.append(image)\n",
"\n", "\n",
"imageio.mimsave(anim_file, images, 'GIF-FI', fps=5)\n", "imageio.mimsave(anim_file, images, 'GIF-FI', fps=5)\n",
"\n", "\n",
"display(IPyImage(open(anim_file, 'rb').read()))" "display(IPyImage(open(anim_file, 'rb').read()))"
], ]
"execution_count": null, }
"outputs": [] ],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "interactive_eager_few_shot_od_training_colab.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
} }
] },
} "nbformat": 4,
\ No newline at end of file "nbformat_minor": 0
}
research/object_detection/colab_tutorials/inference_tf2_colab.ipynb
View file @ c9eb3554
{ {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "inference_tf2_colab.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [ "cells": [
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -37,24 +24,26 @@ ...@@ -37,24 +24,26 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"id": "LBZ9VWZZFUCT", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "LBZ9VWZZFUCT"
}, },
"outputs": [],
"source": [ "source": [
"!pip install -U --pre tensorflow==\"2.2.0\"" "!pip install -U --pre tensorflow==\"2.2.0\""
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"id": "oi28cqGGFWnY", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "oi28cqGGFWnY"
}, },
"outputs": [],
"source": [ "source": [
"import os\n", "import os\n",
"import pathlib\n", "import pathlib\n",
...@@ -65,17 +54,17 @@ ...@@ -65,17 +54,17 @@
" os.chdir('..')\n", " os.chdir('..')\n",
"elif not pathlib.Path('models').exists():\n", "elif not pathlib.Path('models').exists():\n",
" !git clone --depth 1 https://github.com/tensorflow/models" " !git clone --depth 1 https://github.com/tensorflow/models"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"id": "NwdsBdGhFanc", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "NwdsBdGhFanc"
}, },
"outputs": [],
"source": [ "source": [
"# Install the Object Detection API\n", "# Install the Object Detection API\n",
"%%bash\n", "%%bash\n",
...@@ -83,33 +72,33 @@ ...@@ -83,33 +72,33 @@
"protoc object_detection/protos/*.proto --python_out=.\n", "protoc object_detection/protos/*.proto --python_out=.\n",
"cp object_detection/packages/tf2/setup.py .\n", "cp object_detection/packages/tf2/setup.py .\n",
"python -m pip install ." "python -m pip install ."
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"id": "tNR8YgZVFhPm", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "tNR8YgZVFhPm"
}, },
"outputs": [],
"source": [ "source": [
"# Test the Object Detection API installation\n", "# Test the Object Detection API installation\n",
"%%bash\n", "%%bash\n",
"cd models/research\n", "cd models/research\n",
"python object_detection/builders/model_builder_tf2_test.py" "python object_detection/builders/model_builder_tf2_test.py"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "yn5_uV1HLvaz", "id": "yn5_uV1HLvaz"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"import matplotlib\n", "import matplotlib\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
...@@ -128,9 +117,7 @@ ...@@ -128,9 +117,7 @@
"from object_detection.builders import model_builder\n", "from object_detection.builders import model_builder\n",
"\n", "\n",
"%matplotlib inline" "%matplotlib inline"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -144,11 +131,13 @@ ...@@ -144,11 +131,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "-y9R0Xllefec", "id": "-y9R0Xllefec"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"def load_image_into_numpy_array(path):\n", "def load_image_into_numpy_array(path):\n",
" \"\"\"Load an image from file into a numpy array.\n", " \"\"\"Load an image from file into a numpy array.\n",
...@@ -158,7 +147,7 @@ ...@@ -158,7 +147,7 @@
" (height, width, channels), where channels=3 for RGB.\n", " (height, width, channels), where channels=3 for RGB.\n",
"\n", "\n",
" Args:\n", " Args:\n",
" path: a file path (this can be local or on colossus)\n", " path: a file path.\n",
"\n", "\n",
" Returns:\n", " Returns:\n",
" uint8 numpy array with shape (img_height, img_width, 3)\n", " uint8 numpy array with shape (img_height, img_width, 3)\n",
...@@ -183,26 +172,24 @@ ...@@ -183,26 +172,24 @@
" for edge in kp_list:\n", " for edge in kp_list:\n",
" tuple_list.append((edge.start, edge.end))\n", " tuple_list.append((edge.start, edge.end))\n",
" return tuple_list" " return tuple_list"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"id": "R4YjnOjME1gy", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "R4YjnOjME1gy"
}, },
"outputs": [],
"source": [ "source": [
"# @title Choose the model to use, then evaluate the cell.\n", "# @title Choose the model to use, then evaluate the cell.\n",
"MODELS = {'centernet_with_keypoints': 'center_net_resnet101_v1_fpn_512x512_kpts_coco17_tpu-8', 'centernet_without_keypoints': 'center_net_resnet101_v1_fpn_512x512_coco17_tpu-8'}\n", "MODELS = {'centernet_with_keypoints': 'center_net_resnet101_v1_fpn_512x512_kpts_coco17_tpu-8', 'centernet_without_keypoints': 'center_net_resnet101_v1_fpn_512x512_coco17_tpu-8'}\n",
"\n", "\n",
"model_display_name = 'centernet_with_keypoints' # @param ['centernet_with_keypoints', 'centernet_without_keypoints']\n", "model_display_name = 'centernet_with_keypoints' # @param ['centernet_with_keypoints', 'centernet_without_keypoints']\n",
"model_name = MODELS[model_display_name]" "model_name = MODELS[model_display_name]"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -218,24 +205,26 @@ ...@@ -218,24 +205,26 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"id": "ctPavqlyPuU_", "colab": {},
"colab_type": "code", "colab_type": "code",
"colab": {} "id": "ctPavqlyPuU_"
}, },
"outputs": [],
"source": [ "source": [
