272 lines
8.7 KiB
Python
272 lines
8.7 KiB
Python
# coding=utf-8
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import os
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import time
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import logging
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logger = logging.getLogger(__name__)
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def clean_station_name(name):
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"""
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清理场站名称，去除特殊字符和距离信息
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"""
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if not name:
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return ""
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# 驿来特可能特有的清理逻辑
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import re
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# 移除常见的括号备注
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name = re.sub(r'\(.*?\)', '', name)
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name = re.sub(r'（.*?）', '', name)
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return name.strip()
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def take_screenshot(d, filename, save_dir=None):
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"""
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获取屏幕截图并保存
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"""
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if not save_dir:
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from Config.Config import TEMP_IMAGE_DIR
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save_dir = TEMP_IMAGE_DIR
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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# 确保文件名有后缀
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if not filename.endswith(".jpg") and not filename.endswith(".png"):
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filename = f"{filename}.jpg"
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full_path = os.path.join(save_dir, filename)
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d.screenshot(full_path)
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return full_path
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import hashlib
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import numpy as np
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import cv2
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def get_file_md5(file_path):
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"""计算文件的 MD5 值"""
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if not os.path.exists(file_path):
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return None
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hash_md5 = hashlib.md5()
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with open(file_path, "rb") as f:
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for chunk in iter(lambda: f.read(4096), b""):
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hash_md5.update(chunk)
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return hash_md5.hexdigest()
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def get_image_content_md5(file_path, top_ratio=0.1, bottom_ratio=0.1):
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"""
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计算图片核心内容的 MD5 值（排除状态栏和导航栏）
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"""
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img = read_image(file_path)
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if img is None:
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return None
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h, w = img.shape[:2]
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top = int(h * top_ratio)
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bottom = int(h * (1 - bottom_ratio))
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# 裁剪中间部分
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content = img[top:bottom, :]
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# 直接使用原始像素数组计算 MD5，避免压缩损失
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return hashlib.md5(content.tobytes()).hexdigest()
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def read_image(path):
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"""读取图片，支持中文路径"""
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if not path or not os.path.exists(path):
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return None
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try:
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data = np.fromfile(path, dtype=np.uint8)
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if data.size == 0:
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return None
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img = cv2.imdecode(data, -1)
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return img
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except Exception as e:
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logger.error(f"Error reading image {path}: {e}")
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return None
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def save_image(path, img):
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"""保存图片，支持中文路径"""
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try:
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ext = os.path.splitext(path)[1]
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if not ext:
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ext = ".jpg"
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cv2.imencode(ext, img)[1].tofile(path)
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return True
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except Exception as e:
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logger.error(f"Error saving image {path}: {e}")
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return False
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def find_template(image_path, template_name, threshold=0.8):
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"""
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在图片中查找模板位置
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"""
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img = read_image(image_path)
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if img is None:
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return None
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template_path = os.path.join(os.path.dirname(__file__), "Template", template_name)
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if not os.path.exists(template_path):
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logger.warning(f"Template not found: {template_path}")
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return None
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template = read_image(template_path)
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if template is None:
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return None
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res = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED)
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min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
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if max_val >= threshold:
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t_h, t_w = template.shape[:2]
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center_x = max_loc[0] + t_w // 2
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center_y = max_loc[1] + t_h // 2
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return (center_x, center_y)
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return None
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def detect_cards_cv(image_path, top_ratio=0.15, bottom_ratio=0.1):
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"""
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使用计算机图形学 (OpenCV) 检测列表中的场站卡片
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方案：使用 Canny 边缘检测 + 轮廓查找，提取圆角矩形卡片
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"""
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img = read_image(image_path)
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if img is None:
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return []
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h, w = img.shape[:2]
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# 1. 预处理：转灰度 -> 高斯模糊去噪
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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blurred = cv2.GaussianBlur(gray, (5, 5), 0)
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# 2. Canny 边缘检测
