def get_map(image: Image.Image):
"""Use feature matching to determine match and map type"""
path = os.path.join(get_temp_directory(), "temp.png")
map_directory = os.path.join(get_assets_directory(), "maps")
image.save(path)
image = opencv.imread(path, 0)
orb = opencv.ORB_create()
image_kp, image_ds = orb.detectAndCompute(image, None)
map_results = dict()
bf = opencv.BFMatcher(opencv.NORM_L1, crossCheck=False)
for match_type, maps in map_dictionary.items():
for map, file_name in maps.items():
file_name = "{}_{}".format(match_type, file_name + ".jpg")
path = os.path.join(map_directory, file_name)
Image.open(path).resize((200, 200), Image.ANTIALIAS).save(os.path.join(get_temp_directory(), file_name))
template = opencv.imread(os.path.join(get_temp_directory(), file_name), 0)
template_kp, template_ds = orb.detectAndCompute(template, None)
matches = bf.knnMatch(image_ds, template_ds, k=2)
good = list()
for m, n in matches:
if m.distance < 0.8 * n.distance:
good.append(m)
map_results[(match_type, map)] = len(good)
map = max(map_results, key=lambda key: map_results[key])
if map_results[map] < MIN_MATCHES:
return None
return map
def binary_img(imgObj: Image, a=100):
imgObj = imgObj.copy()
img1 = imgObj.convert("L")
img2 = img1.point(set_table(a), '1')
return img2
def match_digit(image: Image.Image)->str:
"""Match the digit in the image to a template in the assets folder"""
folder = os.path.join(get_assets_directory(), "digits")
digits = os.listdir(folder)
results = dict()
image = image.crop((0, 0, image.height, image.height))
image = image.resize((150, 150), Image.BICUBIC)
image = high_pass_invert(image, 600)
image = imageops.replace_color(image, (0, 0, 0), (255, 0, 0))
pixels = image.load()
min_x, min_y, max_x, max_y = image.width - 1, image.height - 1, 0, 0
for x in range(image.width):
for y in range(image.height):
if sum(pixels[x, y]) < 3 * 255 // 2:
min_x = min(min_x, x)
min_y = min(min_y, y)
max_x = max(max_x, x)
max_y = max(max_y, y)
box = (min_x - 3, min_y - 3, max_x + 3, max_y + 3)
image = image.crop(box)
image = imageops.replace_color(image, (0, 0, 0), (255, 255, 255))
image = imageops.replace_color(image, (255, 0, 0), (0, 0, 0))
image = image.resize((150, 150), Image.NEAREST)
for digit in sorted(digits):
if not digit.endswith(".png"):
continue
template: Image.Image = Image.open(os.path.join(folder, digit)).convert("RGB")
digit = digit[:-4]
results[digit] = imageops.get_similarity_monochrome(template, image)
return max(results, key=lambda key: results[key])
def get_score(screenshot: Image.Image, scale: float, location: Tuple[int, int]) -> Tuple[int, int, int]:
"""Parse the score on the scoreboard to (left, right, self)"""
x, y = location
y1, y2 = int(y - 40 * scale), int(y - 20 * scale)
x1_l, x2_l, x1_r, x2_r = x + 50 * scale, x + 85 * scale, x + 180 * scale, 205 * scale
left: Image.Image = screenshot.crop(tuple(map(int, (x1_l, y1, x2_l, y2))))
right: Image.Image = screenshot.crop(tuple(map(int, (x1_r, y1, x2_r, y2))))
score_l, score_r = map(lambda i: tesseract.perform_ocr_scoreboard(i, "score", 420, 360), (left, right))
return score_l, score_r, list(map(ScoreboardParser.is_allied, (left, right))).index(True)
def removeFrame_img(imgObj: Image, frame_width=2):
imgObj = imgObj.copy()
(width, height) = imgObj.size
if frame_width >= width or frame_width >= height:
raise ImageSizeError('image size too small, (%s, %s)' % imgObj.size)
imgObj = imgObj.crop((frame_width, frame_width,
width - frame_width, height - frame_width))
return imgObj
def is_bitonal(im: Image.Image) -> bool:
"""
Tests a PIL.Image for bitonality.
Args:
im (PIL.Image.Image): Image to test
Returns:
True if the image contains only two different color values. False
otherwise.
"""
return im.getcolors(2) is not None and len(im.getcolors(2)) == 2
def make_transparent(cls, slot_item: Image.Image):
px = 0
py = 0
while py < slot_item.size[1] and py < 34:
slot_item_pixel = slot_item.getpixel((px, py))
if slot_item_pixel == (255, 0, 255, 255):
slot_item.putpixel((px, py), (255, 0, 255, 0))
px += 1
if px == slot_item.size[0] or px == 34:
py += 1
px = 0
return slot_item
def paste_watermark(image: Image.Image, watermark: Image.Image) -> Image.Image:
min_image_dimension = min(image.size)
watermark_length = int(min_image_dimension / 4.)
watermark_size = (watermark_length, watermark_length)
watermark = watermark.resize(watermark_size, Image.ANTIALIAS)
foreground_shape = (image.size[1], image.size[0], 4)
top_margin = int(5 * image.size[1] / 6.)
foreground_array = np.zeros(foreground_shape, dtype=np.uint8)
foreground_array[top_margin - watermark_length:top_margin, :watermark_length, :] = np.array(watermark)
image_array = np.array(image)
background_rgb = img_as_float(image_array[..., :-1])
mean = np.mean(background_rgb[top_margin - watermark_length:top_margin, :watermark_length, :])
if mean < 0.4:
foreground_alpha = foreground_array[..., -1:]
foreground_rgb = foreground_array[..., :-1]
foreground_rgb = 255 - foreground_rgb
foreground_array = np.dstack((foreground_rgb, foreground_alpha))
marked_image_array = alpha_composite(foreground_array, image_array)
return Image.fromarray(marked_image_array, mode='RGBA')
def get_item_color(cls, item: Image.Image) -> Tuple[int, int, int]:
"""Gets the average color of an item.