"# Download the checkpoint/ and put it into models/research/object_detection/test_data/" "# Download the checkpoint/ and put it into models/research/object_detection/test_data/"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "4cni4SSocvP_", "id": "4cni4SSocvP_"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"pipeline_config = os.path.join('models/research/object_detection/configs/tf2/',\n", "pipeline_config = os.path.join('models/research/object_detection/configs/tf2/',\n",
" model_name + '.config')\n", " model_name + '.config')\n",
...@@ -268,9 +257,7 @@ ...@@ -268,9 +257,7 @@
" return detect_fn\n", " return detect_fn\n",
"\n", "\n",
"detect_fn = get_model_detection_function(detection_model)" "detect_fn = get_model_detection_function(detection_model)"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -286,11 +273,13 @@ ...@@ -286,11 +273,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "5mucYUS6exUJ", "id": "5mucYUS6exUJ"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"label_map_path = configs['eval_input_config'].label_map_path\n", "label_map_path = configs['eval_input_config'].label_map_path\n",
"label_map = label_map_util.load_labelmap(label_map_path)\n", "label_map = label_map_util.load_labelmap(label_map_path)\n",
...@@ -300,9 +289,7 @@ ...@@ -300,9 +289,7 @@
" use_display_name=True)\n", " use_display_name=True)\n",
"category_index = label_map_util.create_category_index(categories)\n", "category_index = label_map_util.create_category_index(categories)\n",
"label_map_dict = label_map_util.get_label_map_dict(label_map, use_display_name=True)" "label_map_dict = label_map_util.get_label_map_dict(label_map, use_display_name=True)"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -328,11 +315,13 @@ ...@@ -328,11 +315,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "vr_Fux-gfaG9", "id": "vr_Fux-gfaG9"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"image_dir = 'models/research/object_detection/test_images/'\n", "image_dir = 'models/research/object_detection/test_images/'\n",
"image_path = os.path.join(image_dir, 'image2.jpg')\n", "image_path = os.path.join(image_dir, 'image2.jpg')\n",
...@@ -376,9 +365,7 @@ ...@@ -376,9 +365,7 @@
"plt.figure(figsize=(12,16))\n", "plt.figure(figsize=(12,16))\n",
"plt.imshow(image_np_with_detections)\n", "plt.imshow(image_np_with_detections)\n",
"plt.show()" "plt.show()"
], ]
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -396,11 +383,13 @@ ...@@ -396,11 +383,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"colab": {},
"colab_type": "code", "colab_type": "code",
"id": "xBgYgSGMhHVi", "id": "xBgYgSGMhHVi"
"colab": {}
}, },
"outputs": [],
"source": [ "source": [
"if detection_model.__class__.__name__ != 'CenterNetMetaArch':\n", "if detection_model.__class__.__name__ != 'CenterNetMetaArch':\n",
" raise AssertionError('The meta-architecture for this section '\n", " raise AssertionError('The meta-architecture for this section '\n",
...@@ -469,9 +458,20 @@ ...@@ -469,9 +458,20 @@
"plt.imshow(resized_heatmap_unpadded, alpha=0.7,vmin=0, vmax=160, cmap='viridis')\n", "plt.imshow(resized_heatmap_unpadded, alpha=0.7,vmin=0, vmax=160, cmap='viridis')\n",
"plt.title('Object center heatmap (class: ' + class_name + ')')\n", "plt.title('Object center heatmap (class: ' + class_name + ')')\n",
"plt.show()" "plt.show()"
], ]
"execution_count": null, }
"outputs": [] ],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "inference_tf2_colab.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
} }
] },
} "nbformat": 4,
\ No newline at end of file "nbformat_minor": 0
}
research/object_detection/colab_tutorials/object_detection_tutorial.ipynb
View file @ c9eb3554
...@@ -71,7 +71,7 @@ ...@@ -71,7 +71,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -95,7 +95,7 @@ ...@@ -95,7 +95,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -118,7 +118,7 @@ ...@@ -118,7 +118,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -149,7 +149,7 @@ ...@@ -149,7 +149,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -164,7 +164,7 @@ ...@@ -164,7 +164,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -189,7 +189,7 @@ ...@@ -189,7 +189,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -224,7 +224,7 @@ ...@@ -224,7 +224,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -249,7 +249,7 @@ ...@@ -249,7 +249,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -300,7 +300,7 @@ ...@@ -300,7 +300,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -319,7 +319,6 @@ ...@@ -319,7 +319,6 @@
" model_dir = pathlib.Path(model_dir)/\"saved_model\"\n", " model_dir = pathlib.Path(model_dir)/\"saved_model\"\n",
"\n", "\n",
" model = tf.saved_model.load(str(model_dir))\n", " model = tf.saved_model.load(str(model_dir))\n",
" model = model.signatures['serving_default']\n",
"\n", "\n",
" return model" " return model"
] ]
...@@ -337,7 +336,7 @@ ...@@ -337,7 +336,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -362,7 +361,7 @@ ...@@ -362,7 +361,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -398,7 +397,7 @@ ...@@ -398,7 +397,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -417,12 +416,12 @@ ...@@ -417,12 +416,12 @@
"id": "yN1AYfAEJIGp" "id": "yN1AYfAEJIGp"
}, },
"source": [ "source": [
"Check the model's input signature, it expects a batch of 3-color images of type uint8: " "Check the model's input signature, it expects a batch of 3-color images of type uint8:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -430,7 +429,7 @@ ...@@ -430,7 +429,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"print(detection_model.inputs)" "print(detection_model.signatures['serving_default'].inputs)"
] ]
}, },
{ {
...@@ -445,7 +444,7 @@ ...@@ -445,7 +444,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -453,12 +452,12 @@ ...@@ -453,12 +452,12 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"detection_model.output_dtypes" "detection_model.signatures['serving_default'].output_dtypes"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -466,7 +465,7 @@ ...@@ -466,7 +465,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"detection_model.output_shapes" "detection_model.signatures['serving_default'].output_shapes"