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# 阈值设置：降低阈值以适应低对比度边缘 (如浅灰背景上的白卡片)
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# 经过测试，(10, 50) 对于浅灰背景上的白卡片效果最佳
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edges = cv2.Canny(blurred, 10, 50)
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# 3. 查找轮廓
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contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cards = []
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min_card_width = int(w * 0.8) # 卡片宽度通常占屏幕 80% 以上
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min_card_height = 150 # 卡片高度至少 150px
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top_limit = int(h * top_ratio)
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bottom_limit = int(h * (1 - bottom_ratio))
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valid_contours = []
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for cnt in contours:
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x, y, cw, ch = cv2.boundingRect(cnt)
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# 筛选条件：
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# 1. 宽度足够大 (排除小图标、文字)
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# 2. 高度适中
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# 3. 位置在安全区域内 (排除顶部导航和底部 TabBar)
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if (cw > min_card_width and
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ch > min_card_height and
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y > top_limit and
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y + ch < bottom_limit):
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# 简单去重：检查是否与已有的卡片高度重叠
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is_duplicate = False
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center_y = y + ch // 2
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for vx, vy, vcw, vch in valid_contours:
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v_center_y = vy + vch // 2
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if abs(center_y - v_center_y) < 50: # 中心点接近，视为同一个
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is_duplicate = True
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break
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if not is_duplicate:
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valid_contours.append((x, y, cw, ch))
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# 增加一点 padding，收缩一下边界，避免点到边缘
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padding = 5
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cards.append([
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max(0, x + padding),
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max(0, y + padding),
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min(w, x + cw - padding),
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min(h, y + ch - padding)
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])
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# 按 y 坐标排序
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cards.sort(key=lambda c: c[1])
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# 如果没找到，尝试稍微放宽条件兜底 (比如宽度 > 60%)
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if not cards:
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logger.warning("严格轮廓检测未找到卡片，尝试放宽条件...")
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for cnt in contours:
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x, y, cw, ch = cv2.boundingRect(cnt)
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if (cw > int(w * 0.6) and ch > 100 and y > top_limit and y + ch < bottom_limit):
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# 简单去重
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center_y = y + ch // 2
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is_duplicate = False
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for existing in cards:
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ec_y = (existing[1] + existing[3]) // 2
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if abs(center_y - ec_y) < 50:
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is_duplicate = True
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break
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if not is_duplicate:
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cards.append([x, y, x+cw, y+ch])
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cards.sort(key=lambda c: c[1])
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logger.info(f"最终检测到 {len(cards)} 个场站卡片区域")
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return cards
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def clear_temp_dir(save_dir=None):
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"""清空临时目录中的所有文件"""
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if save_dir is None:
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from Config.Config import TEMP_IMAGE_DIR
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save_dir = TEMP_IMAGE_DIR
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if not os.path.exists(save_dir):
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return
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for f in os.listdir(save_dir):
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file_path = os.path.join(save_dir, f)
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try:
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if os.path.isfile(file_path):
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os.remove(file_path)
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except Exception as e:
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logger.error(f"Error deleting file {file_path}: {e}")
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def get_image_md5(image_path):
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"""
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计算图片的 MD5 值
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"""
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import hashlib
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if not os.path.exists(image_path):
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return ""
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with open(image_path, 'rb') as f:
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data = f.read()
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return hashlib.md5(data).hexdigest()
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def draw_rectangles(image_path, points, output_path=None):
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"""
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使用 OpenCV 在图片上绘制矩形框 (计算机图形学绘制)
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:param image_path: 原始图片路径
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:param points: 坐标点列表，格式如 [[x1, y1, x2, y2], ...] 或 [(x, y), ...]
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:param output_path: 输出图片路径，如果不指定则覆盖原图
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"""
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try:
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import cv2
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import numpy as np
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from Apps.YeLiTe.Config.Setting import DRAW_DEBUG_BOXES, DEBUG_BOX_COLOR, DEBUG_BOX_THICKNESS
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if not DRAW_DEBUG_BOXES:
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return image_path
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img = read_image(image_path)
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if img is None:
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return image_path
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for p in points:
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if len(p) == 4: # 矩形 [x1, y1, x2, y2]
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cv2.rectangle(img, (int(p[0]), int(p[1])), (int(p[2]), int(p[3])), DEBUG_BOX_COLOR, DEBUG_BOX_THICKNESS)
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elif len(p) == 2: # 点 (x, y)，画一个小圆圈或正方形表示点击位
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center = (int(p[0]), int(p[1]))
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cv2.circle(img, center, 10, DEBUG_BOX_COLOR, -1)
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save_path = output_path if output_path else image_path
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save_image(save_path, img)
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return save_path
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except Exception as e:
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logger.error(f"绘制矩形框失败: {e}")
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return image_path
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