271 lines
9.2 KiB
Python
271 lines
9.2 KiB
Python
import logging
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import math
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import urllib.request
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import cv2
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import numpy
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from paddleclas import PaddleClas
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from tenacity import retry, stop_after_attempt, wait_random
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@retry(stop=stop_after_attempt(3), wait=wait_random(1, 3), reraise=True,
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after=lambda x: logging.warning("获取图片失败！"))
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def read(image_path):
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"""
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从网络或本地读取图片
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:param image_path: 网络或本地路径
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:return: NumPy数组形式的图片
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"""
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if image_path.startswith("http"):
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# 发送HTTP请求并获取图像数据
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resp = urllib.request.urlopen(image_path, timeout=60)
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# 将数据读取为字节流
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image_data = resp.read()
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# 将字节流转换为NumPy数组
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image_np = numpy.frombuffer(image_data, numpy.uint8)
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# 解码NumPy数组为OpenCV图像格式
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image = cv2.imdecode(image_np, cv2.IMREAD_COLOR)
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else:
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image = cv2.imread(image_path)
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return image
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def capture(image, rectangle):
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"""
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截取图片
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:param image: 图片NumPy数组
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:param rectangle: 要截取的矩形
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:return: 截取之后的图片NumPy
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"""
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x1, y1, x2, y2 = rectangle
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height, width = image.shape[:2]
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if x1 < 0:
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x1 = 0
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if y1 < 0:
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y1 = 0
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if x2 > width:
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x2 = width
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if y2 > height:
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y2 = height
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return image[int(y1):int(y2), int(x1):int(x2)]
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def split(image, ratio=1.414, overlap=0.05, x_compensation=3):
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"""
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分割图片
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:param image:图片，可以是NumPy数组或文件路径
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:param ratio: 分割后的比例
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:param overlap: 图片之间的覆盖比例
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:param x_compensation: 横向补偿倍率
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:return: 分割后的图片组(NumPy数组形式)
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"""
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split_result = []
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if isinstance(image, str):
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image = read(image)
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height, width = image.shape[:2]
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hw_ratio = height / width
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wh_ratio = width / height
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if hw_ratio > ratio: # 纵向过长
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new_img_height = width * ratio
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step = width * (ratio - overlap) # 偏移步长
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for i in range(math.ceil(height / step)):
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offset = round(step * i)
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cropped_img = capture(image, [0, offset, width, offset + new_img_height])
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if cropped_img.shape[0] > 0:
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# 计算误差可能导致图片高度为0，此时不添加
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split_result.append({"img": cropped_img, "x_offset": 0, "y_offset": offset})
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elif wh_ratio > ratio: # 横向过长
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new_img_width = height * ratio
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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
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for i in range(math.ceil(width / step)):
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offset = round(step * i)
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cropped_img = capture(image, [offset, 0, offset + new_img_width, width])
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if cropped_img.shape[1] > 0:
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# 计算误差可能导致图片宽度为0，此时不添加
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split_result.append({"img": cropped_img, "x_offset": offset, "y_offset": 0})
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else:
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split_result.append({"img": image, "x_offset": 0, "y_offset": 0})
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return split_result
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def parse_rotation_angles(image):
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"""
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判断图片旋转角度，逆时针旋转该角度后为正。可能值["0", "90", "180", "270"]
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:param image: 图片NumPy数组或文件路径
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:return: 最有可能的两个角度
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"""
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angles = ['0', '90']
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model = PaddleClas(model_name="text_image_orientation")
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clas_result = model.predict(input_data=image)
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try:
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clas_result = next(clas_result)[0]
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if clas_result["scores"][0] < 0.5:
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return angles
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angles = clas_result["label_names"]
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except Exception as e:
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logging.error("获取图片旋转角度失败", exc_info=e)
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return angles
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def rotate(image, angle):
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"""
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旋转图片
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:param image: 图片NumPy数组
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:param angle: 逆时针旋转角度
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:return: 旋转后的图片NumPy数组
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"""
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if angle == 0:
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return image
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height, width = image.shape[:2]
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if angle == 180:
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new_width = width
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new_height = height
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else:
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new_width = height
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new_height = width
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# 绕图像的中心旋转
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# 参数：旋转中心 旋转度数 scale
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matrix = cv2.getRotationMatrix2D((width / 2, height / 2), angle, 1)
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# 旋转后平移
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matrix[0, 2] += (new_width - width) / 2
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matrix[1, 2] += (new_height - height) / 2
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# 参数：原始图像 旋转参数 元素图像宽高
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rotated = cv2.warpAffine(image, matrix, (new_width, new_height))
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return rotated