] ]
}, },
{ {
...@@ -481,7 +480,7 @@ ...@@ -481,7 +480,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -497,7 +496,8 @@ ...@@ -497,7 +496,8 @@
" input_tensor = input_tensor[tf.newaxis,...]\n", " input_tensor = input_tensor[tf.newaxis,...]\n",
"\n", "\n",
" # Run inference\n", " # Run inference\n",
" output_dict = model(input_tensor)\n", " model_fn = model.signatures['serving_default']\n",
" output_dict = model_fn(input_tensor)\n",
"\n", "\n",
" # All outputs are batches tensors.\n", " # All outputs are batches tensors.\n",
" # Convert to numpy arrays, and take index [0] to remove the batch dimension.\n", " # Convert to numpy arrays, and take index [0] to remove the batch dimension.\n",
...@@ -535,7 +535,7 @@ ...@@ -535,7 +535,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -565,7 +565,7 @@ ...@@ -565,7 +565,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -589,7 +589,7 @@ ...@@ -589,7 +589,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -613,7 +613,7 @@ ...@@ -613,7 +613,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -626,7 +626,7 @@ ...@@ -626,7 +626,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 0, "execution_count": null,
"metadata": { "metadata": {
"colab": {}, "colab": {},
"colab_type": "code", "colab_type": "code",
...@@ -637,19 +637,6 @@ ...@@ -637,19 +637,6 @@
"for image_path in TEST_IMAGE_PATHS:\n", "for image_path in TEST_IMAGE_PATHS:\n",
" show_inference(masking_model, image_path)" " show_inference(masking_model, image_path)"
] ]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "nLlmm9JojEKm"
},
"outputs": [],
"source": [
""
]
} }
], ],
"metadata": { "metadata": {
...@@ -663,6 +650,10 @@ ...@@ -663,6 +650,10 @@
"name": "object_detection_tutorial.ipynb", "name": "object_detection_tutorial.ipynb",
"private_outputs": true, "private_outputs": true,
"provenance": [ "provenance": [
{
"file_id": "/piper/depot/google3/third_party/tensorflow_models/object_detection/colab_tutorials/object_detection_tutorial.ipynb",
"timestamp": 1594335690840
},
{ {
"file_id": "1LNYL6Zsn9Xlil2CVNOTsgDZQSBKeOjCh", "file_id": "1LNYL6Zsn9Xlil2CVNOTsgDZQSBKeOjCh",
"timestamp": 1566498233247 "timestamp": 1566498233247
...@@ -699,8 +690,7 @@ ...@@ -699,8 +690,7 @@
"file_id": "https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb", "file_id": "https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb",
"timestamp": 1556150293326 "timestamp": 1556150293326
} }
], ]
"version": "0.3.2"
}, },
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
......
research/object_detection/dockerfiles/tf1/Dockerfile
View file @ c9eb3554
...@@ -25,20 +25,17 @@ RUN useradd -ms /bin/bash tensorflow ...@@ -25,20 +25,17 @@ RUN useradd -ms /bin/bash tensorflow
USER tensorflow USER tensorflow
WORKDIR /home/tensorflow WORKDIR /home/tensorflow
# Install pip dependencies
RUN pip3 install --user absl-py
RUN pip3 install --user contextlib2
RUN pip3 install --user Cython
RUN pip3 install --user jupyter
RUN pip3 install --user matplotlib
RUN pip3 install --user pycocotools
RUN pip3 install --user tf-slim
# Copy this version of of the model garden into the image # Copy this version of of the model garden into the image
COPY --chown=tensorflow . /home/tensorflow/models COPY --chown=tensorflow . /home/tensorflow/models
# Compile protobuf configs # Compile protobuf configs
RUN (cd /home/tensorflow/models/research/ && protoc object_detection/protos/*.proto --python_out=.) RUN (cd /home/tensorflow/models/research/ && protoc object_detection/protos/*.proto --python_out=.)
WORKDIR /home/tensorflow/models/research/
RUN cp object_detection/packages/tf1/setup.py ./
ENV PATH="/home/tensorflow/.local/bin:${PATH}"
RUN python -m pip install --user -U pip
RUN python -m pip install --user .
ENV PYTHONPATH $PYTHONPATH:/home/tensorflow/models/research/:/home/tensorflow/models/research/slim
ENV TF_CPP_MIN_LOG_LEVEL 3 ENV TF_CPP_MIN_LOG_LEVEL 3
research/object_detection/dockerfiles/tf1/README.md
View file @ c9eb3554
# Tensorflow Object Detection on Docker # TensorFlow Object Detection on Docker
These instructions are experimental. These instructions are experimental.
...@@ -6,6 +6,6 @@ These instructions are experimental. ...@@ -6,6 +6,6 @@ These instructions are experimental.
```bash ```bash
# From the root of the git repository # From the root of the git repository
docker build -f research/object_detection/dockerfiles/1.15/Dockerfile -t od . docker build -f research/object_detection/dockerfiles/tf1/Dockerfile -t od .
docker run -it od docker run -it od
``` ```
research/object_detection/dockerfiles/tf2/Dockerfile
View file @ c9eb3554
...@@ -25,20 +25,17 @@ RUN useradd -ms /bin/bash tensorflow ...@@ -25,20 +25,17 @@ RUN useradd -ms /bin/bash tensorflow
USER tensorflow USER tensorflow
WORKDIR /home/tensorflow WORKDIR /home/tensorflow
# Install pip dependencies
RUN pip3 install --user absl-py
RUN pip3 install --user contextlib2
RUN pip3 install --user Cython
RUN pip3 install --user jupyter
RUN pip3 install --user matplotlib
RUN pip3 install --user pycocotools
RUN pip3 install --user tf-slim
# Copy this version of of the model garden into the image # Copy this version of of the model garden into the image
COPY --chown=tensorflow . /home/tensorflow/models COPY --chown=tensorflow . /home/tensorflow/models
# Compile protobuf configs # Compile protobuf configs
RUN (cd /home/tensorflow/models/research/ && protoc object_detection/protos/*.proto --python_out=.) RUN (cd /home/tensorflow/models/research/ && protoc object_detection/protos/*.proto --python_out=.)
WORKDIR /home/tensorflow/models/research/
RUN cp object_detection/packages/tf2/setup.py ./
ENV PATH="/home/tensorflow/.local/bin:${PATH}"
RUN python -m pip install -U pip
RUN python -m pip install .
