348 lines
12 KiB
Python
348 lines
12 KiB
Python
import logging
|
||
import math
|
||
import urllib.request
|
||
from io import BytesIO
|
||
|
||
import cv2
|
||
import numpy
|
||
from PIL import Image
|
||
from PIL.ExifTags import TAGS
|
||
from paddleclas import PaddleClas
|
||
from tenacity import retry, stop_after_attempt, wait_random
|
||
|
||
|
||
@retry(stop=stop_after_attempt(3), wait=wait_random(1, 3), reraise=True,
|
||
after=lambda x: logging.warning("获取图片失败！"))
|
||
def read(image_path):
|
||
"""
|
||
从网络或本地读取图片
|
||
:param image_path: 网络或本地路径
|
||
:return: NumPy数组形式的图片, EXIF数据
|
||
"""
|
||
if image_path.startswith("http"):
|
||
# 发送HTTP请求并获取图像数据
|
||
resp = urllib.request.urlopen(image_path, timeout=60)
|
||
# 将数据读取为字节流
|
||
image_data = resp.read()
|
||
else:
|
||
with open(image_path, "rb") as f:
|
||
image_data = f.read()
|
||
|
||
# 解析EXIF信息（基于原始字节流）
|
||
exif_data = {}
|
||
try:
|
||
# 用PIL打开原始字节流
|
||
with Image.open(BytesIO(image_data)) as img:
|
||
# 获取EXIF字典
|
||
exif_info = img._getexif()
|
||
if exif_info:
|
||
# 将EXIF标签的数字ID转换为可读名称（如36867对应"DateTimeOriginal"）
|
||
for tag_id, value in exif_info.items():
|
||
tag_name = TAGS.get(tag_id, tag_id)
|
||
exif_data[tag_name] = value
|
||
except Exception as e:
|
||
logging.error("解析EXIF信息失败", exc_info=e)
|
||
# 将字节流转换为NumPy数组
|
||
image_np = numpy.frombuffer(image_data, numpy.uint8)
|
||
# 解码NumPy数组为OpenCV图像格式
|
||
image = cv2.imdecode(image_np, cv2.IMREAD_COLOR)
|
||
return image, exif_data
|
||
|
||
|
||
def capture(image, rectangle):
|
||
"""
|
||
截取图片
|
||
:param image: 图片NumPy数组
|
||
:param rectangle: 要截取的矩形
|
||
:return: 截取之后的图片NumPy
|
||
"""
|
||
x1, y1, x2, y2 = rectangle
|
||
height, width = image.shape[:2]
|
||
if x1 < 0:
|
||
x1 = 0
|
||
if y1 < 0:
|
||
y1 = 0
|
||
if x2 > width:
|
||
x2 = width
|
||
if y2 > height:
|
||
y2 = height
|
||
return image[int(y1):int(y2), int(x1):int(x2)]
|
||
|
||
|
||
def split(image, ratio=1.414, overlap=0.05, x_compensation=3):
|
||
"""
|
||
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)
|
||
height, width = image.shape[:2]
|
||
hw_ratio = height / width
|
||
wh_ratio = width / height
|
||
|
||
if hw_ratio > ratio: # 纵向过长
|
||
new_img_height = width * ratio
|
||
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])
|
||
if cropped_img.shape[0] > 0:
|
||
# 计算误差可能导致图片高度为0，此时不添加
|
||
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])
|
||
if cropped_img.shape[1] > 0:
|
||
# 计算误差可能导致图片宽度为0，此时不添加
|
||
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
|
||
|
||
|
||
def parse_rotation_angles(image):
|
||
"""
|
||
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"]
|
||
:param image: 图片NumPy数组或文件路径
|
||
:return: 最有可能的两个角度
|
||
"""
|
||
angles = ['0', '90']
|
||
model = PaddleClas(model_name="text_image_orientation")
|
||
clas_result = model.predict(input_data=image)
|
||
try:
|
||
