447 lines
16 KiB
Python
447 lines
16 KiB
Python
import asyncio
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import json
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import os
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import sys
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import cv2
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import numpy as np
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project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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if project_root not in sys.path:
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sys.path.append(project_root)
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from Apps.TeLaiDian.Kit import setup_logger, get_ocr_reader, draw_rectangles
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from Apps.TeLaiDian.Config.Setting import SAFE_EXCLUDE_RATIO, BOTTOM_SAFE_EXCLUDE_RATIO
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from Util.LlmUtil import get_llm_response
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logger = setup_logger("TeLaiDian.FirstPageKit")
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TEXT_TOP_RATIO = SAFE_EXCLUDE_RATIO * 0.8
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NON_STATION_KEYWORDS = [
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"地图",
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"目的地",
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"电站名",
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"充电礼",
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"再充",
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"注册礼",
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"元券",
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"PLUS会员",
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"我的收藏",
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"最近充电",
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"我的卡券",
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"我的订单",
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"充电券",
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"电信积分兑换",
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"确认",
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"广告",
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"距离/区域",
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"综合排序",
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"偏好",
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"星级站",
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"停车减免",
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"重卡可用",
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"首页",
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"特省钱",
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"扫码",
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"输入",
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"商城",
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"推荐",
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]
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def _load_image(path):
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if not os.path.exists(path):
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raise FileNotFoundError(path)
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img = cv2.imread(path)
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if img is None:
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raise RuntimeError(f"无法读取图片: {path}")
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h, w = img.shape[:2]
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return img, w, h
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def _extract_json(text: str) -> str:
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if not text:
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return "[]"
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cleaned = text.strip()
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if "```" in cleaned:
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lines = []
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for line in cleaned.splitlines():
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if line.strip().startswith("```"):
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continue
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lines.append(line)
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cleaned = "\n".join(lines).strip()
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decoder = json.JSONDecoder()
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pos = 0
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while pos < len(cleaned):
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idx_dict = cleaned.find("{", pos)
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idx_list = cleaned.find("[", pos)
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candidates = [i for i in (idx_dict, idx_list) if i != -1]
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if not candidates:
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break
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start = min(candidates)
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snippet = cleaned[start:]
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try:
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_, end = decoder.raw_decode(snippet)
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return snippet[:end]
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except json.JSONDecodeError:
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pos = start + 1
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continue
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return "[]"
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async def run_ocr_rect(image_path, log_path=None):
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log_lines = []
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def log_detail(msg):
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logger.info(msg)
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log_lines.append(msg)
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img, w, h = _load_image(image_path)
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log_detail(f"开始处理图片: {image_path}, 宽={w}, 高={h}")
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reader = get_ocr_reader()
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ocr_results = reader.read_text(img)
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log_detail(f"OCR 原始结果数量: {len(ocr_results)}")
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entries = []
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for idx, (quad, text, prob) in enumerate(ocr_results):
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pts = np.array(quad).astype(int)
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x_min = int(np.min(pts[:, 0]))
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y_min = int(np.min(pts[:, 1]))
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x_max = int(np.max(pts[:, 0]))
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y_max = int(np.max(pts[:, 1]))
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cx = (x_min + x_max) / 2.0
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cy = (y_min + y_max) / 2.0
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cx_norm = cx / w
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cy_norm = cy / h
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status = "keep"
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reasons = []
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txt = text or ""
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if prob < 0.3:
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status = "drop"