ENV PYTHONPATH $PYTHONPATH:/home/tensorflow/models/research/:/home/tensorflow/models/research/slim
ENV TF_CPP_MIN_LOG_LEVEL 3 ENV TF_CPP_MIN_LOG_LEVEL 3
research/object_detection/dockerfiles/tf2/README.md
View file @ c9eb3554
# Tensorflow Object Detection on Docker # TensorFlow Object Detection on Docker
These instructions are experimental. These instructions are experimental.
...@@ -6,6 +6,6 @@ These instructions are experimental. ...@@ -6,6 +6,6 @@ These instructions are experimental.
```bash ```bash
# From the root of the git repository # From the root of the git repository
docker build -f research/object_detection/dockerfiles/2.2/Dockerfile -t od . docker build -f research/object_detection/dockerfiles/tf2/Dockerfile -t od .
docker run -it od docker run -it od
``` ```
research/object_detection/model_lib_v2.py
View file @ c9eb3554
...@@ -784,7 +784,16 @@ def eager_eval_loop( ...@@ -784,7 +784,16 @@ def eager_eval_loop(
name='eval_side_by_side_' + str(i), name='eval_side_by_side_' + str(i),
step=global_step, step=global_step,
data=sbys_images, data=sbys_images,
max_outputs=1) max_outputs=eval_config.num_visualizations)
if eval_util.has_densepose(eval_dict):
dp_image_list = vutils.draw_densepose_visualizations(
eval_dict)
dp_images = tf.concat(dp_image_list, axis=0)
tf.compat.v2.summary.image(
name='densepose_detections_' + str(i),
step=global_step,
data=dp_images,
max_outputs=eval_config.num_visualizations)
if evaluators is None: if evaluators is None:
if class_agnostic: if class_agnostic:
......
research/object_detection/models/ssd_efficientnet_bifpn_feature_extractor.py
View file @ c9eb3554
...@@ -221,7 +221,7 @@ class SSDEfficientNetBiFPNKerasFeatureExtractor( ...@@ -221,7 +221,7 @@ class SSDEfficientNetBiFPNKerasFeatureExtractor(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple)) ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))
output_feature_map_dict = self._bifpn_stage( output_feature_map_dict = self._bifpn_stage(
zip(self._output_layer_alias, base_feature_maps)) list(zip(self._output_layer_alias, base_feature_maps)))
return list(output_feature_map_dict.values()) return list(output_feature_map_dict.values())
......
research/object_detection/protos/center_net.proto
View file @ c9eb3554
...@@ -183,6 +183,41 @@ message CenterNet { ...@@ -183,6 +183,41 @@ message CenterNet {
optional float heatmap_bias_init = 3 [default = -2.19]; optional float heatmap_bias_init = 3 [default = -2.19];
} }
optional MaskEstimation mask_estimation_task = 8; optional MaskEstimation mask_estimation_task = 8;
// Parameters which are related to DensePose estimation task.
// http://densepose.org/
message DensePoseEstimation {
// Weight of the task loss. The total loss of the model will be their
// summation of task losses weighted by the weights.
optional float task_loss_weight = 1 [default = 1.0];
// Class ID (0-indexed) that corresponds to the object in the label map that
// contains DensePose data.
optional int32 class_id = 2;
// Loss configuration for DensePose heatmap and regression losses. Note
// that the localization loss is used for surface coordinate losses and
// classification loss is used for part classification losses.
optional Loss loss = 3;
// The number of body parts.
optional int32 num_parts = 4 [default = 24];
// Loss weights for the two DensePose heads.
optional float part_loss_weight = 5 [default = 1.0];
optional float coordinate_loss_weight = 6 [default = 1.0];
// Whether to upsample the prediction feature maps back to the original
// input dimension prior to applying loss. This has the benefit of
// maintaining finer groundtruth location information.
optional bool upsample_to_input_res = 7 [default = true];
// The initial bias value of the convlution kernel of the class heatmap
// prediction head. -2.19 corresponds to predicting foreground with
// a probability of 0.1.
optional float heatmap_bias_init = 8 [default = -2.19];
}
optional DensePoseEstimation densepose_estimation_task = 9;
} }
message CenterNetFeatureExtractor { message CenterNetFeatureExtractor {
......
research/object_detection/utils/colab_utils.py
View file @ c9eb3554
# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved. # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
...@@ -12,59 +13,53 @@ ...@@ -12,59 +13,53 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
# ============================================================================== # ==============================================================================
"""Utils for colab tutorials located in object_detection/colab_tutorials/""" """Utils for colab tutorials located in object_detection/colab_tutorials/..."""
import os import base64
import random
import uuid
import io import io
import operator
import json import json
from typing import Dict
from typing import List
from typing import Union
import uuid
from IPython.display import display
from IPython.display import Javascript
import numpy as np
from PIL import Image
from typing import List, Dict, Union
from base64 import b64decode, b64encode
from IPython.display import display, Javascript
from google.colab import output from google.colab import output
from google.colab.output import eval_js from google.colab.output import eval_js
import tensorflow as tf
import numpy as np
from six import BytesIO
from PIL import Image, ImageDraw, ImageFont
def image_from_numpy(image): def image_from_numpy(image):
""" """Open an image at the specified path and encode it in Base64.
Open an image at the specified path and encode it in Base64.
Parameters Args:
---------- image: np.ndarray
image: np.ndarray
Image represented as a numpy array Image represented as a numpy array
Returns Returns:
------- An encoded Base64 representation of the image
str
Encoded Base64 representation of the image
""" """
with io.BytesIO() as output: with io.BytesIO() as img_output:
Image.fromarray(image).save(output, format="JPEG") Image.fromarray(image).save(img_output, format='JPEG')
data = output.getvalue() data = output.getvalue()
data = str(b64encode(data))[2:-1] data = str(base64.b64encode(data))[2:-1]
return data return data
def draw_bbox(image_urls, callbackId):
"""
Open the bounding box UI and send the results to a callback function.
