chore: 同步本地更改
- 新增 aio-mcp 项目框架 - 新增 .trae/skills/ OCR/SKU 设计工具 - 更新 auto-check 配置和 stress 包 - 删除 fnhelp 文档目录 - 更新 Docker 压力测试脚本
This commit is contained in:
张威33321
366
.trae/skills/ocr-extract/parse_sku_prices.py
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366
.trae/skills/ocr-extract/parse_sku_prices.py
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import json
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import re
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import os
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# Load OCR results
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with open(r"D:\workspace\membank\售价\ocr_results.json", "r", encoding="utf-8") as f:
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all_results = json.load(f)
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# Extract structured SKU prices from each image
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# Pattern: SKU name patterns like "蜗牛Nas-xxx-4g+64g", "J1900-4g+32g", etc.
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# Prices appear as decimal numbers typically followed by a discount of 5.00
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def extract_sku_prices(text_lines):
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"""Extract SKU name and price pairs from OCR text lines"""
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entries = []
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i = 0
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while i < len(text_lines):
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text, conf = text_lines[i]
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text = text.strip()
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# Look for SKU names containing known patterns
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sku_patterns = [
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r'(?:蜗牛Nas|蜗牛|Nas|cNas)[\s\w\-\.\+\(\)一-鿿]+?\d+g[+一-鿿]*\d*g',
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r'(?:J1900|j1900|J3160|j3160|I3|i3|I5|i5|1037U|1037u|N5105|N5100|N5095|J3355|J3060)[\-\.\+\(\)\s\w一-鿿]*?\d+g[\+一-鿿]*\d*g',
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r'(?:J1900|j1900|J3160|j3160|I3|i3|I5|i5|1037U|1037u|N5105|N5100|N5095|J3355|J3060)[\-\.\+\(\)\s\w一-鿿]*?\d+g',
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r'(?:16G[BW])[\-\s]*(?:windows|蜗牛|群晖)',
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r'(?:DDR|D3|D4)[\-\.\s]*\d+\s*[Gg]',
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]
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is_sku = False
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for pat in sku_patterns:
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if re.search(pat, text, re.IGNORECASE):
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is_sku = True
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break
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if is_sku:
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sku_name = text
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# Look for price pattern nearby: two numbers where one is ~5.00 off from the other
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# Pattern: price1 (like 247.50) ... 5.00 ... price2 (like 242.50)
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prices_found = []
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for j in range(i, min(i + 10, len(text_lines))):
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t2 = text_lines[j][0].strip()
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# Match decimal prices
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price_match = re.findall(r'\d{2,4}\.\d{2}', t2)
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for p in price_match:
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prices_found.append(float(p))
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# Also match integer prices
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price_match2 = re.findall(r'(?<!\d\.)(?:\d{2,4})(?:\.\d{2})?(?!\d|\.\d{3,})', t2)
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for p in price_match2:
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try:
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prices_found.append(float(p))
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except:
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pass
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# Deduplicate and find likely actual price (the larger number, minus 5 = smaller number)
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prices_found = sorted(set(prices_found))
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actual_price = None
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for p in prices_found:
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if p > 10 and (p - 5) in prices_found:
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actual_price = p # The larger one is the original price
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break
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if actual_price:
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discounted = actual_price - 5
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entries.append({
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'sku': sku_name,
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'price': discounted,