:param item: The item's image
:return: The item's colors
"""
count = 0
px = 0
py = 0
color = [0, 0, 0]
while py < item.size[1]:
item_pixel = item.getpixel((px, py))
if not (cls.is_empty(item_pixel) or cls.is_background_color(item_pixel)):
color[0] += item_pixel[0]
color[1] += item_pixel[1]
color[2] += item_pixel[2]
count += 1
px += 1
if px == item.size[0]:
px = 0
py += 1
if count == 0:
return 0, 0, 0
color[0] /= count
color[1] /= count
color[2] /= count
return int(color[0]), int(color[1]), int(color[2])
def prepare_image(self, image: Image.Image, colors: list) -> Image.Image:
"""Prepare an image by filtering its text"""
self.conn.send("Processing colors...")
size = tuple(map(lambda v: int(round(self._f * v)), image.size))
image = image.resize(size, Image.NORMAL)
colors: List[Color] = list(set(map(ChatParser.rgb_to_hsv, colors)))
pixels = np.array(image)
img_colors = image.getcolors(image.width * image.height)
cmp_colors = list()
for count, color in img_colors:
color = ChatParser.rgb_to_hsv(color)
if count > 1 and ChatParser.compare_colors(color, colors):
cmp_colors.append(color)
self.conn.send("Pre-processing image...")
result = Pool().map(partial(self.process_row, colors), pixels)
return Image.fromarray(np.vstack(result), "L")
def get_similarity(template: Image.Image, to_match: Image.Image)->float:
"""Compares two images and returns the similarity ratio"""
if template.size != to_match.size:
raise ValueError("These images are not the same size. One: {}, Two: {}.".format(template.size, to_match.size))
diff = sum(abs(color_one - color_two) for pair_one, pair_two in zip(template.getdata(), to_match.getdata())
for color_one, color_two in zip(pair_one, pair_two) if not sum(pair_two) > 3 * 255 - 1)
n_comps = template.size[0] * template.size[1] * 3
return 100 - (diff / 255.0 * 100) / n_comps
def template_match(image: Image.Image, template: Image.Image, margin: float = 95, similarity: bool=False):
"""Use template matching and similarity to return template score"""
image_cv, template_cv = map(image_to_opencv, (image, template))
result = cv.matchTemplate(image_cv, template_cv, cv.TM_CCOEFF)
_, _, _, loc = cv.minMaxLoc(result)
image = image.crop((loc[0], loc[1], loc[0] + template.width, loc[1] + template.height))
ratio = get_similarity(image, template)
ratio = (ratio > margin) if similarity is False else ratio
return ratio, loc
def _crop(image: Image.Image, pad_w: int, pad_h: int) -> Image.Image:
orig_w, orig_h = image.size
w_shift = max(orig_w - pad_w, 0) // 2
h_shift = max(orig_h - pad_h, 0) // 2
even_w = max(orig_w - pad_w, 0) % 2
even_h = max(orig_h - pad_h, 0) % 2
return image.crop(
(w_shift, h_shift, orig_w - w_shift - even_w,
orig_h - h_shift - even_h))
def post(self):
if self.request.files == {} or 'mypicture' not in self.request.files:
""" 看是否有文件且name为picture，跟HTML代码对应 """
self.write('')
return
# 有文件，判断是否为我们需要的格式
# 常用的图片格式有：image/jpeg，image/bmp，image/pjpeg，image/gif，image/x-png，image/png
image_type_list = ['image/gif', 'image/jpeg',
'image/pjpeg', 'image/bmp', 'image/png', 'image/x-png']
send_file = self.request.files['mypicture'][0]
if send_file['content_type'] not in image_type_list:
self.write('')
return
# 上述判断含有很大的上传漏洞，可以通过PIL来避免这些。
# 限制上传文件的大小，通过len获取字节数
if len(send_file['body']) > 4 * 1024 * 1024:
self.write('')
return
# 满足要求后，将图片存储。
# 存储也就是将send_file['body']内容进行存储，type(send_file['body'])为str
# 先将文件写入临时文件，然后再用PIL对这个临时文件进行处理。
tmp_file = tempfile.NamedTemporaryFile(delete=True) # 创建临时文件，当文件关闭时自动删除
tmp_file.write(send_file['body']) # 写入临时文件
tmp_file.seek(0) # 将文件指针指向文件头部，因为上面的操作将指针指向了尾部。
# 此时用PIL再处理进行存储，PIL打开不是图片的文件会出现IOERROR错误，这就可以识别后缀名虽然是图片格式，但内容并非是图片。
try:
image_one = Image.open(tmp_file.name)
except IOError as error:
logging.info(error) # 进行日志记录，因为这些操作大多数是破坏者的做法。
logging.info('+' * 30 + '\n')
logging.info(self.request.headers)
tmp_file.close()
self.write('')
return
# 判断图片尺寸，不在尺寸内拒绝操作
if image_one.size[0] < 250 or image_one.size[1] < 250 or image_one.size[0] > 2000 or image_one.size[1] > 2000:
tmp_file.close()
self.write('')
return
# 进行存储。
# 指定存储目录，产生新的文件名。
# 获取文件格式，用PIL获得的format不一定正确，所以用原文件名获得
image_path = "./static/picture/"
image_format = send_file['filename'].split('.').pop().lower()
tmp_name = image_path + str(int(time.time())) + image_format
image_one.save(tmp_name)
# 关闭临时文件，关闭后临时文件自动删除
tmp_file.close()
self.write('')
return
def get_channel_data(image: Image.Image, *channel_names: str, as_dict: bool = False):
from numpy import array
width, height = image.size
channels = get_image_channels(image)
channel_data = {}
for channel_name in channel_names:
channel_index = channels[channel_name]
channel_data[channel_name] = array(image.getdata(channel_index)).reshape(height, width)
if as_dict or len(channel_names) != 1:
return dict(zip(channel_names, (channel_data[channel_name] for channel_name in channel_names)))
else:
return channel_data[channel_names[0]]
def high_pass_invert(image: Image.Image, treshold: int) -> Image.Image:
"""Perform a high-pass filter on an image and invert"""
result = image.copy() # Do not modify original image
pixels = result.load()
for x in range(image.width):
for y in range(image.height):
pixel = pixels[x, y]
if sum(pixel) < treshold:
pixels[x, y] = (255, 255, 255)
continue
pixels[x, y] = (0, 0, 0)
return result
def get_item_size(cls, item: Image.Image) -> int:
"""Gets the actual size of an item in pixels."""