Parameters def draw_bbox(image_urls, callbackId): # pylint: disable=invalid-name
---------- """Open the bounding box UI and send the results to a callback function.
image_urls: list[str | np.ndarray]
Args:
image_urls: list[str | np.ndarray]
List of locations from where to load the images from. If a np.ndarray is List of locations from where to load the images from. If a np.ndarray is
given, the array is interpretted as an image and sent to the frontend. If given, the array is interpretted as an image and sent to the frontend.
a str is given, the string is interpreted as a path and is read as a If a str is given, the string is interpreted as a path and is read as a
np.ndarray before being sent to the frontend. np.ndarray before being sent to the frontend.
callbackId: str callbackId: str
The ID for the callback function to send the bounding box results to The ID for the callback function to send the bounding box results to
when the user hits submit. when the user hits submit.
""" """
...@@ -126,7 +121,6 @@ def draw_bbox(image_urls, callbackId): ...@@ -126,7 +121,6 @@ def draw_bbox(image_urls, callbackId):
for (var i = 0; i < imgs.length; i++) { for (var i = 0; i < imgs.length; i++) {
allBoundingBoxes[i] = []; allBoundingBoxes[i] = [];
} }
//initialize image view //initialize image view
errorlog.id = 'errorlog'; errorlog.id = 'errorlog';
image.style.display = 'block'; image.style.display = 'block';
...@@ -175,7 +169,6 @@ def draw_bbox(image_urls, callbackId): ...@@ -175,7 +169,6 @@ def draw_bbox(image_urls, callbackId):
} }
resetcanvas(); resetcanvas();
} }
// on delete, deletes the last bounding box // on delete, deletes the last bounding box
deleteButton.textContent = "undo bbox"; deleteButton.textContent = "undo bbox";
deleteButton.onclick = function(){ deleteButton.onclick = function(){
...@@ -187,7 +180,6 @@ def draw_bbox(image_urls, callbackId): ...@@ -187,7 +180,6 @@ def draw_bbox(image_urls, callbackId):
boundingBoxes.map(r => {drawRect(r)}); boundingBoxes.map(r => {drawRect(r)});
}; };
} }
// on all delete, deletes all of the bounding box // on all delete, deletes all of the bounding box
deleteAllbutton.textContent = "delete all" deleteAllbutton.textContent = "delete all"
deleteAllbutton.onclick = function(){ deleteAllbutton.onclick = function(){
...@@ -239,7 +231,7 @@ def draw_bbox(image_urls, callbackId): ...@@ -239,7 +231,7 @@ def draw_bbox(image_urls, callbackId):
start = oMousePos(canvas_img, e); start = oMousePos(canvas_img, e);
// configure is drawing to true // configure is drawing to true
isDrawing = true; isDrawing = true;
} }
function handleMouseMove(e) { function handleMouseMove(e) {
...@@ -268,15 +260,15 @@ def draw_bbox(image_urls, callbackId): ...@@ -268,15 +260,15 @@ def draw_bbox(image_urls, callbackId):
box.x = o.x; box.x = o.x;
} }
else{ else{
box.x = o.x + o.w; box.x = o.x + o.w;
} }
if (o.h > 0){ if (o.h > 0){
box.y = o.y; box.y = o.y;
} }
else{ else{
box.y = o.y + o.h; box.y = o.y + o.h;
} }
box.w = Math.abs(o.w); box.w = Math.abs(o.w);
box.h = Math.abs(o.h); box.h = Math.abs(o.h);
// add the bounding box to the image // add the bounding box to the image
...@@ -285,7 +277,7 @@ def draw_bbox(image_urls, callbackId): ...@@ -285,7 +277,7 @@ def draw_bbox(image_urls, callbackId):
} }
} }
function draw() { function draw() {
o.x = (start.x)/image.width; // start position of x o.x = (start.x)/image.width; // start position of x
o.y = (start.y)/image.height; // start position of y o.y = (start.y)/image.height; // start position of y
o.w = (m.x - start.x)/image.width; // width o.w = (m.x - start.x)/image.width; // width
...@@ -296,7 +288,7 @@ def draw_bbox(image_urls, callbackId): ...@@ -296,7 +288,7 @@ def draw_bbox(image_urls, callbackId):
// draw all the rectangles saved in the rectsRy // draw all the rectangles saved in the rectsRy
boundingBoxes.map(r => {drawRect(r)}); boundingBoxes.map(r => {drawRect(r)});
// draw the actual rectangle // draw the actual rectangle
drawRect(o); drawRect(o);
} }
// add the handlers needed for dragging // add the handlers needed for dragging
...@@ -312,7 +304,7 @@ def draw_bbox(image_urls, callbackId): ...@@ -312,7 +304,7 @@ def draw_bbox(image_urls, callbackId):
img = imgs[curr_image] img = imgs[curr_image]
image.src = "data:image/png;base64," + img; image.src = "data:image/png;base64," + img;
// onload init new canvas and display image // onload init new canvas and display image
image.onload = function() { image.onload = function() {
// normalize display height and canvas // normalize display height and canvas
image.height = height; image.height = height;
...@@ -331,7 +323,7 @@ def draw_bbox(image_urls, callbackId): ...@@ -331,7 +323,7 @@ def draw_bbox(image_urls, callbackId):
} }
function drawRect(o){ function drawRect(o){
// draw a predefined rectangle // draw a predefined rectangle
ctx.strokeStyle = "red"; ctx.strokeStyle = "red";
ctx.lineWidth = 2; ctx.lineWidth = 2;
ctx.beginPath(o); ctx.beginPath(o);
...@@ -342,7 +334,7 @@ def draw_bbox(image_urls, callbackId): ...@@ -342,7 +334,7 @@ def draw_bbox(image_urls, callbackId):
// Function to detect the mouse position // Function to detect the mouse position
function oMousePos(canvas_img, evt) { function oMousePos(canvas_img, evt) {
let ClientRect = canvas_img.getBoundingClientRect(); let ClientRect = canvas_img.getBoundingClientRect();
return { return {
x: evt.clientX - ClientRect.left, x: evt.clientX - ClientRect.left,
y: evt.clientY - ClientRect.top y: evt.clientY - ClientRect.top
}; };
...@@ -370,46 +362,45 @@ def draw_bbox(image_urls, callbackId): ...@@ -370,46 +362,45 @@ def draw_bbox(image_urls, callbackId):
document.querySelector("#output-area").appendChild(div); document.querySelector("#output-area").appendChild(div);
return return
}''') }''')
#load the images as a byte array # load the images as a byte array
bytearrays = [] bytearrays = []
for image in image_urls: for image in image_urls:
if isinstance(image, str): if isinstance(image, np.ndarray):
bytearrays.append(image_from_path(image)) bytearrays.append(image_from_numpy(image))
elif isinstance(image, np.ndarray): else:
bytearrays.append(image_from_numpy(image)) raise TypeError('Image has unsupported type {}.'.format(type(image)))
else:
raise TypeError(f"Image has unsupported type {type(image)}. Only str and np.ndarray are supported.")