clas_result = next(clas_result)[0]
|
||
if clas_result["scores"][0] < 0.5:
|
||
return angles
|
||
angles = clas_result["label_names"]
|
||
except Exception as e:
|
||
logging.error("获取图片旋转角度失败", exc_info=e)
|
||
return angles
|
||
|
||
|
||
def rotate(image, angle):
|
||
"""
|
||
旋转图片
|
||
:param image: 图片NumPy数组
|
||
:param angle: 逆时针旋转角度
|
||
:return: 旋转后的图片NumPy数组
|
||
"""
|
||
if angle == 0:
|
||
return image
|
||
height, width = image.shape[:2]
|
||
if angle == 180:
|
||
new_width = width
|
||
new_height = height
|
||
else:
|
||
new_width = height
|
||
new_height = width
|
||
# 绕图像的中心旋转
|
||
# 参数：旋转中心 旋转度数 scale
|
||
matrix = cv2.getRotationMatrix2D((width / 2, height / 2), angle, 1)
|
||
# 旋转后平移
|
||
matrix[0, 2] += (new_width - width) / 2
|
||
matrix[1, 2] += (new_height - height) / 2
|
||
# 参数：原始图像 旋转参数 元素图像宽高
|
||
rotated = cv2.warpAffine(image, matrix, (new_width, new_height))
|
||
return rotated
|
||
|
||
|
||
def invert_rotate_point(point, center, angle):
|
||
"""
|
||
反向旋转图片上的点
|
||
:param point: 点
|
||
:param center: 旋转中心
|
||
:param angle: 旋转角度
|
||
:return: 旋转后的点坐标
|
||
"""
|
||
matrix = cv2.getRotationMatrix2D(center, angle, 1)
|
||
if angle != 180:
|
||
# 旋转后平移
|
||
matrix[0, 2] += center[1] - center[0]
|
||
matrix[1, 2] += center[0] - center[1]
|
||
|
||
reverse_matrix = cv2.invertAffineTransform(matrix)
|
||
|
||
point = numpy.array([[point[0]], [point[1]], [1]])
|
||
return numpy.dot(reverse_matrix, point)
|
||
|
||
|
||
def invert_rotate_rectangle(rectangle, center, angle):
|
||
"""
|
||
反向旋转图片上的矩形
|
||
:param rectangle: 矩形
|
||
:param center: 旋转中心
|
||
:param angle: 旋转角度
|
||
:return: 旋转后的矩形坐标
|
||
"""
|
||
if angle == 0:
|
||
return list(rectangle)
|
||
|
||
x1, y1, x2, y2 = rectangle
|
||
|
||
# 计算矩形的四个顶点
|
||
top_left = (x1, y1)
|
||
bot_left = (x1, y2)
|
||
top_right = (x2, y1)
|
||
bot_right = (x2, y2)
|
||
|
||
# 旋转矩形的四个顶点
|
||
rot_top_left = invert_rotate_point(top_left, center, angle).astype(int)
|
||
rot_bot_left = invert_rotate_point(bot_left, center, angle).astype(int)
|
||
rot_bot_right = invert_rotate_point(bot_right, center, angle).astype(int)
|
||
rot_top_right = invert_rotate_point(top_right, center, angle).astype(int)
|
||
|
||
# 找出旋转后矩形的新左上角和右下角坐标
|
||
new_top_left = (min(rot_top_left[0], rot_bot_left[0], rot_bot_right[0], rot_top_right[0]),
|
||
min(rot_top_left[1], rot_bot_left[1], rot_bot_right[1], rot_top_right[1]))
|
||
new_bot_right = (max(rot_top_left[0], rot_bot_left[0], rot_bot_right[0], rot_top_right[0]),
|
||
max(rot_top_left[1], rot_bot_left[1], rot_bot_right[1], rot_top_right[1]))
|
||
|
||
return [new_top_left[0], new_top_left[1], new_bot_right[0], new_bot_right[1]]
|
||
|
||
|
||
def expand_to_a4_size(image):
|
||
"""
|
||
以尽量少的方式将图片扩充到a4大小
|
||
:param image: 图片NumPy数组
|
||
:return: 扩充后的图片NumPy数组和偏移量
|
||
"""
|
||
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):
|
||
# 获取两张图片的高度和宽度
|
