Webify a directory of images

pyproject.toml

[tool.poetry]
name = "webImages"
version = "0.1.0"
description = "Scales all images in a directory and preps for web use"
authors = ["Philip Kahn <tigerhawkvok@gmail.com>"]
license = "MIT"

[tool.poetry.dependencies]
python = "^3.7"
scikit-image = "~0.19.0"
numpy = "^1.21.0"

[tool.poetry.dev-dependencies]

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

requirements.txt

cycler==0.10.0; python_version >= "3.7" \
    --hash=sha256:1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d \
    --hash=sha256:cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8
decorator==4.4.2; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.2.0" and python_version >= "3.6" \
    --hash=sha256:41fa54c2a0cc4ba648be4fd43cff00aedf5b9465c9bf18d64325bc225f08f760 \
    --hash=sha256:e3a62f0520172440ca0dcc823749319382e377f37f140a0b99ef45fecb84bfe7
imageio==2.9.0; python_version >= "3.6" \
    --hash=sha256:3604d751f03002e8e0e7650aa71d8d9148144a87daf17cb1f3228e80747f2e6b \
    --hash=sha256:52ddbaeca2dccf53ba2d6dec5676ca7bc3b2403ef8b37f7da78b7654bb3e10f0
kiwisolver==1.3.1; python_version >= "3.7" \
    --hash=sha256:fd34fbbfbc40628200730bc1febe30631347103fc8d3d4fa012c21ab9c11eca9 \
    --hash=sha256:d3155d828dec1d43283bd24d3d3e0d9c7c350cdfcc0bd06c0ad1209c1bbc36d0 \
    --hash=sha256:5a7a7dbff17e66fac9142ae2ecafb719393aaee6a3768c9de2fd425c63b53e21 \
    --hash=sha256:f8d6f8db88049a699817fd9178782867bf22283e3813064302ac59f61d95be05 \
    --hash=sha256:5f6ccd3dd0b9739edcf407514016108e2280769c73a85b9e59aa390046dbf08b \
    --hash=sha256:225e2e18f271e0ed8157d7f4518ffbf99b9450fca398d561eb5c4a87d0986dd9 \
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    --hash=sha256:232c9e11fd7ac3a470d65cd67e4359eee155ec57e822e5220322d7b2ac84fbf0 \
    --hash=sha256:b38694dcdac990a743aa654037ff1188c7a9801ac3ccc548d3341014bc5ca278 \
    --hash=sha256:ca3820eb7f7faf7f0aa88de0e54681bddcb46e485beb844fcecbcd1c8bd01689 \
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    --hash=sha256:b9edd0110a77fc321ab090aaa1cfcaba1d8499850a12848b81be2222eab648f6 \
    --hash=sha256:0cd53f403202159b44528498de18f9285b04482bab2a6fc3f5dd8dbb9352e30d \
    --hash=sha256:33449715e0101e4d34f64990352bce4095c8bf13bed1b390773fc0a7295967b3 \
    --hash=sha256:401a2e9afa8588589775fe34fc22d918ae839aaaf0c0e96441c0fdbce6d8ebe6 \
    --hash=sha256:950a199911a8d94683a6b10321f9345d5a3a8433ec58b217ace979e18f16e248
matplotlib==3.4.2; python_version >= "3.7" \
    --hash=sha256:c541ee5a3287efe066bbe358320853cf4916bc14c00c38f8f3d8d75275a405a9 \
    --hash=sha256:3a5c18dbd2c7c366da26a4ad1462fe3e03a577b39e3b503bbcf482b9cdac093c \
    --hash=sha256:a9d8cb5329df13e0cdaa14b3b43f47b5e593ec637f13f14db75bb16e46178b05 \
    --hash=sha256:7ad19f3fb6145b9eb41c08e7cbb9f8e10b91291396bee21e9ce761bb78df63ec \
    --hash=sha256:7a58f3d8fe8fac3be522c79d921c9b86e090a59637cb88e3bc51298d7a2c862a \
    --hash=sha256:6382bc6e2d7e481bcd977eb131c31dee96e0fb4f9177d15ec6fb976d3b9ace1a \