#format arrays for input # format arrays for input
image_data = json.dumps(bytearrays) image_data = json.dumps(bytearrays)
del bytearrays del bytearrays
#call java script function pass string byte array(image_data) as input # call java script function pass string byte array(image_data) as input
display(js) display(js)
eval_js(f"load_image({image_data}, '{callbackId}')") eval_js('load_image({}, \'{}\')'.format(image_data, callbackId))
return return
def annotate(imgs: List[Union[str, np.ndarray]], box_storage_pointer: List[np.ndarray], callbackId: str = None):
"""
Open the bounding box UI and prompt the user for input.
Parameters def annotate(imgs: List[Union[str, np.ndarray]], # pylint: disable=invalid-name
---------- box_storage_pointer: List[np.ndarray],
imgs: list[str | np.ndarray] callbackId: str = None):
"""Open the bounding box UI and prompt the user for input.
Args:
imgs: list[str | np.ndarray]
List of locations from where to load the images from. If a np.ndarray is List of locations from where to load the images from. If a np.ndarray is
given, the array is interpretted as an image and sent to the frontend. If given, the array is interpretted as an image and sent to the frontend. If
a str is given, the string is interpreted as a path and is read as a a str is given, the string is interpreted as a path and is read as a
np.ndarray before being sent to the frontend. np.ndarray before being sent to the frontend.
box_storage_pointer: list[np.ndarray] box_storage_pointer: list[np.ndarray]
Destination list for bounding box arrays. Each array in this list Destination list for bounding box arrays. Each array in this list
corresponds to one of the images given in imgs. The array is a corresponds to one of the images given in imgs. The array is a
N x 4 array where N is the number of bounding boxes given by the user N x 4 array where N is the number of bounding boxes given by the user
for that particular image. If there are no bounding boxes for an image, for that particular image. If there are no bounding boxes for an image,
None is used instead of an empty array. None is used instead of an empty array.
callbackId: str, optional callbackId: str, optional
The ID for the callback function that communicates between the fontend The ID for the callback function that communicates between the fontend
and the backend. If no ID is given, a random UUID string is used instead. and the backend. If no ID is given, a random UUID string is used instead.
""" """
...@@ -418,23 +409,30 @@ def annotate(imgs: List[Union[str, np.ndarray]], box_storage_pointer: List[np.nd ...@@ -418,23 +409,30 @@ def annotate(imgs: List[Union[str, np.ndarray]], box_storage_pointer: List[np.nd
if callbackId is None: if callbackId is None:
callbackId = str(uuid.uuid1()).replace('-', '') callbackId = str(uuid.uuid1()).replace('-', '')
def dictToList(input): def dictToList(input_bbox): # pylint: disable=invalid-name
''' """Convert bbox.
This function converts the dictionary from the frontend (if the format This function converts the dictionary from the frontend (if the format
{x, y, w, h} as shown in callbackFunction) into a list {x, y, w, h} as shown in callbackFunction) into a list
([y_min, x_min, y_max, x_max]) ([y_min, x_min, y_max, x_max])
'''
return (input['y'], input['x'], input['y'] + input['h'], input['x'] + input['w']) Args:
input_bbox:
def callbackFunction(annotations: List[List[Dict[str, float]]]): Returns:
A list with bbox coordinates in the form [ymin, xmin, ymax, xmax].
""" """
return (input_bbox['y'], input_bbox['x'], input_bbox['y'] + input_bbox['h'],
input_bbox['x'] + input_bbox['w'])
def callbackFunction(annotations: List[List[Dict[str, float]]]): # pylint: disable=invalid-name
"""Callback function.
This is the call back function to capture the data from the frontend and This is the call back function to capture the data from the frontend and
convert the data into a numpy array. convert the data into a numpy array.
Parameters Args:
---------- annotations: list[list[dict[str, float]]]
annotations: list[list[dict[str, float]]]
The input of the call back function is a list of list of objects The input of the call back function is a list of list of objects
corresponding to the annotations. The format of annotations is shown corresponding to the annotations. The format of annotations is shown
below below
...@@ -466,16 +464,17 @@ def annotate(imgs: List[Union[str, np.ndarray]], box_storage_pointer: List[np.nd ...@@ -466,16 +464,17 @@ def annotate(imgs: List[Union[str, np.ndarray]], box_storage_pointer: List[np.nd
boxes.clear() boxes.clear()
# load the new annotations into the boxes list # load the new annotations into the boxes list
for annotationsPerImg in annotations: for annotations_per_img in annotations:
rectanglesAsArrays = [np.clip(dictToList(annotation), 0, 1) for annotation in annotationsPerImg] rectangles_as_arrays = [np.clip(dictToList(annotation), 0, 1)
if rectanglesAsArrays: for annotation in annotations_per_img]
boxes.append(np.stack(rectanglesAsArrays)) if rectangles_as_arrays:
boxes.append(np.stack(rectangles_as_arrays))
else: else:
boxes.append(None) boxes.append(None)
# output the annotations to the errorlog # output the annotations to the errorlog
with output.redirect_to_element('#errorlog'): with output.redirect_to_element('#errorlog'):
display("--boxes array populated--") display('--boxes array populated--')
output.register_callback(callbackId, callbackFunction) output.register_callback(callbackId, callbackFunction)
draw_bbox(imgs, callbackId) draw_bbox(imgs, callbackId)
\ No newline at end of file
research/object_detection/utils/visualization_utils.py
View file @ c9eb3554
...@@ -790,6 +790,81 @@ def draw_side_by_side_evaluation_image(eval_dict, ...@@ -790,6 +790,81 @@ def draw_side_by_side_evaluation_image(eval_dict,
return images_with_detections_list return images_with_detections_list
def draw_densepose_visualizations(eval_dict,
max_boxes_to_draw=20,
min_score_thresh=0.2,
num_parts=24,
dp_coord_to_visualize=0):
"""Draws DensePose visualizations.