size = item.size[0] * item.size[1]
empty = 0
px = 0
py = 0
while py < item.size[1]:
item_pixel = item.getpixel((px, py))
if not cls.is_empty(item_pixel):
size -= empty
empty = 0
px = 0
py += 1
else:
empty += 1
px += 1
if px == item.size[0]:
size -= empty - 1
empty = 0
px = 0
py += 1
empty = 0
px = item.size[0] - 1
py = 0
while py < item.size[1]:
item_pixel = item.getpixel((px, py))
if not cls.is_empty(item_pixel):
size -= empty
empty = 0
px = item.size[0] - 1
py += 1
else:
empty += 1
px -= 1
if px == -1:
empty = 0
px = item.size[0] - 1
py += 1
return size
def createFiles(self):
for directory, width in sizes.items():
image = Image.open(self.path)
startingWidth, startingHeight = image.size
resizedHeight = int(round(startingHeight * (float(width-2)/startingWidth))) #height based on resized width before border applied
if startingWidth < sizes["drawable-xxhdpi"]:
print "WARNING! This image is too small and might look bad at higher resolutions. It's "+str(startingWidth)+" wide."
print "Final image dimensions for "+directory+": "+str(width)+"x"+str(resizedHeight+2)
os.system("convert "+self.path+" +antialias -blur 0 -resize "+str(width-2)+"x"+str(resizedHeight)+
" -bordercolor 'transparent' -border 1x1 -fill black -draw 'point 1,0' -draw 'point 0,1' -draw 'point "
+str(width-2)+",0' -draw 'point 0,"+str(resizedHeight)+"' "+self.out+directory+"/"+self.filename+".9.png")
def clearNoise_img(imgObj: Image):
imgObj = imgObj.copy()
if imgObj.mode not in {'1', 'P'}:
imgObj = imgObj.convert('P')
w, h = imgObj.size
pixdata = imgObj.load()
for y in range(1, h - 1):
for x in range(1, w - 1):
count = 0
if pixdata[x, y - 1] > 245:
count = count + 1
if pixdata[x, y + 1] > 245:
count = count + 1
if pixdata[x - 1, y] > 245:
count = count + 1
if pixdata[x + 1, y] > 245:
count = count + 1
if count > 2:
pixdata[x, y] = 255
return imgObj
def _create_white_bg_image(cls, image: Image.Image) -> Image.Image:
width, height = image.size
new_width = max(width, height)
new_height = new_width
white_img = Image.new("RGBA", (new_width, new_height), (255, 255, 255, 255))
if not image.mode == "RGBA":
image = image.convert("RGBA")
# Put original (potentially transparent) image over white background
white_img.paste(
image,
cls._find_middle_coordinates_pip(white_img.size, image.size),
mask=image
)
return white_img
def feature_match(image: Image.Image, template: Image.Image)->int:
"""Return the amount of features matched with ORB"""
orb = cv.ORB_create()
image.show(), template.show()
matcher = cv.BFMatcher(cv.NORM_L1, crossCheck=False)
template = image_to_opencv(template.convert("RGB"))
tp_kp, tp_ds = orb.detectAndCompute(template, None)
image = image_to_opencv(image.convert("RGB"))
im_kp, im_ds = orb.detectAndCompute(image, None)
assert im_ds is not None and tp_ds is not None
try:
matches = matcher.knnMatch(tp_ds, im_ds, k=2)
except cv.error as e:
print("[OpenCV.feature_match] {}".format(e))
return 0
result = 0
for value in matches:
if len(value) != 2:
continue
m, n = value
if m.distance < 0.8 * n.distance:
result += 1
return result
def _rescale_or_crop(image: Image.Image, pad_w: int, pad_h: int,
rescale_w: bool, rescale_h: bool,
keep_aspect_ratio: bool) -> Image.Image:
"""Rescale and/or crop the image based on the rescale configuration."""
orig_w, orig_h = image.size
if orig_w == pad_w and orig_h == pad_h:
return image
if rescale_w and rescale_h and not keep_aspect_ratio:
image = image.resize((pad_w, pad_h), Image.BILINEAR)
elif rescale_w and rescale_h and keep_aspect_ratio:
ratio = min(pad_h / orig_h, pad_w / orig_w)
image = image.resize((int(orig_w * ratio), int(orig_h * ratio)))
elif rescale_w and not rescale_h:
orig_w, orig_h = image.size
if orig_w != pad_w:
ratio = pad_w / orig_w
image = image.resize((pad_w, int(orig_h * ratio)))
elif rescale_h and not rescale_w:
orig_w, orig_h = image.size
if orig_h != pad_h:
ratio = pad_h / orig_h
image = image.resize((int(orig_w * ratio), pad_h))
return _crop(image, pad_w, pad_h)
def get_score(image: Image.Image):
"""
Implementation:
1. Crop the image
2. (Optional) Use template matching to detect score card
Whether this is required highly depends on the accuracy of the
coordinates. If the coordinates are not accurate, it is
impossible to determine the actual score based on the fill
ratio of the coloured score bars.
