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HuangHai
2026-01-15 09:16:20 +08:00
parent f8af4dd9ae
commit b49dd9bc1b
4 changed files with 130 additions and 33 deletions
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59
Apps/TeLaiDian/Crawler.py
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@@ -298,86 +298,81 @@ class TeLaiDianCrawler(BaseCrawler):
w, h = d.window_size()
# 1. 增加等待时间，确保页面加载完成
logger.info(f"[详情页] 等待 {WAIT_DETAIL_PAGE_LOAD + 2}s 确保页面稳定 (根据要求多等会)...")
await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD + 2)
logger.info(f"[详情页] 等待 {WAIT_DETAIL_PAGE_LOAD + 1}s 确保页面稳定...")
await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD + 1)
# 2. 向上滑动一部分屏 (从屏幕底部的 85% 划到顶部的 15%)
# [优化] 避免滑过头，从全屏改为 0.7 屏幕左右
logger.info("[详情页] 执行向上滑动操作 (y: 85% -> 15%)")
d.swipe(w // 2, int(h * 0.85), w // 2, int(h * 0.15), duration=0.8)
await asyncio.sleep(2.0) # 滑动后再次等待稳定
# 2. 向上滑动大一些，确保“全部时段”露出来
logger.info("[详情页] 执行大范围向上滑动 (y: 90% -> 10%)，确保显示全部时段")
d.swipe(w // 2, int(h * 0.9), w // 2, int(h * 0.1), duration=1.0)
await asyncio.sleep(2.5) # 滑动后等待稳定
# 3. 截图并识别
final_screen_path = take_screenshot(d, f"tld_detail_vlm_input_{int(time.time())}.jpg")
logger.info(f"[详情页] 已截取 VLM 识别用图: {final_screen_path}")
# 3. 截图并识别 (纯 OCR 流程)
final_screen_path = take_screenshot(d, f"tld_detail_ocr_input_{int(time.time())}.jpg")
logger.info(f"[详情页] 已截取 OCR 识别用图: {final_screen_path}")
logger.info("[详情页] 优先使用 VLM 识别顶部“价格”标签和价格入口")
price_tab_screen = final_screen_path
entrance_clicked = False
try:
# 1. 尝试使用 VLM 寻找顶部“价格”标签
tab_data = await self.read_image_kit.find_price_tab_vlm(final_screen_path)
# 1. 使用 OCR 寻找顶部“价格”标签
tab_data = await self.read_image_kit.find_price_tab_ocr(final_screen_path)
tab_x, tab_y = None, None
if tab_data.get("found") and tab_data.get("point"):
p = tab_data["point"]
tab_x = int(p[0] * w / 1000)
tab_y = int(p[1] * h / 1000)
logger.info(f"[详情页] VLM 成功找到价格标签: 归一化{p} -> 像素({tab_x}, {tab_y})")
logger.info(f"[详情页] OCR 成功找到价格标签: 归一化{p} -> 像素({tab_x}, {tab_y})")
else:
tab_x = int(PRICE_TAB_X_NORM * w / 1000)
tab_y = int(PRICE_TAB_Y_NORM * h / 1000)
logger.warning(f"[详情页] VLM 未找到价格标签，使用固定坐标兜底: 像素({tab_x}, {tab_y})")
logger.warning(f"[详情页] OCR 未找到价格标签，使用固定坐标兜底: 像素({tab_x}, {tab_y})")
logger.info(f"[详情页] 正在点击价格标签...")
d.click(tab_x, tab_y)
await asyncio.sleep(2.0) # 点击标签后多等会
await asyncio.sleep(2.5) # 点击标签后等待
price_tab_screen = take_screenshot(d, f"tld_detail_after_price_tab_{int(time.time())}.jpg")
logger.info(f"[详情页] 点击价格标签后的界面截图已保存: {price_tab_screen}")
# 2. 尝试使用 VLM 寻找价格入口 (重点识别“全部时段 >”)
entry_data = await self.read_image_kit.find_price_entrance_vlm(price_tab_screen)
# 2. 寻找并点击“全部时段”入口 (纯 OCR 识别)
entry_data = await self.read_image_kit.find_price_entrance_ocr(price_tab_screen)
entry_x, entry_y = None, None
if entry_data.get("found") and entry_data.get("point"):
p = entry_data["point"]
# 安全校验：严禁点击 y > 850 (85%) 的区域，那是底部浮动条
if p[1] > 850:
logger.warning(f"[详情页] VLM 虽返回坐标 {p}，但 Y 轴过大 (疑似底部浮动条)，强制判定为未找到。")
# 安全校验：严禁点击 y > 900 的区域，那是底部浮动条
if p[1] > 900:
logger.warning(f"[详情页] OCR 返回坐标 {p} 偏低 (疑似底部浮动条)，尝试使用固定坐标兜底")
entry_x = int(PRICE_ENTRY_X_NORM * w / 1000)
entry_y = int(PRICE_ENTRY_Y_NORM * h / 1000)
else:
entry_x = int(p[0] * w / 1000)
entry_y = int(p[1] * h / 1000)
logger.info(f"[详情页] VLM 成功找到价格入口 (匹配全部时段标识): 归一化{p} -> 像素({entry_x}, {entry_y})，原因: {entry_data.get('reason')}")
logger.info(f"[详情页] OCR 成功找到价格入口: 归一化{p} -> 像素({entry_x}, {entry_y})")
else:
entry_x = int(PRICE_ENTRY_X_NORM * w / 1000)
entry_y = int(PRICE_ENTRY_Y_NORM * h / 1000)
logger.warning(f"[详情页] VLM 未找到价格入口，使用固定坐标兜底: 像素({entry_x}, {entry_y})")
logger.warning(f"[详情页] OCR 未找到价格入口，使用固定坐标兜底: 像素({entry_x}, {entry_y})")
click_x = max(5, min(w - 5, entry_x))
click_y = max(5, min(h - 5, entry_y))
debug_click_path = price_tab_screen.replace(
".jpg",
f"_click_tab_{tab_x}_{tab_y}_entry_{click_x}_{click_y}.jpg"
)
# 绘制诊断图
debug_click_path = price_tab_screen.replace(".jpg", f"_click_diag.jpg")
try:
img = read_image(price_tab_screen)
if img is not None:
# 绘制两个点击位置
cv2.circle(img, (tab_x, tab_y), 20, (0, 0, 255), -1)
cv2.circle(img, (click_x, click_y), 20, (0, 0, 255), -1)
cv2.circle(img, (click_x, click_y), 20, (0, 255, 0), -1)
save_image(debug_click_path, img)
logger.info(f"[详情页] 已生成价格点击诊断红点图: {debug_click_path}")
logger.info(f"[详情页] 已生成 OCR 点击诊断图 (红点:价格标签, 绿点:入口): {debug_click_path}")
except Exception as e:
logger.error(f"[详情页] 生成诊断图片失败: {e}")
logger.info(f"[详情页] 正在点击电价入口...")
