265 lines
9.5 KiB
Python
265 lines
9.5 KiB
Python
import os
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import math
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from PIL import Image
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from concurrent.futures import ThreadPoolExecutor
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def resize_and_reduce_quality(image_path, output_path, target_width = None):
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try:
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# 检查图片文件大小
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if os.path.getsize(image_path) < 10 * 1024 * 1024: # 10MB
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print("图片文件大小小于10MB，不进行调整")
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return image_path
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# 打开图片
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with Image.open(image_path) as img:
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# 计算新的高度以保持宽高比
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if target_width is None:
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target_width = img.width
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aspect_ratio = img.height / img.width
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new_height = int(target_width * aspect_ratio)
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# 调整图片大小
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img_resized = img.resize((target_width, new_height), Image.LANCZOS)
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# 降低图片质量
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quality = 70 # 质量从1（最差）到95（最好），可以根据需要调整
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img_resized.save(output_path, quality=quality)
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return output_path
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except Exception as e:
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return f"调整图片大小和质量时出现问题: {str(e)}"
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def get_picture_nums(source_path: str) -> int:
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picture_count = 0
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for root, dirs, files in os.walk(source_path):
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for file in files:
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if file.lower().endswith(('.jpg', '.jpeg', '.png')) and not file.startswith('merged_thumbnail'):
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picture_count += 1
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return picture_count
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def collect_defect_data(
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Y: str,
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picture_dir: str,
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search_file_list: list = [],
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) -> tuple[int, dict]:
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"""
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收集指定年份的缺陷图片数据，并根据布尔值决定是否扫描特定类型的缺陷图。
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Args:
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Y: 叶片号，如 "Y1"、"Y2"、"Y3"
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picture_dir: 图片根目录
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search_file_list (list, optional): 要搜索的文件列表.规定为3个元素
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Returns:
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(缺陷图片总数, 缺陷图片文件名字典)
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"""
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total_num = 0
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result_dict = {}
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try:
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for defect_type in search_file_list:
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dir_path = os.path.join(picture_dir, Y, defect_type)
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num, img_dict = get_picture_nums_and_image_with_name(dir_path)
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total_num += num
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result_dict.update(img_dict)
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except Exception as e:
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print(f"获取图片数据时出现问题: {str(e)},搜寻的目录：{dir_path}")
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return total_num, result_dict
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def get_picture_nums_and_image_with_name(source_path: str) -> tuple[int, dict]:
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"""
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获取指定目录下图片的数量，并返回每个图片的路径和名称（字典）
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Args:
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source_path (str): 要搜索的目录路径
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Returns:
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tuple: 包含两个元素的元组
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picture_count (int): 图片数量
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image_with_name (dict): 图片路径和名称的字典，格式为 {图片名称: 图片完整路径}
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"""
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picture_count = 0
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image_with_name = {}
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name_list = []
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for root, dirs, files in os.walk(source_path):
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for file in files:
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if file.lower().endswith(('.jpg', '.jpeg', '.png')) and not file.startswith('merged_thumbnail'):
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picture_count += 1
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image_with_name[os.path.splitext(file)[0]] = os.path.join(root, file)
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return picture_count, image_with_name
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def find_image(directory, image_name):
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"""
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在指定目录中查找指定名称的图片文件
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参数:
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directory (str): 要搜索的目录路径
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image_name (str): 要查找的图片文件名(可带扩展名或不带)
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返回:
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str: 找到的图片完整路径，如果未找到则返回None
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"""
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# 支持的图片扩展名列表
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image_extensions = ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp']
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# 遍历目录中的所有文件
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for root, dirs, files in os.walk(directory):
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for file in files:
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# 获取文件名和扩展名
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filename, ext = os.path.splitext(file)
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# 检查是否匹配图片名称(带或不带扩展名)
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if (file.lower() == image_name.lower() or
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filename.lower() == image_name.lower() and ext.lower() in image_extensions):
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return os.path.join(root, file)
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return None
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async def make_Thumbnail(source_path: str, output_path: str, size: tuple = (436, 233)) -> str:
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"""获取目录下所有图片，将所有图片合并制作略缩图并保存
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Args:
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source_path: 源目录
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output_path: 输出目录
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size: 合并后的略缩图总大小 (宽度, 高度)
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"""
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print("略缩图处理中")
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try:
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if not os.path.exists(output_path):
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print(f"无输出目录，创建中，输出目录为：{output_path}")
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os.makedirs(output_path)
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except Exception as e:
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print(f"输出目录有问题：{e}")
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return ""
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#如果存在merged_thumbnail.jpg文件，则直接返回该文件路径
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if os.path.exists(os.path.join(output_path,'merged_thumbnail.jpg')):
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print(f"已有略缩图，不用处理, 目前如需重新生成，请去往{output_path}目录 删除 merged_thumbnail.jpg 图片")
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"""
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此处可预留接口，询问用户是否重新生成一份略缩图
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"""
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return os.path.join(output_path,'merged_thumbnail.jpg')
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print("目录中无略缩图，合并略缩图中")
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# 获取源目录下所有的图片文件
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try:
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image_files = []
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for root, dirs, files in os.walk(source_path):
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for file in files:
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if file.lower().endswith(('.jpg', '.jpeg', '.png')):
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image_files.append(os.path.join(root, file))
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except Exception as e:
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print(f"递归获取图片失败,原因：{e}")
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if not image_files:
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print("源目录中没有找到图片文件")
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return ""
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# 计算每个缩略图的大小
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num_images = len(image_files)
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target_width, target_height = size
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# 计算最佳的缩略图排列方式
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# 先尝试计算每行可以放多少个缩略图
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aspect_ratio = target_width / target_height
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cols = math.ceil(math.sqrt(num_images * aspect_ratio))
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rows = math.ceil(num_images / cols)
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# 计算单个缩略图的大小
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thumb_width = target_width // cols
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thumb_height = target_height // rows
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# 创建线程池处理图片
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with ThreadPoolExecutor() as executor:
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thumbnails = list(executor.map(
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lambda file: create_thumbnail(file, (thumb_width, thumb_height)),
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image_files
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))
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# 过滤掉 None 值
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thumbnails = [thumb for thumb in thumbnails if thumb is not None]
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if not thumbnails:
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print("没有成功创建任何略缩图")
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return ""
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# 计算实际需要的行数和列数
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actual_cols = min(len(thumbnails), cols)
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actual_rows = math.ceil(len(thumbnails) / actual_cols)
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# 创建合并后的图像
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merged_image = Image.new('RGB', (actual_cols * thumb_width, actual_rows * thumb_height))
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# 粘贴缩略图
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for index, thumb in enumerate(thumbnails):
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row = index // actual_cols
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col = index % actual_cols
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merged_image.paste(thumb, (col * thumb_width, row * thumb_height))
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# 如果最终尺寸不完全匹配，调整大小
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if merged_image.size != size:
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merged_image = merged_image.resize(size, Image.LANCZOS)
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# 保存合并后的略缩图
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merged_thumbnail_path = os.path.join(output_path, 'merged_thumbnail.jpg')
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merged_image.save(merged_thumbnail_path)
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print(f"合并后的略缩图已保存到：{merged_thumbnail_path}")
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return merged_thumbnail_path
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def create_thumbnail(file_path: str, size: tuple) -> Image:
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"""创建单个图片的略缩图
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Args:
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file_path: 图片文件路径
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size: 缩略图大小
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"""
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try:
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with Image.open(file_path) as img:
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# 保持原始宽高比
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img.thumbnail(size, Image.LANCZOS)
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# 创建新图像确保尺寸一致
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new_img = Image.new('RGB', size)
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new_img.paste(img, ((size[0] - img.width) // 2, (size[1] - img.height) // 2))
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return new_img
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except Exception as e:
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print(f"图片处理有问题：{e}")
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return None
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def process_picture_data(picture_data : list[dict],
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image_source_to_find : list[str],
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quexian_type : str,
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dianxing_type : str,
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other_type : str
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) -> tuple[list[str], list[str]]:
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"""处理从数据库获取的图片数据
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Args:
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picture_data (list[dict]): 图片数据
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image_source_to_find (list[str]): 要查找的图片来源枚举(外内防雷)
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quexian_type (str): 缺陷类型枚举值
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dianxing_type (str): 典型类型枚举值
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other_type (str): 其他类型枚举值
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Returns:
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tuple(
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defct_pictures, 缺陷图列表
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dianxing_pictures, 典型图列表
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other_pictures, 其他图列表
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total_num 总图片数量
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)
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"""
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#过滤目标来源的图片数据
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picture_data = [pic for pic in picture_data if pic['imageSource'] in image_source_to_find]
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#分别择出缺陷图和典型图
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return ([pic for pic in picture_data if pic['imageType'] == quexian_type],
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[pic for pic in picture_data if pic['imageType'] == dianxing_type],
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[pic for pic in picture_data if pic['imageType'] == other_type],
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len(picture_data))
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