Make PyWavelets and scipy optional installs

README.rst

@@ -30,11 +30,16 @@ black & gray fractions (without position information).
 Installation
 ============
 
-Based on PIL/Pillow Image, numpy and scipy.fftpack (for pHash)
+Based on PIL/Pillow Image, numpy, scipy.fftpack (for pHash) and PyWavelets (for wHash)
 Easy installation through `pypi`_::
 
 	pip install imagehash
 
+scipy and PyWavelets are optional. If you don't intend on using them, you can install ImageHash using:
+
+  pip install imagehash --no-dependencies
+  pip install numpy pillow
+
 Basic usage
 ===========
 ::
@@ -162,6 +167,8 @@ and show how you can create a reverse image search using hashes generated by thi
 Changelog
 ----------
 
+* Unreleased: Make PyWavelets and scipy optional if ImageHash is installed with `--no-dependencies` installs by @Avasam
+
 * 4.3: typing annotations by @Avasam @SpangleLabs and @nh2
 
 * 4.2: Cropping-Resistant image hashing added by @joshcoales

imagehash/__init__.py

@@ -266,10 +266,14 @@ def phash(image, hash_size=8, highfreq_factor=4):
 
 	@image must be a PIL instance.
 	"""
+	try:
+		import scipy.fftpack
+	except (ModuleNotFoundError) as e:
+		raise ModuleNotFoundError(f"{e.msg}. Did you forget to install scipy?") from e
+
 	if hash_size < 2:
 		raise ValueError('Hash size must be greater than or equal to 2')
 
-	import scipy.fftpack
 	img_size = hash_size * highfreq_factor
 	image = image.convert('L').resize((img_size, img_size), ANTIALIAS)
 	pixels = numpy.asarray(image)
@@ -289,7 +293,11 @@ def phash_simple(image, hash_size=8, highfreq_factor=4):
 
 	@image must be a PIL instance.
 	"""
-	import scipy.fftpack
+	try:
+		import scipy.fftpack
+	except (ModuleNotFoundError) as e:
+		raise ModuleNotFoundError(f"{e.msg}. Did you forget to install scipy?") from e
+
 	img_size = hash_size * highfreq_factor
 	image = image.convert('L').resize((img_size, img_size), ANTIALIAS)
 	pixels = numpy.asarray(image)
@@ -357,7 +365,11 @@ def whash(image, hash_size=8, image_scale=None, mode='haar', remove_max_haar_ll=
 					'db4' - Daubechies wavelets
 	@remove_max_haar_ll - remove the lowest low level (LL) frequency using Haar wavelet.
 	"""
-	import pywt
+	try:
+		import pywt
+	except (ModuleNotFoundError) as e:
+		raise ModuleNotFoundError(f"{e.msg}. Did you forget to install PyWavelets?") from e
+
 	if image_scale is not None:
 		assert image_scale & (image_scale - 1) == 0, 'image_scale is not power of 2'
 	else:

setup.py

@@ -29,7 +29,7 @@
 		'scipy',		# for phash
 		'pillow',		# or PIL
 		'PyWavelets',  # for whash
-	],
+		'pillow',	# or PIL
 	test_suite='tests',
 	tests_require=['pytest>=3'],
 )