def _loadToArray(self, imagePath):
"""
Creates input array. Applies scale factor to each image.
"""
try:
image = PIL.Image.open(imagePath)
except IOError as e:
#print("Trying to open by converting to png")
png = os.path.splitext(imagePath)[0] + '.png'
wand.image.Image(filename=imagePath).convert('PNG').save(filename=png)
image = PIL.Image.open(png)
#resize
scaleFactor = np.divide(self.imageSize, image.size)
newSize = tuple(round(x * s) for x, s in zip(image.size, scaleFactor))
image.thumbnail(newSize)
#greyscale
image = image.convert('L')
# neurolab seems to expect 1d input, so rescale the images in the
# input array as linear (the network does't know about shape anyway)
imageArray = np.array(image)
newSize = mul(*imageArray.shape)
return imageArray.reshape(newSize)
def get_grays(image, width, height):
"""
将图片转为灰度图片，并将图片缩小到 width*height，返回灰度像素值列表
"""
if isinstance(image, (list, tuple)):
if len(image) != width*height:
raise ValueError('image sequence length ({}) not equal to width*height ({}).'.format(
len(image), width*height
))
return image
if wand is None and PIL is None:
raise ImportError('must have wand or Pillow/PIL installed to use dhash on images.')
if wand is not None and isinstance(image, wand.image.Image):
with image.clone() as small_image:
small_image.type = 'grayscale'
small_image.resize((width, height))
blob = small_image.make_blob(format='RGB')
return list(blob[::3])
elif PIL is not None and isinstance(image, PIL.Image.Image):
gray_image = image.convert('L')
small_image = gray_image.resize((width, height), PIL.Image.ANTIALIAS)
return list(small_image.getdata())
else:
raise ValueError('image must be a wand.image.Image or PIL.Image instance.')
def get_hue_saturation_mean(image):
"""
:param image: PIL.Image object
"""
hsv = ImageStat.Stat(image.convert("HSV"))
hue = hsv.mean[2]
saturation = hsv.mean[1]
return np.mean[hue, saturation]
def _loadToArray(imagePath):
try:
image = PIL.Image.open(imagePath)
except IOError as e:
print("Trying to open by converting to png")
png = os.path.splitext(imagePath)[0] + '.png'
wand.image.Image(filename=imagePath).convert('PNG').save(filename=png)
image = PIL.Image.open(png)
#resize
newSize = tuple(x * scaleFactor for x in image.size)
image.thumbnail(newSize)
#greyscale
image = image.convert('F')
return np.array(image)
def process_file_image(self, doc):
"""processor for image content-type"""
import wand.image
image=wand.image.Image(filename=self.get_file_path(doc["file"]))
#create jpg thumbnail
new_width=200
new_height=(image.height*new_width)//image.width
image.resize(new_width, new_height)
jpg_image=image.convert("jpg")
image.close()
jpg_image.save(filename=self.get_thumb_path(doc["file"]))
doc["thumbnail"]=self.get_thumb_url(doc["file"])
#ocr to extract text
doc["text"]=self.process_ocr(self.get_file_path(doc["file"]))
return(doc)
def get_grays(image, width, height):
"""Convert image to grayscale, downsize to width*height, and return list
of grayscale integer pixel values (for example, 0 to 255).
>>> get_grays([0,0,1,1,1, 0,1,1,3,4, 0,1,6,6,7, 7,7,7,7,9, 8,7,7,8,9], 5, 5)
[0, 0, 1, 1, 1, 0, 1, 1, 3, 4, 0, 1, 6, 6, 7, 7, 7, 7, 7, 9, 8, 7, 7, 8, 9]
>>> import os
>>> test_filename = os.path.join(os.path.dirname(__file__), 'dhash-test.jpg')
>>> with wand.image.Image(filename=test_filename) as image:
... get_grays(image, 9, 9)[:18]
[95, 157, 211, 123, 94, 79, 75, 75, 78, 96, 116, 122, 113, 93, 75, 82, 81, 79]
"""
if isinstance(image, (tuple, list)):
if len(image) != width * height:
raise ValueError(
'image sequence length ({}) not equal to width*height ({})'.
format(len(image), width * height))
return image
if wand is None and PIL is None:
raise ImportError(
'must have wand or Pillow/PIL installed to use dhash on images')
if wand is not None and isinstance(image, wand.image.Image):
with image.clone() as small_image:
small_image.type = 'grayscale'
small_image.resize(width, height)
blob = small_image.make_blob(format='RGB')
if IS_PY3:
return list(blob[::3])
else:
return [ord(c) for c in blob[::3]]
elif PIL is not None and isinstance(image, PIL.Image.Image):
gray_image = image.convert('L')
small_image = gray_image.resize((width, height), PIL.Image.ANTIALIAS)
return list(small_image.getdata())
else:
raise ValueError(
'image must be a wand.image.Image or PIL.Image instance')