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reasons.append("prob<0.3")
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if not txt:
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status = "drop"
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reasons.append("empty_text")
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has_station_kw = False
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has_distance_kw = False
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if txt:
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if ("充电站" in txt) or ("超快充" in txt) or ("快充" in txt) or ("慢充" in txt):
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has_station_kw = True
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if ("km" in txt) or ("m" in txt):
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has_distance_kw = True
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if cy_norm < TEXT_TOP_RATIO and not (
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(has_station_kw and len(txt) >= 4) or has_distance_kw
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):
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status = "drop"
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reasons.append("top_safe_zone")
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if cy_norm > (1 - BOTTOM_SAFE_EXCLUDE_RATIO):
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status = "drop"
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reasons.append("bottom_safe_zone")
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if status == "keep" and txt:
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for kw in NON_STATION_KEYWORDS:
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if kw and kw in txt:
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status = "drop"
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reasons.append("non_station_keyword")
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break
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log_detail(
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f"OCR[{idx + 1}] text={repr(text)} prob={prob:.3f} "
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f"cx_norm={cx_norm:.4f} cy_norm={cy_norm:.4f} "
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f"status={status} reasons={','.join(reasons) if reasons else '-'}"
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)
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if status != "keep":
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continue
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entries.append(
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{
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"text": text,
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"prob": float(prob),
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"cx_norm": cx_norm,
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"cy_norm": cy_norm,
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}
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)
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log_detail(f"OCR 通过过滤的有效文本数量: {len(entries)}")
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if not entries:
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log_detail("无有效 OCR 文本, 结束当前图片处理")
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return
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indexed_entries = []
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for idx, e in enumerate(entries):
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indexed_entries.append(
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{
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"id": idx + 1,
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"text": e["text"],
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"prob": e["prob"],
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"cx_norm": round(e["cx_norm"], 4),
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"cy_norm": round(e["cy_norm"], 4),
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}
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)
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payload_json = json.dumps(indexed_entries, ensure_ascii=False)
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log_detail(f"传给 LLM 的 OCR 条目数: {len(indexed_entries)}")
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query_text = (
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"下面是特来电列表页整张截图的 OCR 结果，每一项代表一行文字，包含其中心点的归一化坐标：\n"
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"ocr_items = " + payload_json + "\n"
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"请你根据这些文本，将它们聚合成若干个“充电场站卡片”。输出一个 JSON 数组，每个元素必须包含:\n"
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"1) station_name: 场站名称，只能是卡片标题中的名称，不允许是筛选标签、导航按钮、底部功能区等。\n"
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"2) anchor_point_norm: 一个对象 {\"x\": number, \"y\": number}，表示该场站名称文字所在行的中心点坐标，取值范围 0-1。\n"
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"并且尽量补充以下可选字段（找不到时可以省略或设为 null）:\n"
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"3) distance_text: 距离字符串，例如 \"6.9km\"、\"500m\"，从对应卡片中的距离行提取。\n"
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"4) busy_list: 忙闲信息数组，数组中的每一项是 {\"mode\": \"快|慢|超|普通\", \"idle\": number, \"total\": number}。\n"
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" 在特来电 UI 中，忙闲文本通常是 \"空闲x/总y\" 或 \"快 空闲x/总y\" 这种形式，\n"
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" 请从相应行中解析出模式和空闲/总数。\n"
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"额外提示：\n"
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"- 每个场站卡片通常包含一行类似 \"1.4km\"、\"3.6km\" 的距离文本；\n"
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"- 该距离文本所在行的左侧、且在同一卡片中的那一行文字，就是对应的场站标题 station_name；\n"
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"- 忙闲信息通常出现在卡片右侧的彩色小块中，例如 \"快 空闲24/32\"、\"慢 空闲0/10\" 等；\n"
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"要求：\n"
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"- 场站按从上到下排序；\n"
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"- station_name 不能取距离行本身（如 \"1.4km\"），而是要取与之成一对的标题行；\n"
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"- 如果某些 OCR 文本显然不属于任何场站卡片，可以忽略；\n"
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"- 只输出 JSON 数组，不要输出其它任何文字。"
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)
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chunks = []
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async for part in get_llm_response(
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query_text=query_text,
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stream=False,
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system_prompt="你是一个帮助整理 OCR 文本的助手，只输出 JSON。",
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chat_history=None,
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temperature=0,
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):
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chunks.append(part)
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full_text = "".join(chunks)
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log_detail("LLM 原始返回内容开始")
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log_lines.append(full_text)
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log_detail("LLM 原始返回内容结束")
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raw = _extract_json(full_text)
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log_detail(f"从 LLM 返回内容中抽取出的 JSON 片段: {raw}")
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try:
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data = json.loads(raw)
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except Exception as e:
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log_detail(f"解析 LLM 返回 JSON 失败: {e}")
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logs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Logs")
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os.makedirs(logs_dir, exist_ok=True)
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if log_path is None:
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final_log_path = os.path.join(
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logs_dir, os.path.basename(image_path).replace(".jpg", ".log")
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)
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mode = "w"
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else:
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final_log_path = log_path
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mode = "a"
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with open(final_log_path, mode, encoding="utf-8") as f:
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for line in log_lines:
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f.write(line + "\n")
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log_detail(f"已写入详细日志到: {final_log_path}")
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return
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if not isinstance(data, list):
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log_detail(f"期望 LLM 返回 JSON 数组, 实际类型: {type(data)}")
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return
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if not data:
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log_detail("LLM 返回空数组, 结束当前图片处理")
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return
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stations = []
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for idx, item in enumerate(data):
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if not isinstance(item, dict):
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log_detail(f"LLM item[{idx}] 不是对象类型, 跳过")
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continue
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name = item.get("station_name") or item.get("name")
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anchor = item.get("anchor_point_norm") or item.get("anchor")
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distance = item.get("distance_text") or item.get("distance")
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busy_list = item.get("busy_list") or []
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if not name or not isinstance(anchor, dict):
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log_detail(f"LLM item[{idx}] 缺少必要字段, 跳过: {item}")
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continue
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ax = float(anchor.get("x", 0.5))
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ay = float(anchor.get("y", 0.5))
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if not (0 <= ax <= 1 and 0 <= ay <= 1):
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log_detail(f"LLM item[{idx}] anchor_point_norm 超出范围, 跳过: {anchor}")
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continue
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px = int(ax * w)
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py = int(ay * h)
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stations.append(
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{
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"station_name": name,
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"anchor_point_norm": {"x": ax, "y": ay},
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"distance_text": distance,
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"busy_list": busy_list,
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"anchor_px": px,
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"anchor_py": py,
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}
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)
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log_detail(
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f"LLM anchor 规范化[{len(stations)}] name={name} ax={ax:.4f} ay={ay:.4f} "
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f"px={px} py={py} distance={distance} busy_list={busy_list}"
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)
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if not stations:
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log_detail("LLM 解析后无有效场站, 结束当前图片处理")
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return
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filtered = []
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for s in stations:
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dl = s.get("distance_text")
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bl = s.get("busy_list") or []
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ok_dist = isinstance(dl, str) and (("km" in dl) or ("m" in dl))
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ok_busy = isinstance(bl, list) and len(bl) > 0
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if ok_dist:
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filtered.append(s)
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if not ok_busy:
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log_detail(f"场站缺少忙闲信息但保留: {s.get('station_name')}")
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else:
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log_detail(f"丢弃缺少距离信息的条目: {s.get('station_name')}")
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stations = filtered
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if not stations:
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log_detail("过滤后无有效场站, 结束当前图片处理")
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return
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stations.sort(key=lambda s: s["anchor_py"])
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min_gap = None
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if len(stations) >= 2:
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gaps = []
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for i in range(1, len(stations)):
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gaps.append(stations[i]["anchor_py"] - stations[i - 1]["anchor_py"])
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min_gap = min(gaps)
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box_h = min_gap
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if box_h < int(h * 0.20):
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box_h = int(h * 0.20)
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if box_h > int(h * 0.32):
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box_h = int(h * 0.32)
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log_detail(f"根据最小锚点间距调整 box_h: min_gap={min_gap}, final={box_h}")
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else:
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box_h = int(h * 0.22)
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log_detail(f"仅有一个场站, 使用默认 box_h={box_h}")
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box_w = int(w * 0.90)
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x1_fixed = int((w - box_w) / 2)
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x2_fixed = x1_fixed + box_w
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result = []
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rects = []
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click_points = []
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effective_top = int(h * SAFE_EXCLUDE_RATIO)