3. Use brightest pixel detection to get score location
4. Compute the ratio filled of the score bars
5. Compute the score
6. Detect the presence of satellite markers
7. Calculate the score from ratio and match type
If the length of the coloured bar cannot be determined, then it
must be assumed that one of the bars is full, and thus has gained
full points. The ratio can then be determined with the other bar,
moving in the left direction only horizontally, continuing for as
long as the pixels are coloured and not black.
"""
red, green = get_brightest_pixel_loc(image, 0), get_brightest_pixel_loc(image, 1)
# Assume one of the bars is full
if red is None or green is None:
return 0.0
(xr, yr), (xg, yg) = red, green
pixels = image.load()
# Move over pixels in left direction from brightest point on
xpr = xr
while xpr > 0:
color = pixels[xpr, yr]
if color[0] < 150:
break
xpr -= 1
xpg = xg
while xpg > 0:
color = pixels[xpg, yg]
if color[1] < 175:
break
xpg -= 1
# Now calculate ratio between two scores (ally/enemy)
if xr == xpr:
return 0.0
return (xg - xpg) / (xr - xpr)
def split_scoreboard(image: Image.Image, scale: float, header_loc: tuple) -> list:
"""Split a scoreboard into different elements"""
columns, widths = ScoreboardParser.COLUMNS, ScoreboardParser.WIDTHS
image = ScoreboardParser.crop_scoreboard(image, scale, header_loc)
result = list()
for i in range(16):
row = (0, i * (image.height / ROWS), image.width, (i + 1) * (image.height / ROWS))
row_img = image.crop(row)
row_elems = list()
for name in columns:
start = sum([widths[col] * image.width for col in columns[:columns.index(name)]])
end = start + widths[name] * image.width
crop = (start, 0, end, row_img.height)
column = row_img.crop(crop)
column = column.resize((column.width * 3, column.height * 3), Image.LANCZOS)
row_elems.append(column)
result.append(row_elems)
return result
def bisect_img(imgobj: Image, n):
"""
split imgobj to single-character img
using avarage width
:param imgobj:
:param n:
:return:
"""
(width, height) = imgobj.size
img_slice = width // n
imgs = []
incx = img_slice
for i in range(n):
x = i * incx # 里的数字参数需要自己
y = height # 证码图片的像素进行
imgs.append(imgobj.crop((x, 0, x + incx, y)))
return imgs
def number_scan(cls, slot_image: Image.Image) -> Tuple[int, Any]:
"""Scans a slot's image looking for amount digits
:param slot_image: The image of an inventory slot.
:return: A tuple containing the number parsed, the slot's image and the number's image.
"""
digit_thousands = slot_image.crop((0, 20, 0 + 8, 20 + 7))
digit_hundreds = slot_image.crop((8, 20, 8 + 8, 20 + 7))
digit_tens = slot_image.crop((16, 20, 16 + 8, 20 + 7))
digit_units = slot_image.crop((24, 20, 24 + 8, 20 + 7))
item_numbers = [digit_thousands, digit_hundreds, digit_tens, digit_units]
number_string = ""
numbers_image = Image.new("RGBA", (32, 11), (255, 255, 255, 0))
a = 0
for item_number in item_numbers:
i = 0
for number in numbers:
px = 0
py = 0
while py < item_number.size[1] and py < number.size[1]:
item_number_pixel = item_number.getpixel((px, py))
number_pixel = number.getpixel((px, py))
if not cls.is_transparent(number_pixel) and not item_number_pixel == number_pixel:
break
px += 1
if px == item_number.size[0] or px == number.size[0]:
py += 1
px = 0
if py == item_number.size[1]:
if i > 9:
i = "k"
number_string += str(i)
numbers_image.paste(number, (8 * a, 0))
i = -1
break
if i == -1:
break
i += 1
a += 1
px = 0
py = 0
while py < numbers_image.size[1]:
numbers_image_pixel = numbers_image.getpixel((px, py))
if not cls.is_transparent(numbers_image_pixel):
slot_image.putpixel((px, py + 20), (255, 255, 0, 0))
px += 1
if px == numbers_image.size[0]:
py += 1
px = 0
return 1 if number_string == "" else int(number_string.replace("k", "000")), numbers_image
def create_thumbnail(self):
from django.core.files.storage import default_storage as storage
if not self.picture:
return ""
file_path = self.picture.name
filename_base, filename_ext = os.path.split(file_path)
thumbnail_file_path = "%s_thumbnail.jpg" % filename_base
if storage.exists(thumbnail_file_path):
# If thumbnail version exists, return its URL path
return "exists"
try:
# resize the original image and
# return URL path of the thumbnail version
f = storage.open(file_path, 'r')
image = Image.open(f)
width, height = image.size
thumbnail_size = 50, 50
if width > height:
delta = width - height
left = int(delta/2)
upper = 0
right = height + left
lower = height
else:
delta = height - width
left = 0
upper = int(delta/2)
rght = width
lower = width + upper
image = image.crop((left, upper, right, lower))
image = image.resize(thumbnail_size, Image.ANTIALIAS)
f_mob = storage.open(thumbnail_file_path, "w")
image.save(f_mob, "JPEG")
f_mob.close()
return "Success"
except:
raise IOError
return "error"
def clear_background(cls, slot_item: Image.Image, *, copy=False) -> Image.Image:
"""Clears the slot's background of an image.
:param slot_item: The slot's image.
:param copy: Whether to create a copy or alter the original.
:returns: The item's image without the slot's background.