||
height1, width1 = img1.shape[:2]
|
||
height2, width2 = img2.shape[:2]
|
||
|
||
# 确保两张图片的高度相同
|
||
if height1 != height2:
|
||
# 如果高度不同，调整较小高度的图片
|
||
if height1 < height2:
|
||
img1 = cv2.resize(img1, (int(width1 * height2 / height1), height2))
|
||
else:
|
||
img2 = cv2.resize(img2, (int(width2 * height1 / height2), height1))
|
||
|
||
# 再次获取调整后的图片尺寸
|
||
height1, width1 = img1.shape[:2]
|
||
height2, width2 = img2.shape[:2]
|
||
|
||
# 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
|
||
total_width = width1 + width2
|
||
max_height = max(height1, height2)
|
||
combined_img = numpy.zeros((max_height, total_width, 3), dtype=numpy.uint8)
|
||
|
||
# 将img1和img2复制到新的图像中
|
||
combined_img[:height1, :width1] = img1
|
||
combined_img[:height2, width1:width1 + width2] = img2
|
||
return combined_img
|
||
|
||
|
||
def parse_clarity(image):
|
||
"""
|
||
判断图片清晰度
|
||
:param image: 图片NumPy数组或文件路径
|
||
:return: 判断结果及置信度
|
||
"""
|
||
clarity_result = [1, 0]
|
||
model = PaddleClas(inference_model_dir=r"model/clas/clarity_assessment", use_gpu=True)
|
||
clas_result = model.predict(input_data=image)
|
||
try:
|
||
clas_result = next(clas_result)[0]
|
||
clarity_result = [clas_result["class_ids"][0], clas_result["scores"][0]]
|
||
except Exception as e:
|
||
logging.error("获取图片清晰度失败", exc_info=e)
|
||
return clarity_result
|
||
|
||
|
||
def is_photo_by_exif(exif_tags):
|
||
"""分析EXIF数据判断是否为照片"""
|
||
# 照片通常包含的EXIF标签
|
||
photo_tags = [
|
||
'FNumber', # 光圈
|
||
'ExposureTime', # 曝光时间
|
||
'ISOSpeedRatings', # ISO
|
||
'FocalLength', # 焦距
|
||
'LensModel', # 镜头型号
|
||
'GPSLatitude' # GPS位置信息
|
||
]
|
||
|
||
# 统计照片相关的EXIF标签数量
|
||
photo_tag_count = 0
|
||
if exif_tags:
|
||
for tag in photo_tags:
|
||
if tag in exif_tags:
|
||
photo_tag_count += 1
|
||
# 如果有2个以上照片相关的EXIF标签，倾向于是照片
|
||
if photo_tag_count >= 2:
|
||
return True
|
||
# 不确定是照片返回False
|
||
return False
|
||
|
||
|
||
def is_screenshot_by_image_features(image):
|
||
"""分析图像特征判断是否为截图"""
|
||
# 定义边缘像素标准差阈值，小于此阈值则认为图片是截图
|
||
edge_std_threshold = 20.0
|
||
try:
|
||
# 检查边缘像素的一致性（截图边缘通常更整齐）
|
||
edge_pixels = []
|
||
# 取图像边缘10像素
|
||
edge_pixels.extend(image[:10, :].flatten()) # 顶部边缘
|
||
edge_pixels.extend(image[-10:, :].flatten()) # 底部边缘
|
||
edge_pixels.extend(image[:, :10].flatten()) # 左侧边缘
|
||
edge_pixels.extend(image[:, -10:].flatten()) # 右侧边缘
|
||
|
||
# 计算边缘像素的标准差（值越小说明越一致）
|
||
edge_std = numpy.std(edge_pixels)
|
||
logging.info(f"边缘像素标准差: {edge_std}")
|
||
return edge_std < edge_std_threshold
|
||
except Exception as e:
|
||
logging.error("图像特征分析失败", exc_info=e)
|
||
return False
|
||
|
||
|
||
def is_screenshot(image, exif_tags):
|
||
"""综合判断是否是截图"""
|
||
# 先检查EXIF数据
|
||
result_of_exif = is_photo_by_exif(exif_tags)
|
||
# 如果有明显的照片EXIF信息，直接判断为照片
|
||
if result_of_exif:
|
||
return False
|
||
# 分析图像特征
|
||
return is_screenshot_by_image_features(image)
|