Args:
eval_dict: The evaluation dictionary returned by
eval_util.result_dict_for_batched_example().
max_boxes_to_draw: The maximum number of boxes to draw for detections.
min_score_thresh: The minimum score threshold for showing detections.
num_parts: The number of different densepose parts.
dp_coord_to_visualize: Whether to visualize v-coordinates (0) or
u-coordinates (0) overlaid on the person masks.
Returns:
A list of [1, H, W, C] uint8 tensor, each element corresponding to an image
in the batch.
Raises:
ValueError: If `dp_coord_to_visualize` is not 0 or 1.
"""
if dp_coord_to_visualize not in (0, 1):
raise ValueError('`dp_coord_to_visualize` must be either 0 for v '
'coordinates), or 1 for u coordinates, but instead got '
'{}'.format(dp_coord_to_visualize))
detection_fields = fields.DetectionResultFields()
input_data_fields = fields.InputDataFields()
if detection_fields.detection_masks not in eval_dict:
raise ValueError('Expected `detection_masks` in `eval_dict`.')
if detection_fields.detection_surface_coords not in eval_dict:
raise ValueError('Expected `detection_surface_coords` in `eval_dict`.')
images_with_detections_list = []
for indx in range(eval_dict[input_data_fields.original_image].shape[0]):
# Note that detection masks have already been resized to the original image
# shapes, but `original_image` has not.
# TODO(ronnyvotel): Consider resizing `original_image` in
# eval_util.result_dict_for_batched_example().
true_shape = eval_dict[input_data_fields.true_image_shape][indx]
original_shape = eval_dict[
input_data_fields.original_image_spatial_shape][indx]
image = eval_dict[input_data_fields.original_image][indx]
image = shape_utils.pad_or_clip_nd(image, [true_shape[0], true_shape[1], 3])
image = _resize_original_image(image, original_shape)
scores = eval_dict[detection_fields.detection_scores][indx]
detection_masks = eval_dict[detection_fields.detection_masks][indx]
surface_coords = eval_dict[detection_fields.detection_surface_coords][indx]
def draw_densepose_py_func(image, detection_masks, surface_coords, scores):
"""Overlays part masks and surface coords on original images."""
surface_coord_image = np.copy(image)
for i, (score, surface_coord, mask) in enumerate(
zip(scores, surface_coords, detection_masks)):
if i == max_boxes_to_draw:
break
if score > min_score_thresh:
draw_part_mask_on_image_array(image, mask, num_parts=num_parts)
draw_float_channel_on_image_array(
surface_coord_image, surface_coord[:, :, dp_coord_to_visualize],
mask)
return np.concatenate([image, surface_coord_image], axis=1)
image_with_densepose = tf.py_func(
draw_densepose_py_func,
[image, detection_masks, surface_coords, scores],
tf.uint8)
images_with_detections_list.append(
image_with_densepose[tf.newaxis, :, :, :])
return images_with_detections_list
def draw_keypoints_on_image_array(image, def draw_keypoints_on_image_array(image,
keypoints, keypoints,
keypoint_scores=None, keypoint_scores=None,
...@@ -918,8 +993,6 @@ def draw_mask_on_image_array(image, mask, color='red', alpha=0.4): ...@@ -918,8 +993,6 @@ def draw_mask_on_image_array(image, mask, color='red', alpha=0.4):
raise ValueError('`image` not of type np.uint8') raise ValueError('`image` not of type np.uint8')
if mask.dtype != np.uint8: if mask.dtype != np.uint8:
raise ValueError('`mask` not of type np.uint8') raise ValueError('`mask` not of type np.uint8')
if np.any(np.logical_and(mask != 1, mask != 0)):
raise ValueError('`mask` elements should be in [0, 1]')
if image.shape[:2] != mask.shape: if image.shape[:2] != mask.shape:
raise ValueError('The image has spatial dimensions %s but the mask has ' raise ValueError('The image has spatial dimensions %s but the mask has '
'dimensions %s' % (image.shape[:2], mask.shape)) 'dimensions %s' % (image.shape[:2], mask.shape))
...@@ -929,11 +1002,85 @@ def draw_mask_on_image_array(image, mask, color='red', alpha=0.4): ...@@ -929,11 +1002,85 @@ def draw_mask_on_image_array(image, mask, color='red', alpha=0.4):
solid_color = np.expand_dims( solid_color = np.expand_dims(
np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3]) np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3])
pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA') pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA')
pil_mask = Image.fromarray(np.uint8(255.0*alpha*mask)).convert('L') pil_mask = Image.fromarray(np.uint8(255.0*alpha*(mask > 0))).convert('L')
pil_image = Image.composite(pil_solid_color, pil_image, pil_mask) pil_image = Image.composite(pil_solid_color, pil_image, pil_mask)
np.copyto(image, np.array(pil_image.convert('RGB'))) np.copyto(image, np.array(pil_image.convert('RGB')))
def draw_part_mask_on_image_array(image, mask, alpha=0.4, num_parts=24):
"""Draws part mask on an image.
Args:
image: uint8 numpy array with shape (img_height, img_height, 3)
mask: a uint8 numpy array of shape (img_height, img_height) with
1-indexed parts (0 for background).
alpha: transparency value between 0 and 1 (default: 0.4)
num_parts: the maximum number of parts that may exist in the image (default
24 for DensePose).
Raises:
ValueError: On incorrect data type for image or masks.
