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import numpy as np
# Imports for visualization
import PIL.Image
from io import BytesIO
try:
# Python 3
from urllib.request import urlopen, Request
from urllib.parse import quote
except ImportError:
# Python 2
from urllib2 import urlopen, Request
from urllib2 import quote
# Imports for pulling metadata from imgur url
import requests
from bs4 import BeautifulSoup
# All images are returned as PIL images, not numpy arrays
def loadImageGrayscale(img_file):
"""Load image from file, convert to grayscale float32 numpy array"""
img = PIL.Image.open(img_file)
# Convert to grayscale and return
return img.convert("L")
def loadImageFromURL(url, max_size_bytes=4000000):
"""Load image from url.
If the url has more data than max_size_bytes, fail out
Try and update with metadata url link if an imgur link"""
# If imgur try to load from metadata
url = tryUpdateImgurURL(url)
# Try loading image from url directly
try:
req = Request(url, headers={'User-Agent' : "TensorFlow Chessbot"})
con = urlopen(req)
# Load up to max_size_bytes of data from url
data = con.read(max_size_bytes)
# If there is more, image is too big, skip
if len(con.read(1)) != 0:
print("Skipping, url data larger than %d bytes" % max_size_bytes)
return None, url
# Process into PIL image
img = PIL.Image.open(BytesIO(data))
# Return PIL image and url used
return img, url
except IOError as e:
# Return None on failure to load image from url
return None, url
def tryUpdateImgurURL(url):
"""Try to get actual image url from imgur metadata"""
if 'imgur' not in url: # Only attempt on urls that have imgur in it
return url
soup = BeautifulSoup(requests.get(url).content, "lxml")
# Get metadata tags
meta = soup.find_all('meta')
# Get the specific tag, ex.
# <meta content="https://i.imgur.com/bStt0Fuh.jpg" name="twitter:image"/>
tags = list(filter(lambda tag: 'name' in tag.attrs and tag.attrs['name'] == "twitter:image", meta))
if tags:
# Replace url with metadata url
url = tags[0]['content']
return url
def loadImageFromPath(img_path):
"""Load PIL image from image filepath, keep as color"""
return PIL.Image.open(open(img_path,'rb'))
def resizeAsNeeded(img, max_size=(2000,2000), max_fail_size=(2000,2000)):
if not PIL.Image.isImageType(img):
img = PIL.Image.fromarray(img) # Convert to PIL Image if not already
# If image is larger than fail size, don't try resizing and give up
if img.size[0] > max_fail_size[0] or img.size[1] > max_fail_size[1]:
return None
"""Resize if image larger than max size"""
if img.size[0] > max_size[0] or img.size[1] > max_size[1]:
print("Image too big (%d x %d)" % (img.size[0], img.size[1]))
new_size = np.min(max_size) # px
if img.size[0] > img.size[1]:
# resize by width to new limit
ratio = np.float(new_size) / img.size[0]
else:
# resize by height
ratio = np.float(new_size) / img.size[1]
print("Reducing by factor of %.2g" % (1./ratio))
new_size = (np.array(img.size) * ratio).astype(int)
print("New size: (%d x %d)" % (new_size[0], new_size[1]))
img = img.resize(new_size, PIL.Image.BILINEAR)
return img
def getVisualizeLink(corners, url):
"""Return online link to visualize found corners for url"""
encoded_url = quote(url, safe='')
return ("http://tetration.xyz/tensorflow_chessbot/overlay_chessboard.html?%d,%d,%d,%d,%s" %
(corners[0], corners[1], corners[2], corners[3], encoded_url)) |