Merge pull request tensorflow#890 from lambdaflow:the300wlp

PiperOrigin-RevId: 264830499

setup.py

@@ -117,6 +117,7 @@
     'librispeech': ['pydub'],  # and ffmpeg installed
     'pet_finder': ['pandas'],
     'svhn': ['scipy'],
+    'the300w_lp': ['scipy'],
     'wikipedia': ['mwparserfromhell', 'apache_beam'],
 }
 

tensorflow_datasets/image/__init__.py

@@ -77,6 +77,7 @@
 from tensorflow_datasets.image.stanford_online_products import StanfordOnlineProducts
 from tensorflow_datasets.image.sun import Sun397
 from tensorflow_datasets.image.svhn import SvhnCropped
+from tensorflow_datasets.image.the300w_lp import The300wLp
 from tensorflow_datasets.image.uc_merced import UcMerced
 from tensorflow_datasets.image.visual_domain_decathlon import VisualDomainDecathlon
 from tensorflow_datasets.image.voc import Voc2007

tensorflow_datasets/image/the300w_lp.py

@@ -0,0 +1,152 @@
+# coding=utf-8
+# Copyright 2019 The TensorFlow Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""300W-LP Dataset."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import numpy as np
+import tensorflow as tf
+import tensorflow_datasets.public_api as tfds
+
+_DATASET_URL = "https://drive.google.com/uc?export=download&id=0B7OEHD3T4eCkVGs0TkhUWFN6N1k"
+
+_PROJECT_URL = "http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm"
+
+_CITATION = """\
+@article{DBLP:journals/corr/ZhuLLSL15,
+  author    = {Xiangyu Zhu and
+               Zhen Lei and
+               Xiaoming Liu and
+               Hailin Shi and
+               Stan Z. Li},
+  title     = {Face Alignment Across Large Poses: {A} 3D Solution},
+  journal   = {CoRR},
+  volume    = {abs/1511.07212},
+  year      = {2015},
+  url       = {http://arxiv.org/abs/1511.07212},
+  archivePrefix = {arXiv},
+  eprint    = {1511.07212},
+  timestamp = {Mon, 13 Aug 2018 16:48:23 +0200},
+  biburl    = {https://dblp.org/rec/bib/journals/corr/ZhuLLSL15},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DESCRIPTION = """\
+300W-LP Dataset is expanded from 300W, which standardises multiple alignment \
+databases with 68 landmarks, including AFW, LFPW, HELEN, IBUG and XM2VTS. With \
+300W, 300W-LP adopt the proposed face profiling to generate 61,225 samples \
+across large poses (1,786 from IBUG, 5,207 from AFW, 16,556 from LFPW and \
+37,676 from HELEN, XM2VTS is not used).
+
+The dataset can be employed as the training set for the following computer \
+vision tasks: face attribute recognition and landmark (or facial part) \
+locaization.
+"""
+
+
+class The300wLp(tfds.core.GeneratorBasedBuilder):
+  """300W-LP dataset."""
+
+  VERSION = tfds.core.Version("1.0.0")
+
+  def _info(self):
+    return tfds.core.DatasetInfo(
+        builder=self,
+        description=_DESCRIPTION,
+        features=tfds.features.FeaturesDict({
+            "image":
+                tfds.features.Image(
+                    shape=(450, 450, 3), encoding_format="jpeg"),
+            "landmarks_origin":
+                tfds.features.Tensor(shape=(68, 2), dtype=tf.float32),
+            "landmarks_2d":
+                tfds.features.Tensor(shape=(68, 2), dtype=tf.float32),
+            "landmarks_3d":
+                tfds.features.Tensor(shape=(68, 2), dtype=tf.float32),
+            "roi":
+                tfds.features.Tensor(shape=(4,), dtype=tf.float32),
+            "illum_params":
+                tfds.features.Tensor(shape=(10,), dtype=tf.float32),
+            "color_params":
+                tfds.features.Tensor(shape=(7,), dtype=tf.float32),
+            "tex_params":