"""
if image.dtype != np.uint8:
raise ValueError('`image` not of type np.uint8')
if mask.dtype != np.uint8:
raise ValueError('`mask` not of type np.uint8')
if image.shape[:2] != mask.shape:
raise ValueError('The image has spatial dimensions %s but the mask has '
'dimensions %s' % (image.shape[:2], mask.shape))
pil_image = Image.fromarray(image)
part_colors = np.zeros_like(image)
mask_1_channel = mask[:, :, np.newaxis]
for i, color in enumerate(STANDARD_COLORS[:num_parts]):
rgb = np.array(ImageColor.getrgb(color), dtype=np.uint8)
part_colors += (mask_1_channel == i + 1) * rgb[np.newaxis, np.newaxis, :]
pil_part_colors = Image.fromarray(np.uint8(part_colors)).convert('RGBA')
pil_mask = Image.fromarray(np.uint8(255.0 * alpha * (mask > 0))).convert('L')
pil_image = Image.composite(pil_part_colors, pil_image, pil_mask)
np.copyto(image, np.array(pil_image.convert('RGB')))
def draw_float_channel_on_image_array(image, channel, mask, alpha=0.9,
cmap='YlGn'):
"""Draws a floating point channel on an image array.
Args:
image: uint8 numpy array with shape (img_height, img_height, 3)
channel: float32 numpy array with shape (img_height, img_height). The values
should be in the range [0, 1], and will be mapped to colors using the
provided colormap `cmap` argument.
mask: a uint8 numpy array of shape (img_height, img_height) with
1-indexed parts (0 for background).
alpha: transparency value between 0 and 1 (default: 0.9)
cmap: string with the colormap to use.
Raises:
ValueError: On incorrect data type for image or masks.
"""
if image.dtype != np.uint8:
raise ValueError('`image` not of type np.uint8')
if channel.dtype != np.float32:
raise ValueError('`channel` not of type np.float32')
if mask.dtype != np.uint8:
raise ValueError('`mask` not of type np.uint8')
if image.shape[:2] != channel.shape:
raise ValueError('The image has spatial dimensions %s but the channel has '
'dimensions %s' % (image.shape[:2], channel.shape))
if image.shape[:2] != mask.shape:
raise ValueError('The image has spatial dimensions %s but the mask has '
'dimensions %s' % (image.shape[:2], mask.shape))
cm = plt.get_cmap(cmap)
pil_image = Image.fromarray(image)
colored_channel = cm(channel)[:, :, :3]
pil_colored_channel = Image.fromarray(
np.uint8(colored_channel * 255)).convert('RGBA')
pil_mask = Image.fromarray(np.uint8(255.0 * alpha * (mask > 0))).convert('L')
pil_image = Image.composite(pil_colored_channel, pil_image, pil_mask)
np.copyto(image, np.array(pil_image.convert('RGB')))
def visualize_boxes_and_labels_on_image_array( def visualize_boxes_and_labels_on_image_array(
image, image,
boxes, boxes,
...@@ -973,8 +1120,8 @@ def visualize_boxes_and_labels_on_image_array( ...@@ -973,8 +1120,8 @@ def visualize_boxes_and_labels_on_image_array(
boxes and plot all boxes as black with no classes or scores. boxes and plot all boxes as black with no classes or scores.
category_index: a dict containing category dictionaries (each holding category_index: a dict containing category dictionaries (each holding
category index `id` and category name `name`) keyed by category indices. category index `id` and category name `name`) keyed by category indices.
instance_masks: a numpy array of shape [N, image_height, image_width] with instance_masks: a uint8 numpy array of shape [N, image_height, image_width],
values ranging between 0 and 1, can be None. can be None.
instance_boundaries: a numpy array of shape [N, image_height, image_width] instance_boundaries: a numpy array of shape [N, image_height, image_width]
with values ranging between 0 and 1, can be None. with values ranging between 0 and 1, can be None.
keypoints: a numpy array of shape [N, num_keypoints, 2], can keypoints: a numpy array of shape [N, num_keypoints, 2], can
......
research/object_detection/utils/visualization_utils_test.py
View file @ c9eb3554
...@@ -373,6 +373,38 @@ class VisualizationUtilsTest(test_case.TestCase): ...@@ -373,6 +373,38 @@ class VisualizationUtilsTest(test_case.TestCase):
color='Blue', alpha=.5) color='Blue', alpha=.5)
self.assertAllEqual(test_image, expected_result) self.assertAllEqual(test_image, expected_result)
def test_draw_part_mask_on_image_array(self):
test_image = np.asarray([[[0, 0, 0], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0]]], dtype=np.uint8)
mask = np.asarray([[0, 1],
[1, 6]], dtype=np.uint8)
visualization_utils.draw_part_mask_on_image_array(test_image, mask,
alpha=.5)
self.assertAllEqual([0, 0, 0], test_image[0, 0])
self.assertAllGreater(test_image[0, 1], 0)
self.assertAllGreater(test_image[1, 0], 0)
self.assertAllGreater(test_image[1, 1], 0)
self.assertAllEqual(test_image[0, 1], test_image[1, 0])
def test_draw_float_channel_on_image_array(self):
test_image = np.asarray([[[0, 0, 0], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0]]], dtype=np.uint8)
channel = np.asarray([[0., 0.5],
[0., 1.]], dtype=np.float32)
mask = np.asarray([[0, 1],
[1, 1]], dtype=np.uint8)
# The colormap ('bwr') maps the values as follows:
# 0.0 -> Blue
# 0.5 -> White
# 1.0 -> Red
visualization_utils.draw_float_channel_on_image_array(
test_image, channel, mask, alpha=1.0, cmap='bwr')
expected_result = np.asarray([[[0, 0, 0], [255, 254, 254]],
[[0, 0, 255], [255, 0, 0]]], dtype=np.uint8)
self.assertAllEqual(test_image, expected_result)
def test_draw_heatmaps_on_image(self): def test_draw_heatmaps_on_image(self):
test_image = self.create_colorful_test_image() test_image = self.create_colorful_test_image()
test_image = Image.fromarray(test_image) test_image = Image.fromarray(test_image)
......
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