d.click(click_x, click_y)
entrance_clicked = True
await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD)
await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD + 1)
except Exception as e:
logger.error(f"[详情页] 识别或点击价格入口失败: {e}")

102
Apps/TeLaiDian/ReadImageKit.py
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@@ -140,6 +140,108 @@ class ReadImageKit:
logger.error(f"VLM 寻找价格入口失败: {e}")
return {"found": False}
async def find_price_tab_ocr(self, image_path):
"""
使用 OCR 在详情页顶部标签栏中寻找“价格”标签
不再使用 VLM
"""
from Apps.TeLaiDian.Kit import draw_rectangles
img = read_image(image_path)
if img is None:
return {"found": False}
h, w = img.shape[:2]
reader = get_easyocr_reader(gpu=True)
# 限制只在顶部 30% 区域查找标签
roi = img[0:int(h*0.3), :]
results = reader.read_text(roi)
for (quad, text, prob) in results:
if '价格' in text and prob > 0.3:
# 转换坐标 (加上 ROI 的偏移，这里 y 偏移是 0)
res = reader.get_normalized_rect(quad, w, h)
center_x = (res[0] + res[2]) // 2
center_y = (res[1] + res[3]) // 2
# 绘制诊断图
actual_p = [int(center_x * w / 1000), int(center_y * h / 1000)]
bbox = [int(res[0] * w / 1000), int(res[1] * h / 1000), int(res[2] * w / 1000), int(res[3] * h / 1000)]
draw_rectangles(image_path, bboxes=[bbox], click_points=[actual_p])
logger.info(f"[OCR] 已定位顶部价格标签并保存诊断图: {image_path.replace('.jpg', '_tab_vl.jpg')}")
return {
"found": True,
"reason": f"OCR 定位到‘{text}’",
"point": [center_x, center_y]
}
logger.warning(f"[OCR] 未能在顶部区域定位到‘价格’标签")
return {"found": False}
async def find_price_tab_ocr(self, image_path):
"""
使用 OCR 在详情页寻找顶部“价格”标签
"""
img = read_image(image_path)
if img is None:
return {"found": False}
h, w = img.shape[:2]
from Util.EasyOcrKit import get_easyocr_reader
reader = get_easyocr_reader(gpu=True)
# 顶部标签栏通常在屏幕上部 10%-30% 区域
# 我们先识别所有文字，然后过滤出“价格”
results = reader.read_text(img)
for (quad, text, prob) in results:
if "价格" in text and prob > 0.3:
# 获取中心点
rect = reader.get_normalized_rect(quad, w, h)
center_x = (rect[0] + rect[2]) // 2
center_y = (rect[1] + rect[3]) // 2
# 校验位置：必须在屏幕上半部分
if center_y < 500:
logger.info(f"[OCR] 找到顶部价格标签: '{text}', 置信度: {prob:.4f}, 坐标: [{center_x}, {center_y}]")
return {
"found": True,
"point": [center_x, center_y]
}
return {"found": False}
async def find_price_entrance_ocr(self, image_path):
"""
使用 OCR 在详情页寻找价格入口 (全部时段 >)
不再使用 VLM 和 掩码 CV
"""
container_norm = detect_price_info_container_cv(image_path)
if container_norm:
# 找到矩形框 [x1, y1, x2, y2]
x1, y1, x2, y2 = container_norm
# 计算中心点
center_x = (x1 + x2) // 2
center_y = (y1 + y2) // 2
# 绘制诊断图 (复用之前的逻辑)
img = read_image(image_path)
if img is not None:
h, w = img.shape[:2]
actual_p = [int(center_x * w / 1000), int(center_y * h / 1000)]
bbox = [int(x1 * w / 1000), int(y1 * h / 1000), int(x2 * w / 1000), int(y2 * h / 1000)]
draw_rectangles(image_path, bboxes=[bbox], click_points=[actual_p])
logger.info(f"[OCR] 已定位价格入口并保存诊断图: {image_path.replace('.jpg', '_vl.jpg')}")
return {
"found": True,
"reason": "OCR 精准定位到‘全部时段’文字",
"point": [center_x, center_y]
}
logger.warning(f"[OCR] 未能在页面中定位到‘全部时段’")
return {"found": False}
async def find_close_button_vlm(self, image_path):
"""
使用 VLM 在页面上寻找关闭按钮（用于清理广告弹窗）

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Apps/TelaiDian/__pycache__/Crawler.cpython-310.pyc
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Apps/TelaiDian/__pycache__/ReadImageKit.cpython-310.pyc
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