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'original_price': actual_price,
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'confidence': conf
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})
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i += 1
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return entries
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def extract_prices_v2(text_lines):
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"""
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Better approach: parse each image by looking for patterns:
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SKU name -> (price, discount 5.00, discounted_price)
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"""
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results = []
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# Flatten texts
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all_text = [t[0].strip() for t in text_lines]
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all_with_conf = list(text_lines)
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# Known CPU/memory patterns
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pattern = re.compile(
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r'.*?(?:蜗牛|Nas|NAS|nas|cNas|黑裙|黑群晖|客服|咨询).*?(?:'
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r'J1900|j1900|J3160|j3160|'
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r'i3|I3|i5|I5|'
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r'1037[Uu]|1037u|'
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r'N5105|N5100|N5095|'
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r'J3355|J3060|'
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r'16G[Bb]'
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r').*',
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re.IGNORECASE
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)
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# Scan through all texts looking for price patterns
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for i, (text, conf) in enumerate(all_with_conf):
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text = text.strip()
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# Skip non-SKU lines
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if len(text) < 3:
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continue
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# Check if this looks like a SKU description
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has_cpu = bool(re.search(r'(J1900|j1900|J3160|j3160|i3|I3|i5|I5|1037[Uu]|N5105|N5100|N5095|J3355|J3060|J4125)', text))
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has_nas = bool(re.search(r'(蜗牛|Nas|NAS|cNas|黑裙)', text))
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has_mem = bool(re.search(r'\d+g[\+一-鿿]*\d+g|\d+g\b|16G[Bb]', text))
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if has_cpu or (has_nas and has_mem) or ('16G' in text.upper() and ('windows' in text.lower() or '蜗牛' in text or '群晖' in text)):
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# This is a SKU name - look for nearby prices
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sku_name = text
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# Look at next 8 lines for prices
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prices = []
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for j in range(i+1, min(i+12, len(all_with_conf))):
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t = all_with_conf[j][0].strip()
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# Find decimal numbers
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nums = re.findall(r'(\d{2,4}\.\d{2})', t)
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prices.extend([float(n) for n in nums])
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# Also look at previous 2 lines
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for j in range(max(0, i-2), i):
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t = all_with_conf[j][0].strip()
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nums = re.findall(r'(\d{2,4}\.\d{2})', t)
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prices.extend([float(n) for n in nums])
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if prices:
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# Find pairs where diff = 5.00
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prices = sorted(set(prices))
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found_price = None
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for p in prices:
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if p > 20 and (p - 5.00) in [round(x, 2) for x in prices]:
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found_price = p
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break
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# If no pair found, take the most common price range
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if not found_price:
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valid_prices = [p for p in prices if 100 < p < 500]
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if valid_prices:
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found_price = max(valid_prices)
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if found_price and found_price > 20:
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discounted = round(found_price - 5.0, 2)
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results.append({
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'sku': sku_name,
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'price': discounted,
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'original_price': round(found_price, 2),
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'confidence': float(conf) if conf else 0
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})
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# Deduplicate - keep highest confidence for each SKU
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seen = {}
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for r in results:
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key = r['sku']
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if key not in seen or r['confidence'] > seen[key]['confidence']:
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seen[key] = r
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return sorted(seen.values(), key=lambda x: x['price'])
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def extract_prices_v3(text_lines):
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"""
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Most robust: look for every <SKU name> ... <price_a> ... 5.00 ... <price_b> pattern
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where price_a = price_b - 5 (or vice versa)
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"""
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results = []
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all_with_conf = list(text_lines)
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for i, (text, conf) in enumerate(all_with_conf):
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text = text.strip()
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# Must contain key SKU indicators
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has_indicator = (
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bool(re.search(r'(J1900|j1900|J3160|j3160|i3|I3|i5|I5|1037[Uu]|N5105|N5100|N5095|J3355|J3060|J4125|5005)', text)) or
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bool(re.search(r'(蜗牛|Nas|NAS|黑裙|黑群晖|客服|咨询)', text)) or
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bool(re.search(r'16G[Bb].*(?:windows|蜗牛|群晖|系统)', text)) or
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bool(re.search(r'(?:预装系统|系统).*(?:双盘位|单盘位)', text))
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)
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if not has_indicator or len(text) < 4:
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continue
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# But also filter out pure UI text
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skip_patterns = ['商品价格', '关注券', '折扣', '券后价', '商品ID', '活动', '修改', '查看', '营销', '官方', '拼多多', '商家']
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if any(s in text for s in skip_patterns):
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continue
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sku_name = text
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# Search window around this line for prices
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window_texts = []
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for j in range(max(0, i-3), min(i+15, len(all_with_conf))):
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window_texts.append(all_with_conf[j][0].strip())
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# Extract all decimal prices from the window
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prices = []
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for wt in window_texts:
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nums = re.findall(r'(\d{2,4}\.\d{2})', wt)
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prices.extend([float(n) for n in nums])
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if not prices:
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continue
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prices = sorted(set(prices))
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# Filter noise prices (too small)
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prices = [p for p in prices if p > 15]
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# Find the discount pattern: original_price and (original_price - 5)
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found_price = None
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price_set = set(round(p, 2) for p in prices)
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for p in prices:
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if round(p - 5.0, 2) in price_set:
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# p is the higher (original), p-5 is the discounted
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found_price = p
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break
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if round(p + 5.0, 2) in price_set:
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# p is the discounted, p+5 is the original
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found_price = p + 5.0
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break
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if not found_price:
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# Fallback: take the highest price that looks like a product price
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valid = [p for p in prices if 100 < p < 600]
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if valid:
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found_price = max(valid)
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if found_price and found_price > 20:
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results.append({
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'sku': sku_name,
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'price': round(found_price - 5.0, 2),
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'original_price': round(found_price, 2),
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'confidence': float(conf) if conf else 0
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})
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# Deduplicate by SKU name similarity
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def sku_key(s):
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return re.sub(r'[\s\-\.\+]+', '', s.lower())
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deduped = {}
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for r in results:
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key = sku_key(r['sku'])
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if key not in deduped or r['confidence'] > deduped[key]['confidence']:
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deduped[key] = r
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return sorted(deduped.values(), key=lambda x: x['price'])
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print("=" * 80)
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print("Extracting SKU Price Summary")
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print("=" * 80)
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all_entries = []
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for img_name, lines in all_results.items():
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print(f"\nProcessing: {img_name}")
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entries = extract_prices_v3(lines)
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print(f" Found {len(entries)} SKUs")
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all_entries.extend(entries)
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# Global deduplication
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def normalize_sku(name):
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"""Normalize SKU name for deduplication"""
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name = name.strip()
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# Remove leading symbols
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name = re.sub(r'^[>\s\-\.]+', '', name)
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# Standardize separators
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name = name.replace('+', '+').replace('＋', '+')
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# Remove trailing garbage
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name = re.sub(r'[>\s\.\-]+$', '', name)
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return name
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# Categorize entries
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# Dedup keeping highest confidence
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global_dedup = {}
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for e in all_entries:
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norm = normalize_sku(e['sku'])
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# Further normalize for key
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key = re.sub(r'[\s\-\>\.\(\)]', '', norm.lower())
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# Extract CPU type for grouping
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if key not in global_dedup or e['confidence'] > global_dedup[key]['confidence']:
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global_dedup[key] = e
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final_entries = sorted(global_dedup.values(), key=lambda x: x['price'])
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# Group by CPU/platform
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groups = {}
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for e in final_entries:
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name = e['sku']
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# Extract group
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if '16G' in name.upper() or '16g' in name.lower():
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group = '系统U盘 (16GB)'
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elif re.search(r'(i3|I3|i5|I5)', name):
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if '双盘' in name or '雙盤' in name:
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group = 'NAS 双盘位 - i3/i5系列'
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else:
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group = 'NAS - i3/i5系列'
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elif re.search(r'1037[Uu]', name):
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if '单盘' in name or '單盤' in name or 'A4' in name:
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group = 'NAS 单盘位 - 1037U系列'
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elif '双盘' in name or '雙盤' in name:
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group = 'NAS 双盘位 - 1037U系列'
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else:
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group = 'NAS - 1037U系列'
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elif re.search(r'J1900|j1900', name):
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group = 'NAS 双盘位 - J1900系列'
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elif re.search(r'i3|I3', name):
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group = 'NAS - i3系列'
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elif re.search(r'i5|I5', name):
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group = 'NAS - i5系列'
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elif '黑裙' in name or '客服' in name or '咨询' in name:
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group = 'NAS - 黑裙晖系列'
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else:
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group = '其他'
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if group not in groups:
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groups[group] = []
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groups[group].append(e)
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# Generate markdown output
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md_lines = []
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md_lines.append("# SKU 售价汇总")
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md_lines.append("")
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md_lines.append(f"> 从拼多多商家后台截图 OCR 识别提取")
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md_lines.append(f"> 提取日期: 2026-06-16")
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md_lines.append(f"> 数据来源: `售价/` 目录下 {len(all_results)} 张截图")
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md_lines.append(f"> 共识别 {len(final_entries)} 个 SKU 售价（已去重）")
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md_lines.append("")
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md_lines.append("---")
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md_lines.append("")
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for group_name in sorted(groups.keys()):
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entries = groups[group_name]
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md_lines.append(f"## {group_name}")
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md_lines.append("")
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md_lines.append("| SKU 名称 | 券后售价 (¥) | 券前原价 (¥) | OCR 置信度 |")
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md_lines.append("|---------|------------:|------------:|----------:|")
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for e in entries:
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name = normalize_sku(e['sku'])
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# Clean up OCR artifacts in name
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name = name.replace('關注券5元优惠券', '').strip()
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name = re.sub(r'\s+', ' ', name)
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md_lines.append(f"| {name} | {e['price']:.2f} | {e['original_price']:.2f} | {e['confidence']:.2f} |")
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md_lines.append("")
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md_lines.append("---")
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md_lines.append("")
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md_lines.append("## 说明")
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md_lines.append("")
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md_lines.append("- **券后售价** = 券前原价 - 5.00元 (店铺关注券)")
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md_lines.append("- 价格数据通过 RapidOCR 从拼多多商家后台截图自动识别提取")
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md_lines.append("- 同一SKU在不同截图中出现时保留置信度最高的识别结果")
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md_lines.append("- OCR 识别可能存在误差，请以实际后台数据为准")
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output_path = r"D:\workspace\membank\售价\SKU售价汇总.md"
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with open(output_path, "w", encoding="utf-8") as f:
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f.write("\n".join(md_lines))
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print(f"\n\nSummary saved to: {output_path}")
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print(f"Total: {len(final_entries)} SKUs, {len(groups)} categories")
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258
.trae/skills/ocr-extract/parse_v2.py
Normal file
258
.trae/skills/ocr-extract/parse_v2.py
Normal file
@@ -0,0 +1,258 @@
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import json
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import re
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import os
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# Load OCR results
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with open(r"D:\workspace\membank\售价\ocr_results.json", "r", encoding="utf-8") as f:
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all_results = json.load(f)
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def extract_sku_v4(text_lines):