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def invert_rotate_point(point, center, angle):
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"""
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反向旋转图片上的点
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:param point: 点
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:param center: 旋转中心
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:param angle: 旋转角度
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:return: 旋转后的点坐标
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"""
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matrix = cv2.getRotationMatrix2D(center, angle, 1)
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if angle != 180:
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# 旋转后平移
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matrix[0, 2] += center[1] - center[0]
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matrix[1, 2] += center[0] - center[1]
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reverse_matrix = cv2.invertAffineTransform(matrix)
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point = numpy.array([[point[0]], [point[1]], [1]])
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return numpy.dot(reverse_matrix, point)
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def invert_rotate_rectangle(rectangle, center, angle):
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"""
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反向旋转图片上的矩形
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:param rectangle: 矩形
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:param center: 旋转中心
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:param angle: 旋转角度
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:return: 旋转后的矩形坐标
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"""
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if angle == 0:
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return list(rectangle)
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x1, y1, x2, y2 = rectangle
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# 计算矩形的四个顶点
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top_left = (x1, y1)
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bot_left = (x1, y2)
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top_right = (x2, y1)
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bot_right = (x2, y2)
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# 旋转矩形的四个顶点
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rot_top_left = invert_rotate_point(top_left, center, angle).astype(int)
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rot_bot_left = invert_rotate_point(bot_left, center, angle).astype(int)
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rot_bot_right = invert_rotate_point(bot_right, center, angle).astype(int)
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rot_top_right = invert_rotate_point(top_right, center, angle).astype(int)
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# 找出旋转后矩形的新左上角和右下角坐标
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new_top_left = (min(rot_top_left[0], rot_bot_left[0], rot_bot_right[0], rot_top_right[0]),
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min(rot_top_left[1], rot_bot_left[1], rot_bot_right[1], rot_top_right[1]))
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new_bot_right = (max(rot_top_left[0], rot_bot_left[0], rot_bot_right[0], rot_top_right[0]),
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max(rot_top_left[1], rot_bot_left[1], rot_bot_right[1], rot_top_right[1]))
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return [new_top_left[0], new_top_left[1], new_bot_right[0], new_bot_right[1]]
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def expand_to_a4_size(image):
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"""
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以尽量少的方式将图片扩充到a4大小
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:param image: 图片NumPy数组
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:return: 扩充后的图片NumPy数组和偏移量
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"""
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height, width = image.shape[:2]
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x_offset, y_offset = 0, 0
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hw_ratio = height / width
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if hw_ratio >= 1.42:
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exp_w = int(height / 1.414 - width)
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x_offset = int(exp_w / 2)
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exp_img = numpy.zeros((height, x_offset, 3), dtype="uint8")
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exp_img.fill(255)
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image = numpy.hstack([exp_img, image, exp_img])
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elif 1 <= hw_ratio <= 1.40:
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exp_h = int(width * 1.414 - height)
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y_offset = int(exp_h / 2)
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exp_img = numpy.zeros((y_offset, width, 3), dtype="uint8")
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exp_img.fill(255)
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image = numpy.vstack([exp_img, image, exp_img])
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elif 0.72 <= hw_ratio < 1:
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exp_w = int(height * 1.414 - width)
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x_offset = int(exp_w / 2)
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exp_img = numpy.zeros((height, x_offset, 3), dtype="uint8")
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exp_img.fill(255)
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image = numpy.hstack([exp_img, image, exp_img])
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elif hw_ratio <= 0.7:
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exp_h = int(width / 1.414 - height)
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y_offset = int(exp_h / 2)
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exp_img = numpy.zeros((y_offset, width, 3), dtype="uint8")
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exp_img.fill(255)
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image = numpy.vstack([exp_img, image, exp_img])
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return image, x_offset, y_offset
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def combined(img1, img2):
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# 获取两张图片的高度和宽度
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height1, width1 = img1.shape[:2]
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height2, width2 = img2.shape[:2]
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# 确保两张图片的高度相同
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if height1 != height2:
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# 如果高度不同，调整较小高度的图片
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if height1 < height2:
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img1 = cv2.resize(img1, (int(width1 * height2 / height1), height2))
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else:
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img2 = cv2.resize(img2, (int(width2 * height1 / height2), height1))
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# 再次获取调整后的图片尺寸
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height1, width1 = img1.shape[:2]
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height2, width2 = img2.shape[:2]
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# 创建一个空白的图像，宽度等于两张图片的宽度之和，高度等于它们共同的高度
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total_width = width1 + width2
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max_height = max(height1, height2)
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combined_img = numpy.zeros((max_height, total_width, 3), dtype=numpy.uint8)
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# 将img1和img2复制到新的图像中
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combined_img[:height1, :width1] = img1
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combined_img[:height2, width1:width1 + width2] = img2
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return combined_img
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def parse_clarity(image):
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"""
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判断图片清晰度
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:param image: 图片NumPy数组或文件路径
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:return: 判断结果及置信度
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"""
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clarity_result = [1, 0]
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model = PaddleClas(inference_model_dir=r"model/clas/clarity_assessment", use_gpu=True)
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clas_result = model.predict(input_data=image)
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try:
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clas_result = next(clas_result)[0]
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clarity_result = [clas_result["class_ids"][0], clas_result["scores"][0]]
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except Exception as e:
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logging.error("获取图片清晰度失败", exc_info=e)
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return clarity_result
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