"""
px = 0
py = 0
if copy:
slot_item = slot_item.copy()
background = {'Normal': 0, 'Gray': 0, 'Green': 0, 'Blue': 0, 'Violet': 0, 'Golden': 0, 'Other': -255}
for i in range(0, 32):
pixel = slot_item.getpixel((i, 0))
lum = (0.299 * pixel[0] + 0.587 * pixel[1] + 0.114 * pixel[2])
background[cls.get_background_type(lum)] += 1
for i in range(0, 32):
pixel = slot_item.getpixel((0, i))
lum = (0.299 * pixel[0] + 0.587 * pixel[1] + 0.114 * pixel[2])
background[cls.get_background_type(lum)] += 1
for i in range(0, 32):
pixel = slot_item.getpixel((31, i))
lum = (0.299 * pixel[0] + 0.587 * pixel[1] + 0.114 * pixel[2])
background[cls.get_background_type(lum)] += 1
# no point checking the last row since its always blanked out
# for i in range(0, 32):
# pixel = slot_item.getpixel((i, 31))
# lum = (0.299 * pixel[0] + 0.587 * pixel[1] + 0.114 * pixel[2])
# background[get_background_type(lum)]+=1
background_type = max(background.items(), key=operator.itemgetter(1))[0]
while py < slot_item.size[1] and py < slot[background_type].size[1]:
slot_item_pixel = slot_item.getpixel((px, py))
slot_pixel = slot[background_type].getpixel((px + 1, py + 1))
if slot_item_pixel[:3] == slot_pixel[:3]:
slot_item.putpixel((px, py), (255, 0, 255, 0))
px += 1
if px == slot_item.size[0] or px == slot[background_type].size[0]:
py += 1
px = 0
return slot_item
def scan_item(cls, slot_item: Image.Image, item_list: List[Dict[str, Any]]) -> \
Union[Dict[str, Union[str, int]], str]:
"""Scans an item's image, and looks for it among similar items in the database.
:param slot_item: The item's cropped image.
:param item_list: The list of similar items.
:return: The matched item, represented in a dictionary.
"""
results = {}
if slot_item is None:
return "Empty"
for item in item_list:
if item['id'] in results:
continue
item_image = pickle.loads(item['frame'])
item_image = Image.open(io.BytesIO(bytearray(item_image)))
px = 0
py = 0
while py < slot_item.size[1] and py < item_image.size[1]:
slot_item_pixel = slot_item.getpixel((px, py))
item_pixel = item_image.getpixel((px, py))
if cls.is_empty(item_pixel) == cls.is_empty(slot_item_pixel):
pass
elif cls.is_empty(slot_item_pixel):
if not cls.is_number(slot_item_pixel):
break
elif cls.is_empty(item_pixel) or item_pixel != slot_item_pixel:
break
px += 1
if px == slot_item.size[0] or px == item_image.size[0]:
py += 1
px = 0
if py == slot_item.size[1] or py == item_image.size[1]:
return item
return "Unknown"
def extract_boxes(im: Image.Image, bounds: Dict[str, Any]) -> Image:
"""
Yields the subimages of image im defined in the list of bounding boxes in
bounds preserving order.
Args:
im (PIL.Image.Image): Input image
bounds (list): A list of tuples (x1, y1, x2, y2)
Yields:
(PIL.Image) the extracted subimage
"""
if bounds['text_direction'].startswith('vertical'):
angle = 90
else:
angle = 0
for box in bounds['boxes']:
if isinstance(box, tuple):
box = list(box)
if (box < [0, 0, 0, 0] or box[::2] > [im.size[0], im.size[0]] or
box[1::2] > [im.size[1], im.size[1]]):
logger.error('bbox {} is outside of image bounds {}'.format(box, im.size))
raise KrakenInputException('Line outside of image bounds')
yield im.crop(box).rotate(angle, expand=True), box
import pathlib
from typing import Dict, Iterator, Literal, Optional, Set, Text, Tuple, Union
import cv2
import numpy as np
from PIL.Image import Image
from PIL.Image import open as open_image
from . import clients
LOGGER = logging.getLogger(__name__)
TARGET_WIDTH = 540
_CACHED_SCREENSHOT: Dict[Literal["value"], Tuple[dt.datetime, Image]] = {
"value": (dt.datetime.fromtimestamp(0), Image())
}
def invalidate_screeshot():
_CACHED_SCREENSHOT["value"] = (dt.datetime.fromtimestamp(0), Image())
class g:
last_screenshot_save_path: str = ""
screenshot_width = TARGET_WIDTH
def screenshot(*, max_age: float = 1) -> Image:
cached_time, _ = _CACHED_SCREENSHOT["value"]
if cached_time < dt.datetime.now() - dt.timedelta(seconds=max_age):
def __call__(self,
img: Image.Image,
boxes: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None):
return np.array(img.convert("RGB")), boxes, labels
def adjust_image_size(image: Image.Image, master: Image.Image) -> Image.Image:
'Adjusts the image size.'
return image.resize(master.size)
def resize(im: Image.Image, size):
if size != (512, 512):
im = im.resize(size, Image.ANTIALIAS)
return im
def crop(img: Image.Image, down=False):
p = 80
w, h = img.size
box = (0, p, w, h) if down else (0, 0, w, p)
return img.crop(box)
def __call__(self, img: Image.Image):
return img.resize(self.size, self.mode)
def smooth(source: Image.Image) -> Image.Image:
return source.copy().filter(ImageFilter.SMOOTH_MORE)
def save_png(img: Image.Image, filename: str, transparency=None):
with open(filename, "wb") as fd:
img.save(fd, "png", transparency=transparency)
def to_bytes(image: ImageType, image_format: str = "png") -> bytes:
buffer = io.BytesIO()
image.save(buffer, format=image_format)
return buffer.getvalue()
def embossing(image: Image.Image) -> Image.Image:
"""Cria uma versão metálica da imagem passada.
O algoritmo utilizado é o "embossing", e utiliza o kernel de convolução passado no vídeo relacionado à tarefa.
Parâmetros
----------
image : Image.Image
A imagem a partir da qual a nova imagem será gerada.
A imagem deve ser uma imagem da biblioteca PIL (ou Pillow), e deve ter formato RGB ou RGBA [err #1].
Retorno
-------
Image.Image
A imagem "metálica", gerada a partir da imagem passada. É uma imagem da biblioteca PIL (ou Pillow) em formato
RGBA.
Erros
-----
ValueError
[1] Caso a imagem passada esteja em um formato que não seja RGB ou RGBA.
