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优化图片分割和拓展方法，兼容横向过长的图片

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liuyebo
2024-08-08 14:15:51 +08:00
parent dd8dcc0d3c
commit d296fa4414
3 changed files with 50 additions and 43 deletions
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87
util/image_util.py
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@@ -41,31 +41,36 @@ def capture(image, rectangle):
return image[int(y1):int(y2), int(x1):int(x2)]
def split(image, ratio=1.414, overlap=0.05):
def split(image, ratio=1.414, overlap=0.05, x_compensation=3):
"""
分割图片，只分割过长的图片，暂不处理过宽的图片
分割图片
:param image:图片，可以是NumPy数组或文件路径
:param ratio: 分割后的比例
:param overlap: 图片之间的覆盖比例
:param x_compensation: 横向补偿倍率
:return: 分割后的图片组(NumPy数组形式)
"""
split_result = []
if isinstance(image, str):
image = read(image)
# 获取图片的宽度和高度
height, width = image.shape[:2]
# 计算宽高比
img_ratio = height / width
# 检查是否需要裁剪
if img_ratio > ratio:
split_ratio = ratio - overlap
# 分割后的高度
hw_ratio = height / width
wh_ratio = width / height
if hw_ratio > ratio: # 纵向过长
new_img_height = width * ratio
for i in range(math.ceil(height / (width * split_ratio))):
offset = round(width * split_ratio * i)
# 参数形式为[y1:y2, x1:x2]
step = width * (ratio - overlap) # 偏移步长
for i in range(math.ceil(height / step)):
offset = round(step * i)
cropped_img = capture(image, [0, offset, width, offset + new_img_height])
split_result.append({"img": cropped_img, "x_offset": 0, "y_offset": offset})
elif wh_ratio > ratio: # 横向过长
new_img_width = height * ratio
step = height * (ratio - overlap * x_compensation) # 一般文字是横向的，所以横向截取时增大重叠部分
for i in range(math.ceil(width / step)):
offset = round(step * i)
cropped_img = capture(image, [offset, 0, offset + new_img_width, width])
split_result.append({"img": cropped_img, "x_offset": offset, "y_offset": 0})
else:
split_result.append({"img": image, "x_offset": 0, "y_offset": 0})
return split_result
@@ -171,38 +176,40 @@ def invert_rotate_rectangle(rectangle, center, angle):
return [new_top_left[0], new_top_left[1], new_bot_right[0], new_bot_right[1]]
def expand_to_a4_size(image, center=False):
def expand_to_a4_size(image):
"""
将图片扩充到a4大小
以尽量少的方式将图片扩充到a4大小
:param image: 图片NumPy数组
:param center: 是否将原图置于中间
:return: 扩充后的图片NumPy数组和偏移量
"""
h, w = image.shape[:2]
offset_x, offset_y = 0, 0
if h * 1.0 / w >= 1.42:
exp_w = int(h / 1.414 - w)
if center:
offset_x = int(exp_w / 2)
exp_img = numpy.zeros((h, offset_x, 3), dtype="uint8")
exp_img.fill(255)
image = numpy.hstack([exp_img, image, exp_img])
else:
exp_img = numpy.zeros((h, exp_w, 3), dtype="uint8")
exp_img.fill(255)
image = numpy.hstack([image, exp_img])
elif h * 1.0 / w <= 1.40:
exp_h = int(w * 1.414 - h)
if center:
offset_y = int(exp_h / 2)
exp_img = numpy.zeros((offset_y, w, 3), dtype="uint8")
exp_img.fill(255)
image = numpy.vstack([exp_img, image, exp_img])
else:
exp_img = numpy.zeros((exp_h, w, 3), dtype="uint8")
exp_img.fill(255)
image = numpy.vstack([image, exp_img])
return image, offset_x, offset_y
height, width = image.shape[:2]
x_offset, y_offset = 0, 0
hw_ratio = height / width
if hw_ratio >= 1.42:
exp_w = int(height / 1.414 - width)
x_offset = int(exp_w / 2)
exp_img = numpy.zeros((height, x_offset, 3), dtype="uint8")
exp_img.fill(255)
image = numpy.hstack([exp_img, image, exp_img])
elif 1 <= hw_ratio <= 1.40:
exp_h = int(width * 1.414 - height)
y_offset = int(exp_h / 2)
exp_img = numpy.zeros((y_offset, width, 3), dtype="uint8")
exp_img.fill(255)
image = numpy.vstack([exp_img, image, exp_img])
elif 0.72 <= hw_ratio < 1:
exp_w = int(height * 1.414 - width)
x_offset = int(exp_w / 2)
exp_img = numpy.zeros((height, x_offset, 3), dtype="uint8")
exp_img.fill(255)
image = numpy.hstack([exp_img, image, exp_img])
elif hw_ratio <= 0.7:
exp_h = int(width / 1.414 - height)
y_offset = int(exp_h / 2)
exp_img = numpy.zeros((y_offset, width, 3), dtype="uint8")
exp_img.fill(255)
image = numpy.vstack([exp_img, image, exp_img])
return image, x_offset, y_offset
def combined(img1, img2):