+                tfds.features.Tensor(shape=(199,), dtype=tf.float32),
+            "shape_params":
+                tfds.features.Tensor(shape=(199,), dtype=tf.float32),
+            "exp_params":
+                tfds.features.Tensor(shape=(29,), dtype=tf.float32),
+            "pose_params":
+                tfds.features.Tensor(shape=(7,), dtype=tf.float32)
+        }),
+        urls=[_PROJECT_URL],
+        citation=_CITATION,
+    )
+
+  def _split_generators(self, dl_manager):
+    """Returns SplitGenerators."""
+    extracted_path = dl_manager.download_and_extract(_DATASET_URL)
+    return [
+        tfds.core.SplitGenerator(
+            name=tfds.Split.TRAIN,
+            gen_kwargs={
+                "image_dir_path": os.path.join(extracted_path, "300W_LP"),
+            }),
+    ]
+
+  def _generate_examples(self, image_dir_path):
+    """Yields examples."""
+    image_files = tf.io.gfile.glob(
+        pattern=os.path.join(image_dir_path, "[!Code]*[!_Flip]/[!_]*.jpg"))
+    label_files = [s.replace("jpg", "mat") for s in image_files]
+    landmark_files = [
+        s.replace("300W_LP", "300W_LP/landmarks").replace(".jpg", "_pts.mat")
+        for s in image_files
+    ]
+    for image_file, label_file, landmark_file in zip(image_files, label_files,
+                                                     landmark_files):
+      with tf.io.gfile.GFile(label_file, "rb") as f:
+        mat = tfds.core.lazy_imports.scipy.io.loadmat(f)
+      pt2d_origin = mat["pt2d"].T
+      pt2d_origin = (pt2d_origin / 450.0).astype(np.float32)
+      roi = mat["roi"].reshape(4).astype(np.float32)
+      illum_params = mat["Illum_Para"].reshape([-1]).astype(np.float32)
+      color_params = mat["Color_Para"].reshape([-1]).astype(np.float32)
+      tex_params = mat["Tex_Para"].reshape([-1]).astype(np.float32)
+      shape_params = mat["Shape_Para"].reshape([-1]).astype(np.float32)
+      exp_params = mat["Exp_Para"].reshape([-1]).astype(np.float32)
+      pose_params = mat["Pose_Para"].reshape([-1]).astype(np.float32)
+      with tf.io.gfile.GFile(landmark_file, "rb") as f:
+        ldm_mat = tfds.core.lazy_imports.scipy.io.loadmat(f)
+        pt2d = (ldm_mat["pts_2d"] / 450.0).astype(np.float32)
+        pt3d = (ldm_mat["pts_3d"] / 450.0).astype(np.float32)
+      record = {
+          "image": image_file,
+          "landmarks_origin": pt2d_origin,
+          "landmarks_2d": pt2d,
+          "landmarks_3d": pt3d,
+          "roi": roi,
+          "illum_params": illum_params,
+          "color_params": color_params,
+          "tex_params": tex_params,
+          "shape_params": shape_params,
+          "exp_params": exp_params,
+          "pose_params": pose_params
+      }
+      yield os.path.basename(image_file), record

tensorflow_datasets/image/the300w_lp_test.py

@@ -0,0 +1,47 @@
+# coding=utf-8
+# Copyright 2019 The TensorFlow Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Copyright 2019 The TensorFlow Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for 300W-LP dataset module."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow_datasets import testing
+from tensorflow_datasets.image import the300w_lp
+
+
+class The300wLpTest(testing.DatasetBuilderTestCase):
+  DATASET_CLASS = the300w_lp.The300wLp
+  SPLITS = {
+      "train": 2,
+  }
+
+
+if __name__ == "__main__":
+  testing.test_main()

tensorflow_datasets/url_checksums/the300w_lp.txt

@@ -0,0 +1 @@
+https://drive.google.com/uc?export=download&id=0B7OEHD3T4eCkVGs0TkhUWFN6N1k 2828125606 250c366c417ad48f72522b629ff83dbb2b2a57945803ad4a530a696bb6b13ee7