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"""
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Robust extraction:
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Pattern in PDD backend: SKU_name ... [price1] ... [5.00] ... [price2]
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where price2 = price1 - 5
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"""
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results = []
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# Build full text with confidences
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items = [(t[0].strip(), float(t[1]) if t[1] else 0.0) for t in text_lines]
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i = 0
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while i < len(items):
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text, conf = items[i]
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# Check for price patterns - find lines with two prices and 5.00 in between
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# Look ahead for price patterns
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prices_in_window = []
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window_items = []
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for j in range(i, min(i + 8, len(items))):
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t = items[j][0]
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nums = re.findall(r'(\d{2,4}\.\d{2})', t)
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if nums:
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prices_in_window.extend([float(n) for n in nums])
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window_items.append((j, t, [float(n) for n in nums]))
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if not prices_in_window:
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i += 1
|
||||
continue
|
||||
|
||||
prices_set = set(round(p, 2) for p in prices_in_window)
|
||||
|
||||
# Try to find original price and discounted price (diff = 5)
|
||||
found_prices = []
|
||||
for p in sorted(prices_set):
|
||||
if round(p - 5, 2) in prices_set and p > 15:
|
||||
found_prices.append((round(p - 5, 2), p)) # (discounted, original)
|
||||
elif round(p + 5, 2) in prices_set and p > 15:
|
||||
found_prices.append((p, round(p + 5, 2)))
|
||||
|
||||
if not found_prices:
|
||||
i += 1
|
||||
continue
|
||||
|
||||
# Now find the SKU name - look backwards from the price
|
||||
# The SKU name is typically 1-3 lines before the price
|
||||
sku_name = None
|
||||
for j in range(i-1, max(0, i-5), -1):
|
||||
t = items[j][0]
|
||||
# Must contain CPU or platform identifiers
|
||||
if re.search(r'(J1900|j1900|J3160|j3160|i3|I3|i5|I5|1037[Uu]|5005[Uu]|N5105|16G[Bb]|预装系统|双盘位|单盘位|三盘位|四盘位|黑裙|黑群晖|飞牛|蜗牛|Nas|NAS|盘位)', t):
|
||||
# Not a pure UI element
|
||||
if not re.match(r'^(商品|营销|官方|拼多多|商家|查看|修改|活动|价格|关注|折扣|券后|ID)', t):
|
||||
sku_name = t
|
||||
break
|
||||
|
||||
if sku_name:
|
||||
for discounted, original in found_prices:
|
||||
results.append({
|
||||
'sku': sku_name,
|
||||
'price': discounted,
|
||||
'original_price': original,
|
||||
'confidence': conf
|
||||
})
|
||||
|
||||
i += 1
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def normalize_sku_key(name):
|
||||
"""Create a normalized key for deduplication by extracting core identifiers"""
|
||||
name = name.strip()
|
||||
# Extract CPU type
|
||||
cpu = ''
|
||||
cpu_match = re.search(r'(J1900|j1900|J3160|j3160|i3|I3|i5|I5|1037[Uu]|5005[Uu]|N5105|N5095|J3355)', name)
|
||||
if cpu_match:
|
||||
cpu = cpu_match.group(1).upper().replace('J1900','J1900').replace('J3160','J3160')
|
||||
|
||||
# Extract memory
|
||||
mem = ''
|
||||
mem_match = re.search(r'(\d+[Gg])[+]?(\d+[Gg])?', name)
|
||||
if mem_match:
|
||||
mem = mem_match.group(0).upper()
|
||||
|
||||
# Extract form factor
|
||||
form = ''
|
||||
if '单盘' in name or '单盤' in name:
|
||||
form = '单盘'
|
||||
elif '双盘' in name or '雙盤' in name:
|
||||
form = '双盘'
|
||||
elif '三盘' in name:
|
||||
form = '三盘'
|
||||
elif '四盘' in name:
|
||||
form = '四盘'
|
||||
|
||||
# System USB
|
||||
if '16G' in name.upper() or '16g' in name:
|
||||
if 'windows' in name.lower():
|
||||
return 'USB:16GB:windows'
|
||||
elif '飞牛' in name or '蜗牛' in name:
|
||||
return 'USB:16GB:飞牛'
|
||||
return f'USB:16GB:{name[:20]}'
|
||||
|
||||
return f"{cpu}:{mem}:{form}"
|
||||
|
||||
|
||||
def clean_sku_name(name):
|
||||
"""Clean up OCR artifacts in SKU name"""
|
||||
name = name.strip()
|
||||
# Remove known noise
|
||||
name = re.sub(r'关注券5元优惠券', '', name)
|
||||
name = re.sub(r'店铺关注券5元优惠券', '', name)
|
||||
name = re.sub(r'[>\s]+$', '', name)
|
||||
name = re.sub(r'^[>\s]+', '', name)
|
||||
name = re.sub(r'\s+', ' ', name)
|
||||
return name
|
||||
|
||||
|
||||
# Collect all unique SKUs
|
||||
all_entries = []
|
||||
for img_name, lines in all_results.items():
|
||||
entries = extract_sku_v4(lines)
|
||||
all_entries.extend(entries)
|
||||
|
||||
# Deduplicate - keep highest confidence per normalized key
|
||||
dedup = {}
|
||||
for e in all_entries:
|
||||
key = normalize_sku_key(e['sku'])
|
||||
if key and key not in dedup:
|
||||
dedup[key] = e
|
||||
elif key and e['confidence'] > dedup[key]['confidence']:
|
||||
dedup[key] = e
|
||||
|
||||
# Sort by price
|
||||
sorted_entries = sorted(dedup.values(), key=lambda x: x['price'])
|
||||
|
||||
# Print for debugging
|
||||
print("=" * 60)
|
||||
print("Extracted unique SKUs:")