    --hash=sha256:6a6a44f27aabe720ec4fd485061e8a35784c2b9ffa6363ad546316dfc9cea04e \
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    --hash=sha256:21b31057bbc5e75b08e70a43cefc4c0b2c2f1b1a850f4a0f7af044eb4163086c \
    --hash=sha256:b26535b9de85326e6958cdef720ecd10bcf74a3f4371bf9a7e5b2e659c17e153 \
    --hash=sha256:32fa638cc10886885d1ca3d409d4473d6a22f7ceecd11322150961a70fab66dd \
    --hash=sha256:956c8849b134b4a343598305a3ca1bdd3094f01f5efc8afccdebeffe6b315247 \
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    --hash=sha256:df815378a754a7edd4559f8c51fc7064f779a74013644a7f5ac7a0c31f875866 \
    --hash=sha256:d8d994cefdff9aaba45166eb3de4f5211adb4accac85cbf97137e98f26ea0219
networkx==2.5.1; python_version >= "3.6" \
    --hash=sha256:0635858ed7e989f4c574c2328380b452df892ae85084144c73d8cd819f0c4e06 \
    --hash=sha256:109cd585cac41297f71103c3c42ac6ef7379f29788eb54cb751be5a663bb235a
numpy==1.21.0; python_version >= "3.7" \
    --hash=sha256:d5caa946a9f55511e76446e170bdad1d12d6b54e17a2afe7b189112ed4412bb8 \
    --hash=sha256:ac4fd578322842dbda8d968e3962e9f22e862b6ec6e3378e7415625915e2da4d \
    --hash=sha256:598fe100b2948465cf3ed64b1a326424b5e4be2670552066e17dfaa67246011d \
    --hash=sha256:7c55407f739f0bfcec67d0df49103f9333edc870061358ac8a8c9e37ea02fcd2 \
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    --hash=sha256:3537b967b350ad17633b35c2f4b1a1bbd258c018910b518c30b48c8e41272717 \
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    --hash=sha256:cf680682ad0a3bef56dae200dbcbac2d57294a73e5b0f9864955e7dd7c2c2491 \
    --hash=sha256:d95d16204cd51ff1a1c8d5f9958ce90ae190be81d348b514f9be39f878b8044a \
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    --hash=sha256:e80fe25cba41c124d04c662f33f6364909b985f2eb5998aaa5ae4b9587242cce
pillow==8.3.0; python_version >= "3.7" \
    --hash=sha256:333313bcc53a8a7359e98d5458dfe37bfa301da2fd0e0dc41f585ae0cede9181 \
    --hash=sha256:bccd0d604d814e9494f3bf3f077a23835580ed1743c5175581882e7dd1f178c3 \
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    --hash=sha256:803606e206f3e366eea46b1e7ab4dac74cfac770d04de9c35319814e11e47c46
pyparsing==2.4.7; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7" \
    --hash=sha256:ef9d7589ef3c200abe66653d3f1ab1033c3c419ae9b9bdb1240a85b024efc88b \
    --hash=sha256:c203ec8783bf771a155b207279b9bccb8dea02d8f0c9e5f8ead507bc3246ecc1
python-dateutil==2.8.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7" \
    --hash=sha256:73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c \
    --hash=sha256:75bb3f31ea686f1197762692a9ee6a7550b59fc6ca3a1f4b5d7e32fb98e2da2a
pywavelets==1.1.1; python_version >= "3.6" \
    --hash=sha256:35959c041ec014648575085a97b498eafbbaa824f86f6e4a59bfdef8a3fe6308 \
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    --hash=sha256:1a64b40f6acb4ffbaccce0545d7fc641744f95351f62e4c6aaa40549326008c9
scikit-image==0.16.2; python_version >= "3.6" \
    --hash=sha256:dd7fbd32da74d4e9967dc15845f731f16e7966cee61f5dc0e12e2abb1305068c \