"""
# Verificar se o formato da imagem está correto.
if image.mode != "RGB" and image.mode != "RGBA":
raise ValueError("[1] O formato da imagem deve ser RGB ou RGBA.")
# Construir o kernel de convolução.
kernel = ConvolutionKernel(matrix=[[0, 1, 1], [-1, 0, 1], [-1, -1, 0]],
anchor=(1, 1))
# Aplicar o kernel de convolução em cada layer da imagem, exceto a transparência, que será conservada da imagem
# original.
out_r = kernel.apply(function=lambda coord: image.getpixel(coord)[0],
limits=image.size,
weight=1,
default=0)
out_g = kernel.apply(function=lambda coord: image.getpixel(coord)[1],
limits=image.size,
weight=1,
default=0)
out_b = kernel.apply(function=lambda coord: image.getpixel(coord)[2],
limits=image.size,
weight=1,
default=0)
# Criar a imagem nova e colocar os novos valores de cada layer em cada pixel, após serem parametrizados. A
# aplicação do kernel terá resultados entre -765 e +765, que serão parametrizados linearmente para o intervalo
# 0 a +255.
out_image = Image.new(mode="RGBA", size=image.size)
for pixel_iy in range(out_image.size[1]):
for pixel_ix in range(out_image.size[0]):
out_image.putpixel(
xy=(pixel_ix, pixel_iy),
value=(int((out_r[pixel_ix][pixel_iy] + 765) / 6),
int((out_g[pixel_ix][pixel_iy] + 765) / 6),
int((out_b[pixel_ix][pixel_iy] + 765) / 6),
255 if image.mode == "RGB" else image.getpixel(
(pixel_ix, pixel_iy))[3]))
return out_image
def edges(source: Image.Image) -> Image.Image:
return source.copy().filter(ImageFilter.EDGE_ENHANCE_MORE)
def crop_self(self, bg_img: Image.Image):
return bg_img.crop(self.inner_box)
def update_image(self, image: Image.Image):
resized_image = image.resize((self.canvas_width, self.canvas_height))
self.photo_image = ImageTk.PhotoImage(resized_image, master=self)
self.itemconfigure(self.image_id, image=self.photo_image)
def __call__(self, img: Image.Image):
img = np.float32(img.convert('L')) / np.float32(255.0)
return img[np.newaxis, :]
def save_final_image(self, image: Image) -> None:
file_path = self.get_image_file_path()
image.save(file_path)
self.print_file_ready_message(file_path)
def compositeImgs(img_src:PILImage, img_dst:PILImage) -> PILImage:
img_dst.alpha_composite(img_src, dest=(0,0), source=(0,0))
return img_dst
def edge_detection(image: Image.Image) -> Image.Image:
"""Cria uma nova imagem com de arestas detectadas na imagem passada.
O algoritmo de detecção utiliza o kernel de convolução passado no vídeo relacionado à tarefa.
Parâmetros
----------
image : Image.Image
A imagem a partir da qual a nova imagem de arestas detectadas será gerada.
A imagem deve ser uma imagem da biblioteca PIL (ou Pillow), e deve ter formato RGB ou RGBA [err #1].
Retorno
-------
Image.Image
A imagem de arestas detectadas, gerada a partir da imagem passada. É uma imagem da biblioteca PIL (ou Pillow)
em formato RGBA.
Erros
-----
ValueError
[1] Caso a imagem passada esteja em um formato que não seja RGB ou RGBA.
"""
# Verificar se o formato da imagem está correto.
if image.mode != "RGB" and image.mode != "RGBA":
raise ValueError("[1] O formato da imagem deve ser RGB ou RGBA.")
# Construir o kernel de convolução.
kernel = ConvolutionKernel(matrix=[[0, -1, 0], [-1, 0, 1], [0, 1, 0]],
anchor=(1, 1))
# Construir matriz do brilho de cada pixel previamente, pois cada pixel será chamado múltiplas vezes, e calcular a
# média múltiplas vezes deixaria o código mais pesado. É considerado que o brilho é a média dos valores RGB.
brightness = [[
sum(image.getpixel((x_index, y_index))[:3]) / 3
for y_index in range(image.size[1])
] for x_index in range(image.size[0])]
# Matriz com valores de brilho (não-parametrizados, estão no range -510 a 510) para cada pixel após a aplicação do
# kernel.
out_brightness = kernel.apply(
function=lambda coord: brightness[coord[0]][coord[1]],
limits=image.size,
weight=1,
default=0)
# Criar a imagem nova e colocar os valores do brilho em cada pixel, após serem parametrizados. A parametrização é
# um simples módulo do brilho dividido por 2, e todos os pixels estão em escala de cinza.
out_image = Image.new(mode="RGBA", size=image.size)
for pixel_iy in range(out_image.size[1]):
for pixel_ix in range(out_image.size[0]):
out_image.putpixel(
xy=(pixel_ix, pixel_iy),
value=(int(abs(out_brightness[pixel_ix][pixel_iy]) / 2),
int(abs(out_brightness[pixel_ix][pixel_iy]) / 2),
int(abs(out_brightness[pixel_ix][pixel_iy]) / 2),
255 if image.mode == "RGB" else image.getpixel(
(pixel_ix, pixel_iy))[3]))
return out_image
def to_bytes(cls, content: img.Image) -> bytes:
dst = io.BytesIO()
content.save(dst, format="png")
return dst.getvalue()
def extend_random_crop(image: ImPIL.Image,
labels: np.ndarray,
ltrb: np.ndarray,
points: np.ndarray = None,
osize: tuple = (320, 320),
min_box_side: int = 30,
ignore_intersection: tuple = (0.5, 0.9),
aspect_ratio_bounds: tuple = (0.5, 2)):
r"""Does random crop and adjusts the boxes while maintaining minimum
box size. When not None, the points (xy locations within a bounding
box) are adjusted.
