:recycle: pillow go to a soft requirements (e3ebecd7) · Commits · 钟慕尧 / deepchem

README.md

+7 −4

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		@@ -36,7 +36,6 @@ DeepChem requires these packages on any condition.

		- [joblib](https://pypi.python.org/pypi/joblib)
		- [pandas](http://pandas.pydata.org/)
		- [Pillow](https://pypi.org/project/Pillow/)
		- [scikit-learn](https://github.com/scikit-learn/scikit-learn.git)
		- [tensorflow](https://www.tensorflow.org/)
		- `deepchem>=2.4.0` requires tensorflow v2
		@@ -46,9 +45,13 @@ DeepChem requires these packages on any condition.

		DeepChem has a number of "soft" requirements. These are packages which are needed for various submodules of DeepChem but not for the package as a whole.

		- [RDKit](http://www.rdkit.org/docs/Install.html)
		- [MDTraj](http://mdtraj.org/)
		- [NetworkX](https://networkx.github.io/documentation/stable/index.html)
		- [PDBFixer](https://github.com/pandegroup/pdbfixer)
		- [Pillow](https://pypi.org/project/Pillow/)
		- [RDKit](http://www.rdkit.org/docs/Install.html)
		- [simdna](https://github.com/kundajelab/simdna)
		- [XGBoost](https://xgboost.readthedocs.io/en/latest/)

		## Installation

		@@ -72,13 +75,13 @@ However, pip installation is under development, so this command may not work wel
		`deepchem>=2.4.0`

		```bash
		pip install joblib pandas pillow scikit-learn tensorflow deepchem
		pip install joblib pandas scikit-learn tensorflow deepchem
		```

		`deepchem<2.4.0`

		```bash
		pip install joblib pandas pillow scikit-learn==0.22.1 tensorflow==0.1.15 deepchem==2.3.0
		pip install joblib pandas pillow scikit-learn==0.22.1 tensorflow(tensorflow-gpu)==1.15 deepchem==2.2.1.dev54
		```

		### Install from source

deepchem/data/data_loader.py

+1 −1

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		@@ -18,7 +18,6 @@ from deepchem.utils.genomics import encode_fasta_sequence
		from deepchem.feat import UserDefinedFeaturizer
		from deepchem.data import DiskDataset, NumpyDataset, ImageDataset
		import zipfile
		from PIL import Image

		logger = logging.getLogger(__name__)

		@@ -625,6 +624,7 @@ class ImageLoader(DataLoader):

		@staticmethod
		def load_img(image_files):
		from PIL import Image
		images = []
		for image_file in image_files:
		_, extension = os.path.splitext(image_file)

deepchem/data/tests/test_image_loader.py

+1 −1

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		@@ -5,7 +5,6 @@ import os
		import unittest
		import tempfile
		from scipy import misc
		from PIL import Image
		import deepchem as dc
		import zipfile

		@@ -17,6 +16,7 @@ class TestImageLoader(unittest.TestCase):

		def setUp(self):
		super(TestImageLoader, self).setUp()
		from PIL import Image
		self.current_dir = os.path.dirname(os.path.abspath(__file__))
		self.tif_image_path = os.path.join(self.current_dir, "a_image.tif")

deepchem/trans/tests/test_transformers.py

+1 −1

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		@@ -16,7 +16,6 @@ import pandas as pd
		import deepchem as dc
		import tensorflow as tf
		import scipy.ndimage
		from PIL import Image


		class TestTransformers(unittest.TestCase):
		@@ -613,6 +612,7 @@ class TestTransformers(unittest.TestCase):
		assert np.allclose(flip_lr, check_lr)

		def test_scaling(self):
		from PIL import Image
		# Check Scales
		dt = DataTransforms(self.d)
		h = 150

deepchem/trans/transformers.py

+2 −1

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		@@ -11,7 +11,6 @@ import time
		import deepchem as dc
		import tensorflow as tf
		from deepchem.data import NumpyDataset
		from PIL import Image


		def undo_transforms(y, transformers):
		@@ -1042,6 +1041,7 @@ class ImageTransformer(Transformer):

		def transform_array(self, X, y, w):
		"""Transform the data in a set of (X, y, w) arrays."""
		from PIL import Image
		images = [scipy.ndimage.imread(x, mode='RGB') for x in X]
		images = [Image.fromarray(x).resize(self.size) for x in images]
		return np.array(images), y, w
		@@ -1286,6 +1286,7 @@ class DataTransforms(Transformer):
		h - height of the images
		w - width of the images
		"""
		from PIL import Image
		return Image.fromarray(self.Image).resize((h, w))

		def flip(self, direction="lr"):

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