    --hash=sha256:0808ab5f8218d91a1c008036993636535a37efd67a52ab0f2e6e3f4b7e75aeda \
    --hash=sha256:3af3d781ce085573ced37b2b5b9abfd32ce3d4723bd17f37e829025d189b0421 \
    --hash=sha256:063d1c20fcd53762f82ee58c29783ae4e8f6fbed445b41b704fa33b6f355729d \
    --hash=sha256:2a54bea469eb1b611bee1ce36e60710f5f94f29205bc5bd67a51793909b1e62b \
    --hash=sha256:2d346d49b6852cffb47cbde995e2696d5b07f688d8c057a0a4548abf3a98f920 \
    --hash=sha256:8b2b768b02c6b7476f2e16ddd91f827d3817aef73f82cf28bff7a8dcdfd8c55c \
    --hash=sha256:3ad2efa792ab8de5fcefe6f4f5bc1ab64c411cdb5c829ce1526ab3a5a7729627 \
    --hash=sha256:2aa962aa82d815606d7dad7f045f5d7ca55c65b4320d47e15a98fc92612c2d6c \
    --hash=sha256:e774377876cb258e8f4d63f7809863f961c98aa02263b3ff54a39483bc6f7d26 \
    --hash=sha256:6786b127f33470fd843e644435522fbf43bce05c9f5527946c390ccb9e1cac27 \
    --hash=sha256:a48fb0d34a090b578b87ffebab0fe035295c1945dbc2b28e1a55ea2cf6031751 \
    --hash=sha256:41e28db0136f29ecd305bef0408fdfc64be9d415e54f5099a95555c65f5c1865 \
    --hash=sha256:0715b7940778ba5d73da3908d60ddf2eb93863f7c394493a522fe56d3859295c \
    --hash=sha256:e18d73cc8893e2268b172c29f9aab530faf8cd3b7c11ae0bee3e763d719d35c5
scipy==1.6.1; python_version >= "3.7" \
    --hash=sha256:a15a1f3fc0abff33e792d6049161b7795909b40b97c6cc2934ed54384017ab76 \
    --hash=sha256:e79570979ccdc3d165456dd62041d9556fb9733b86b4b6d818af7a0afc15f092 \
    --hash=sha256:a423533c55fec61456dedee7b6ee7dce0bb6bfa395424ea374d25afa262be261 \
    --hash=sha256:33d6b7df40d197bdd3049d64e8e680227151673465e5d85723b3b8f6b15a6ced \
    --hash=sha256:6725e3fbb47da428794f243864f2297462e9ee448297c93ed1dcbc44335feb78 \
    --hash=sha256:5fa9c6530b1661f1370bcd332a1e62ca7881785cc0f80c0d559b636567fab63c \
    --hash=sha256:bd50daf727f7c195e26f27467c85ce653d41df4358a25b32434a50d8870fc519 \
    --hash=sha256:f46dd15335e8a320b0fb4685f58b7471702234cba8bb3442b69a3e1dc329c345 \
    --hash=sha256:0e5b0ccf63155d90da576edd2768b66fb276446c371b73841e3503be1d63fb5d \
    --hash=sha256:2481efbb3740977e3c831edfd0bd9867be26387cacf24eb5e366a6a374d3d00d \
    --hash=sha256:68cb4c424112cd4be886b4d979c5497fba190714085f46b8ae67a5e4416c32b4 \
    --hash=sha256:5f331eeed0297232d2e6eea51b54e8278ed8bb10b099f69c44e2558c090d06bf \
    --hash=sha256:0c8a51d33556bf70367452d4d601d1742c0e806cd0194785914daf19775f0e67 \
    --hash=sha256:83bf7c16245c15bc58ee76c5418e46ea1811edcc2e2b03041b804e46084ab627 \
    --hash=sha256:794e768cc5f779736593046c9714e0f3a5940bc6dcc1dba885ad64cbfb28e9f0 \
    --hash=sha256:5da5471aed911fe7e52b86bf9ea32fb55ae93e2f0fac66c32e58897cfb02fa07 \
    --hash=sha256:8e403a337749ed40af60e537cc4d4c03febddcc56cd26e774c9b1b600a70d3e4 \
    --hash=sha256:a5193a098ae9f29af283dcf0041f762601faf2e595c0db1da929875b7570353f \
    --hash=sha256:c4fceb864890b6168e79b0e714c585dbe2fd4222768ee90bc1aa0f8218691b11
six==1.16.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7" \
    --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 \
    --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926