Args:
image (pil-image): pillow input image
labels (np.ndarray): labels of each box
ltrb (np.ndarray): object locations in ltrb format.
Requires 2D-array, with rows of (left, top, right, bottom) in
pixels.
points (np.ndarray, optional): (x, y) locations of landmarks within
a box. Expects a 3D-array with points.shape[0] = ltrb.shape[0]
and points.shape[2] = 2
osize (tuple/list): Output image size (width, height)
min_box_side (int): Minimum size of the box predicted by the model.
Default = 30 -- SSD minimum size
ignore_intersection (tuple/list of floats): avoids objects within
the intersection range in the final crop.
aspect_ratio_bounds (tuple/list of floats): allowed crop ratios
given an image
Return:
image, labels, ltrb, points
** requires some speed-up
"""
valid_points = None
# filter boxes with negative width -- not usual but a safe check
_valid = np.stack((ltrb[:, 2] - ltrb[:, 0], ltrb[:, 3] - ltrb[:, 1]))
_valid = _valid.min(0) > 2
labels, ltrb, points = labels[_valid], ltrb[_valid], points[_valid]
w, h = image.size
# minimum ltrb side on actual image
mbox = min((ltrb[:, 3] - ltrb[:, 1]).min(),
(ltrb[:, 2] - ltrb[:, 0]).min())
# min & max possible crop size to maintain min_box_side
mincw = int(mbox * 1.1)
maxcw = int(min(mincw * min(osize) / min_box_side, min(w, h)))
if mincw > maxcw:
mincw = maxcw - 1
# random width and height given all the above conditions
nw = random.randint(mincw, maxcw)
nh = random.randint(int(nw * aspect_ratio_bounds[0]),
int(nw * aspect_ratio_bounds[1]))
nh = min(max(nh, int(mbox * 1.1)), h)
# find all possible boxes, given nw and nh
all_ls, all_ts = np.arange(0, w - nw, 10), np.arange(0, h - nh, 10)
all_ls = all_ls.repeat(len(all_ts))
all_ts = np.tile(all_ts[None, :],
(len(np.arange(0, w - nw, 10)), 1)).reshape(-1)
possible = np.concatenate((all_ls[None, ], all_ts[None, ])).T
possible = np.concatenate([
possible[:, [0]], possible[:, [1]], possible[:, [0]] + nw,
possible[:, [1]] + nh
], 1)
# intersection in percentage to validate all possible boxes
lt = np.maximum(ltrb[:, :2][:, np.newaxis],
possible[:, :2][np.newaxis, :])
rb = np.minimum(ltrb[:, 2:][:, np.newaxis],
possible[:, 2:][np.newaxis, :])
intersection = np.multiply(*np.split(np.clip(rb - lt, 0, None), 2, 2))
intersection = intersection.squeeze(2)
area = ((ltrb[:, 2] - ltrb[:, 0]) * (ltrb[:, 3] - ltrb[:, 1]))
intersection = intersection / area[:, None]
idx = np.where((intersection > ignore_intersection[1]).sum(0))[0]
idx = [
x for x in idx
if not ((intersection[:, x] > ignore_intersection[0]) *
(intersection[:, x] < ignore_intersection[1])).any()
]
if len(idx) > 0:
# randomly pick one valid possible box
pick = random.randint(0, len(idx) - 1)
crop = possible[idx[pick]]
valid = intersection[:, idx[pick]] > ignore_intersection[1]
valid_ltrb = ltrb[valid].copy()
if points is not None:
valid_points = points[valid].copy()
valid_labels = labels[valid].copy()
else:
# if the above fails -- fall back to a single object
pick = random.randint(0, len(ltrb) - 1)
crop = ltrb[pick].copy()
# adjust crop - add some width and some height
rw_ = (crop[2] - crop[0]) * (random.random() * 0.2) + 0.05
_rw = (crop[2] - crop[0]) * (random.random() * 0.2) + 0.05
rh_ = (crop[3] - crop[1]) * (random.random() * 0.2) + 0.05
_rh = (crop[3] - crop[1]) * (random.random() * 0.2) + 0.05
crop[0] -= rw_
crop[1] -= rh_
crop[2] += _rw
crop[3] += _rh
valid_ltrb = ltrb[[pick]].copy()
if points is not None:
valid_points = points[[pick]].copy()
valid_labels = labels[[pick]].copy()
# adjust xy's
valid_ltrb[:, 0::2] -= crop[0]
valid_ltrb[:, 1::2] -= crop[1]
if points is not None:
valid_points[:, :, 0] -= crop[0]
valid_points[:, :, 1] -= crop[1]
image = image.crop(list(map(int, crop)))
w, h = image.size
image = image.resize(osize)
valid_ltrb[:, 0::2] *= osize[0] / w
valid_ltrb[:, 1::2] *= osize[1] / h
if points is not None:
valid_points[:, :, 0] *= osize[0] / w
valid_points[:, :, 1] *= osize[1] / h
valid_ltrb[:, 0::2] = np.clip(valid_ltrb[:, 0::2], 0, osize[0] - 1)
valid_ltrb[:, 1::2] = np.clip(valid_ltrb[:, 1::2], 0, osize[1] - 1)
if points is not None:
valid_points[:, :, 0] = np.clip(valid_points[:, :, 0], 0, osize[0])
valid_points[:, :, 1] = np.clip(valid_points[:, :, 1], 0, osize[1])
return image, valid_labels, valid_ltrb, valid_points
def invalidate_screeshot():
_CACHED_SCREENSHOT["value"] = (dt.datetime.fromtimestamp(0), Image())
def get_image_and_offset(
name: str, id: int, image: ImageType, off_x: int, off_y: int, copy_level: int
) -> Tuple[ImageType, int, int]: # image, off_x, off_y
# blame rob; honestly, I am done with doing all those exceptions at this point ~ nekit
if "robot" in name:
if f"{id}_02_" in name:
image = image.rotate(-45, resample=Image.BICUBIC, expand=True)
off_x -= 50 if copy_level else 40
off_y -= 20
if TWO in name:
if id in {2, 5, 6, 8, 9, 11, 12, 15, 17, 24}:
off_x += 15