|
||||
for i, e in enumerate(sorted_entries):
|
||||
key = normalize_sku_key(e['sku'])
|
||||
print(f" [{key}] {clean_sku_name(e['sku'])} -> ${e['price']} (orig: ${e['original_price']})")
|
||||
|
||||
# Build markdown
|
||||
md = []
|
||||
md.append("# SKU 售价汇总")
|
||||
md.append("")
|
||||
md.append("> 从拼多多商家后台「商品价格管理」截图 OCR 识别提取")
|
||||
md.append(f"> 提取日期: 2026-06-16")
|
||||
md.append(f"> 图片数量: {len(all_results)} 张")
|
||||
md.append(f"> 识别 SKU: {len(sorted_entries)} 个（已去重合并）")
|
||||
md.append("")
|
||||
md.append("---")
|
||||
md.append("")
|
||||
|
||||
# Group by platform
|
||||
groups = {
|
||||
'系统U盘 (16GB)': [],
|
||||
'单盘位 - 1037U': [],
|
||||
'双盘位 - 1037U': [],
|
||||
'单盘位 - J1900': [],
|
||||
'双盘位 - J1900': [],
|
||||
'单盘位 - J3160': [],
|
||||
'双盘位 - J3160': [],
|
||||
'单盘位 - i3': [],
|
||||
'双盘位 - i3': [],
|
||||
'双盘位 - i5': [],
|
||||
'三盘位/四盘位 - i3/i5': [],
|
||||
'5005U 系列': [],
|
||||
'其他NAS/黑裙晖': [],
|
||||
}
|
||||
|
||||
for e in sorted_entries:
|
||||
name = e['sku']
|
||||
key = normalize_sku_key(name)
|
||||
|
||||
if 'USB' in key:
|
||||
groups['系统U盘 (16GB)'].append(e)
|
||||
elif '1037U' in key:
|
||||
if '单盘' in key:
|
||||
groups['单盘位 - 1037U'].append(e)
|
||||
else:
|
||||
groups['双盘位 - 1037U'].append(e)
|
||||
elif 'J1900' in key:
|
||||
if '单盘' in key:
|
||||
groups['单盘位 - J1900'].append(e)
|
||||
else:
|
||||
groups['双盘位 - J1900'].append(e)
|
||||
elif 'J3160' in key:
|
||||
if '单盘' in key:
|
||||
groups['单盘位 - J3160'].append(e)
|
||||
else:
|
||||
groups['双盘位 - J3160'].append(e)
|
||||
elif 'I3' in key:
|
||||
if '三盘' in key or '四盘' in key:
|
||||
groups['三盘位/四盘位 - i3/i5'].append(e)
|
||||
elif '单盘' in key:
|
||||
groups['单盘位 - i3'].append(e)
|
||||
else:
|
||||
groups['双盘位 - i3'].append(e)
|
||||
elif 'I5' in key:
|
||||
if '三盘' in key or '四盘' in key:
|
||||
groups['三盘位/四盘位 - i3/i5'].append(e)
|
||||
else:
|
||||
groups['双盘位 - i5'].append(e)
|
||||
elif '5005U' in key:
|
||||
groups['5005U 系列'].append(e)
|
||||
else:
|
||||
groups['其他NAS/黑裙晖'].append(e)
|
||||
|
||||
for group_name in groups:
|
||||
entries = groups[group_name]
|
||||
if not entries:
|
||||
continue
|
||||
|
||||
md.append(f"## {group_name}")
|
||||
md.append("")
|
||||
md.append("| SKU 配置 | 券后售价 (¥) | 券前原价 (¥) | 优惠 |")
|
||||
md.append("|---------|------------:|------------:|-----|")
|
||||
|
||||
for e in entries:
|
||||
name = clean_sku_name(e['sku'])
|
||||
md.append(f"| {name} | {e['price']:.2f} | {e['original_price']:.2f} | -5.00 |")
|
||||
|
||||
md.append("")
|
||||
|
||||
md.append("---")
|
||||
md.append("")
|
||||
md.append("## 说明")
|
||||
md.append("")
|
||||
md.append("- **优惠**: 统一使用店铺关注券 -5.00 元")
|
||||
md.append("- **数据来源**: 拼多多商家后台「商品价格管理」页面截图")
|
||||
md.append("- **识别工具**: RapidOCR (rapidocr-onnxruntime)")
|
||||
md.append("- **注意事项**: OCR 识别的 SKU 名称可能有碎片化/错字，建议对比后台实际数据确认")
|
||||
md.append("- **价格日期**: 2026-06-05 (截图时间)")
|
||||
md.append("")
|
||||
|
||||
# Fix the price summary line
|
||||
overall_min = min(e['price'] for e in sorted_entries) if sorted_entries else 0
|
||||
overall_max = max(e['price'] for e in sorted_entries) if sorted_entries else 0
|
||||
md.append(f"> 价格范围: ¥{overall_min:.2f} ~ ¥{overall_max:.2f}")
|
||||
|
||||
output_path = r"D:\workspace\membank\售价\SKU售价汇总.md"
|
||||
with open(output_path, "w", encoding="utf-8") as f:
|
||||
f.write("\n".join(md))
|
||||
|
||||
print(f"\n\nSaved to: {output_path}")
|
||||
print(f"Total unique SKUs: {len(sorted_entries)}")
|
||||
54
.trae/skills/ocr-extract/run_ocr.py
Normal file
54
.trae/skills/ocr-extract/run_ocr.py
Normal file
@@ -0,0 +1,54 @@
|
||||
from rapidocr_onnxruntime import RapidOCR
|
||||
from PIL import Image
|
||||
import os, time, json
|
||||
|
||||
ocr = RapidOCR()
|
||||
img_dir = r"D:\workspace\membank\售价"
|
||||
images = sorted([f for f in os.listdir(img_dir) if f.lower().endswith(('.jpeg', '.jpg', '.png'))])
|
||||
|
||||
all_results = {}
|
||||
|
||||
for img_name in images:
|
||||
img_path = os.path.join(img_dir, img_name)
|
||||
img = Image.open(img_path)
|
||||
w, h = img.size
|
||||
print(f"\n=== {img_name} ({w}x{h}) ===")
|
||||
|
||||
# Use chunking for images taller than 2000px
|
||||
if h > 2000:
|
||||
chunk_size = 1200
|
||||
all_text = []
|
||||
for i in range(0, h, chunk_size):
|
||||
bottom = min(i + chunk_size, h)
|
||||
chunk = img.crop((0, i, w, bottom))
|
||||
tmp_path = os.path.join(os.environ['TEMP'], f'ocr_chunk_{i}.png')
|
||||
chunk.save(tmp_path)
|
||||
result, _ = ocr(tmp_path)
|
||||
if result:
|
||||
for line in result:
|
||||
text = line[1]
|
||||
conf = line[2]
|
||||
bbox = line[0]
|
||||
y_center = (bbox[0][1] + bbox[2][1]) / 2 + i # absolute y position
|
||||
all_text.append((y_center, text, conf))
|
||||
if os.path.exists(tmp_path):
|
||||
os.remove(tmp_path)
|
||||
# Sort by vertical position
|
||||
all_text.sort(key=lambda x: x[0])
|
||||
lines = [(t[1], t[2]) for t in all_text]
|
||||
else:
|
||||
result, _ = ocr(img_path)
|
||||
if result:
|
||||
lines = [(line[1], line[2]) for line in result]
|
||||
else:
|
||||
lines = []
|
||||
|
||||
all_results[img_name] = lines
|
||||
for text, conf in lines:
|
||||
print(f" [{conf}] {text}")
|
||||
|
||||
# Save raw results as JSON for later processing
|
||||
with open(os.path.join(img_dir, "ocr_results.json"), "w", encoding="utf-8") as f:
|
||||
json.dump(all_results, f, ensure_ascii=False, indent=2)
|
||||
|
||||
print("\n\n=== OCR完成，结果已保存到 ocr_results.json ===")
|
||||
Reference in New Issue
Block a user