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effective_bottom = int(h * (1 - BOTTOM_SAFE_EXCLUDE_RATIO))
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prev_y2 = None
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anchor_ratio = 0.15
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for idx, st in enumerate(stations, start=1):
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name = st.get("station_name")
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py = st["anchor_py"]
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anchor_norm = st.get("anchor_point_norm") or {}
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try:
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ay = float(anchor_norm.get("y", py / h))
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except Exception:
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ay = py / h
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y1 = int(py - box_h * anchor_ratio)
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y2 = y1 + box_h
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if min_gap is not None:
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downward_limit = int(min_gap * 0.75)
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max_y2 = py + downward_limit
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if y2 > max_y2:
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y2 = max_y2
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y1 = y2 - box_h
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if y1 < 0:
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y1 = 0
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y2 = y1 + box_h
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if y2 > h:
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y2 = h
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y1 = y2 - box_h
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if y2 > effective_bottom:
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y2 = effective_bottom
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y1 = y2 - box_h
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if prev_y2 is not None and y1 <= prev_y2:
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shift = prev_y2 - y1 + 1
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y1 += shift
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y2 += shift
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if y2 > effective_bottom:
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if idx == len(stations):
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new_y1 = prev_y2 + 1
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new_y2 = effective_bottom
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if new_y2 <= new_y1:
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log_detail(f"底部空间不足，丢弃: {st.get('station_name')}")
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continue
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y1 = new_y1
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y2 = new_y2
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else:
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log_detail(f"避免重叠无法放置，丢弃: {st.get('station_name')}")
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continue
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rect = [x1_fixed, y1, x2_fixed, y2]
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cx = int((rect[0] + rect[2]) / 2)
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cy = int((rect[1] + rect[3]) / 2)
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click_points.append([cx, cy])
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rects.append(rect)
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prev_y2 = y2
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item = {
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"index": idx,
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"station_name": st["station_name"],
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"rect": rect,
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"click_point": [cx, cy],
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"distance_text": st.get("distance_text"),
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"busy_list": st.get("busy_list") or [],
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}
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result.append(item)
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log_detail(
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f"Station[{idx}] name={item['station_name']} "
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f"rect={rect} click={item['click_point']} "
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f"distance={item['distance_text']} busy_list={item['busy_list']}"
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)
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try:
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draw_rectangles(image_path, rects, click_points, save_vl=False)
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except Exception as e:
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log_detail(f"绘制调试矩形失败: {e}")
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logs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Logs")
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os.makedirs(logs_dir, exist_ok=True)
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if log_path is None:
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final_log_path = os.path.join(
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logs_dir, os.path.basename(image_path).replace(".jpg", ".log")
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)
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mode = "w"
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else:
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final_log_path = log_path
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mode = "a"
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with open(final_log_path, mode, encoding="utf-8") as f:
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for line in log_lines:
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f.write(line + "\n")
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log_detail(f"已写入详细日志到: {final_log_path}")
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return result
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async def run_batch_in_dir(image_dir, log_file=None):
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img_files = []
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for name in os.listdir(image_dir):
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lower = name.lower()
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if not (lower.endswith(".jpg") or lower.endswith(".png")):
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continue
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if "_flag" in lower or "_vl" in lower:
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continue
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img_files.append(os.path.join(image_dir, name))
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img_files.sort()
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if not img_files:
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logger.info(f"目录下未找到图片: {image_dir}")
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return
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for idx, path in enumerate(img_files, start=1):
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logger.info(f"[批处理] 开始处理第 {idx} 张图片: {path}")
|
||
try:
|
||
await run_ocr_rect(path, log_path=log_file)
|
||
except Exception as e:
|
||
logger.exception(f"[批处理] 处理图片失败: {path}, {e}")
|
||
|