off_y -= 5
elif id in {7, 10, 19, 20}:
off_x += 7
elif id == 13:
off_x += 10
off_y -= 4
elif id == 18:
off_x -= 1
off_y -= 1
elif id in {21, 25}:
off_x += 12
elif id == 22:
off_y -= 5
elif id in {3, 26}:
off_x += 1
elif id == 23:
off_x -= 3
off_y -= 2
elif f"{id}_03_" in name:
image = image.rotate(45, resample=Image.BICUBIC, expand=True)
off_x -= 40 if copy_level else 30
if TWO in name and id in {3, 5, 6, 8, 16, 17}:
off_x += 10
off_y -= 52 if id == 21 and TWO not in name else 60
elif f"{id}_04_" in name:
if copy_level:
off_x -= 10
off_y -= 70
elif "spider" in name:
if f"{id}_02_" in name:
if copy_level > 1:
off_x += 55
off_y -= 38
image = image.transpose(Image.FLIP_LEFT_RIGHT)
elif copy_level > 0:
off_x += 18
off_y -= 38
else:
off_x -= 16
off_y -= 38
elif f"{id}_03_" in name:
off_x -= 86
off_y -= 38
if id == 7:
off_x += 15
off_y += 13
elif id == 15:
off_x += 5
off_y += 3
if TWO in name:
if id == 16:
off_y += 5
elif id in {1, 8, 9, 11, 13, 14}:
off_x += 2
elif id in {2, 3}:
off_x += 25
elif id == 10:
off_x += 18
off_y -= 5
if GLOW in name:
off_y += 3
image = image.rotate(-45, resample=Image.BICUBIC, expand=True)
elif f"{id}_04_" in name:
off_x -= 30
off_y -= 20
return image, off_x, off_y
def pad(
img: Image.Image,
padding: Union[int, List[int], Tuple[int, ...]],
fill: Optional[Union[float, List[float], Tuple[float, ...]]] = 0,
padding_mode: Literal["constant", "edge", "reflect", "symmetric"] = "constant",
) -> Image.Image:
if not _is_pil_image(img):
raise TypeError(f"img should be PIL Image. Got {type(img)}")
if not isinstance(padding, (numbers.Number, tuple, list)):
raise TypeError("Got inappropriate padding arg")
if not isinstance(fill, (numbers.Number, str, tuple)):
raise TypeError("Got inappropriate fill arg")
if not isinstance(padding_mode, str):
raise TypeError("Got inappropriate padding_mode arg")
if isinstance(padding, list):
padding = tuple(padding)
if isinstance(padding, tuple) and len(padding) not in [1, 2, 4]:
raise ValueError(f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple")
if isinstance(padding, tuple) and len(padding) == 1:
# Compatibility with `functional_tensor.pad`
padding = padding[0]
if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
raise ValueError("Padding mode should be either constant, edge, reflect or symmetric")
if padding_mode == "constant":
opts = _parse_fill(fill, img, name="fill")
if img.mode == "P":
palette = img.getpalette()
image = ImageOps.expand(img, border=padding, **opts)
image.putpalette(palette)
return image
return ImageOps.expand(img, border=padding, **opts)
else:
if isinstance(padding, int):
pad_left = pad_right = pad_top = pad_bottom = padding
if isinstance(padding, tuple) and len(padding) == 2:
pad_left = pad_right = padding[0]
pad_top = pad_bottom = padding[1]
if isinstance(padding, tuple) and len(padding) == 4:
pad_left = padding[0]
pad_top = padding[1]
pad_right = padding[2]
pad_bottom = padding[3]
p = [pad_left, pad_top, pad_right, pad_bottom]
cropping = -np.minimum(p, 0)
if cropping.any():
crop_left, crop_top, crop_right, crop_bottom = cropping
img = img.crop((crop_left, crop_top, img.width - crop_right, img.height - crop_bottom))
pad_left, pad_top, pad_right, pad_bottom = np.maximum(p, 0)
if img.mode == "P":
palette = img.getpalette()
img = np.asarray(img)
img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right)), mode=padding_mode)
img = Image.fromarray(img)
img.putpalette(palette)
return img
img = np.asarray(img)
# RGB image
if len(img.shape) == 3:
img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right), (0, 0)), padding_mode)
# Grayscale image
if len(img.shape) == 2:
img = np.pad(img, ((pad_top, pad_bottom), (pad_left, pad_right)), padding_mode)
return Image.fromarray(img)
def __call__(self, image: Image.Image) -> Image.Image:
return image.resize(self.size, self.interpolation)
def hflip(img: Image.Image) -> Image.Image:
if not _is_pil_image(img):
raise TypeError(f"img should be PIL Image. Got {type(img)}")
return img.transpose(Image.FLIP_LEFT_RIGHT)
def resize_img(self, img: Image.Image):
width, height = img.size
scale = width / self.target_width
img = img.resize((int(width / scale), int(height / scale)))
return img
def vflip(img: Image.Image) -> Image.Image:
if not _is_pil_image(img):
raise TypeError(f"img should be PIL Image. Got {type(img)}")
return img.transpose(Image.FLIP_TOP_BOTTOM)
def reshape(x: Image.Image, h, w, resample=0):
"`resize` `x` to `(w,h)`"
return x.resize((w, h), resample=resample)
def ensure_rgb_format(image: Image.Image) -> Image.Image:
return image.convert("RGB")
def __call__(self, img: Image.Image):
if random.random() < self.p:
return img.transpose(Image.FLIP_LEFT_RIGHT)
return img
def to_bytes_format(im: Image.Image, format='png'):
"Convert to bytes, default to PNG format"
arr = io.BytesIO()
im.save(arr, format=format)
return arr.getvalue()