webImages.py

#!python3
"""
When run as a script, opens all .jpg and .png
files in the folder and downsamples them to
maximum 1280 pixels wide and quality level 75

PNG optimization requires OptiPNG to be installed
in your PATH: http://optipng.sourceforge.net/
(GitHub mirror: https://github.com/countingpine/optipng )

@author Philip Kahn <https://github.com/tigerhawkvok/>
@license MIT
@url https://gist.github.com/tigerhawkvok/2d5ecd4a56235edc06ac8f96984fb5d6
"""

import glob
import os
import imghdr
from typing import Union
from skimage.io import imread, imsave
from skimage.transform import rescale
import numpy as np


# This list of files will be IGNORED and not optimized
# when run as a script
IGNORE_OPTIMIZE_LIST = frozenset([
    "logo.png",
    "fps_pfe_4x6.png",
])


def _getImage(filePath) -> Union[np.ndarray, None]:
    """
    Tidy file return.
    Renames the file if the extension doesn't match
    the filetype.
    """
    try:
        if os.path.basename(filePath) in IGNORE_OPTIMIZE_LIST:
            return None
        fileExtension = os.path.splitext(filePath)[1][1:]
        what = imghdr.what(filePath)
        filePathNew = None
        if what == "jpeg" and fileExtension.lower() not in ("jpg", "jpeg"):
            filePathNew = filePath.replace(f".{fileExtension}", ".jpg")
        elif what is not None and fileExtension.lower() != what:
            filePathNew = filePath.replace(f".{fileExtension}", f".{what}")
        if filePathNew is not None:
            # Rename the file with the corrected resolution
            os.rename(filePath, filePathNew)
            filePath = filePathNew
        return imread(filePath).astype(np.uint8)
    except Exception: #pylint: disable= broad-except
        return None

def downscaleJPGs(relativeDir:str= "./", maxResolution:int= 1280, quality:int= 75):
    """
    Opens all .jpg files in a folder and downscale them to
    maxResolution
    """
    for jpgFile in glob.glob(os.path.join(relativeDir, '*.jpg')):
        img = _getImage(jpgFile)
        if img is None:
            continue
        scale = maxResolution / max(img.shape[0], img.shape[1])
        if scale >= 1:
            print(f"{jpgFile} OK size")
            continue
        print(f"Checking {jpgFile}")
        img = rescale(img, scale, anti_aliasing= True, preserve_range= True, multichannel= True).astype(np.uint8)
        try:
            imsave(jpgFile, img, quality= quality)
        except Exception as e: #pylint: disable= broad-except
            print(f">> Failed to convert {jpgFile} of shape {img.shape}: {e}")


def optimizePNGs(relativeDir:str= "./", maxResolution:int= 1280):
    """
    Call this function to optimize all .png files in a folder
    """
    validPNG = set()
    for pngFile in glob.glob(os.path.join(relativeDir, '*.png')):
        img = _getImage(pngFile)
        if img is None:
            continue
        scale = maxResolution / max(img.shape[0], img.shape[1])
        if scale >= 1:
            print(f"{pngFile} OK size")
            validPNG.update([pngFile])
            continue
        print(f"Resizing {pngFile}")
        img = rescale(img, scale, anti_aliasing= True, preserve_range= True, multichannel= True).astype(np.uint8)
        try:
            imsave(pngFile, img)
            validPNG.update([pngFile])
        except Exception as e: #pylint: disable= broad-except
            print(f">> Failed to convert {pngFile} of shape {img.shape}: {e}")
    # Now call optiPNG locally to optimize the rescaled images
    for pngFile in validPNG:
        print(f"Optimizing {pngFile}")
        cmd = f"optipng -o5 -v {pngFile}"
        os.system(cmd)

if __name__ == "__main__":
    TARGET_MAX_RESOLUTION_PX = 1280
    downscaleJPGs(maxResolution= TARGET_MAX_RESOLUTION_PX)
    optimizePNGs(maxResolution= TARGET_MAX_RESOLUTION_PX)