同步的检查

ccs_base/views/cc_clearance_file_view.xml

@@ -14,6 +14,7 @@
                 <field name="file" string="PDF File" required="1" filename="attachment_name"/>
                 <field name="is_upload" readonly="1"/>
                 <field name="upload_time"/>
+                <field name="create_date" optional="show"/> 
                 <button name="action_sync" string="SyncTo.." type="object" icon="fa-upload"/>
                 <field name="attachment_name" invisible="1"/>
             </tree>

ccs_base/wizard/image-to-coordinate(1).py

+import os
+from openai import OpenAI
+import requests
+import mimetypes
+import base64
+import fitz  # PyMuPDF
+import json
+from PIL import Image, ImageDraw
+
+client = OpenAI(
+    # 若没有配置环境变量，请用阿里云百炼API Key将下行替换为：api_key="sk-xxx",
+    # 新加坡和北京地域的API Key不同。获取API Key：https://help.aliyun.com/zh/model-studio/get-api-key
+    api_key='sk-e41914f0d9c94035a5ae1322e9a61fb1',
+    # 以下是北京地域base_url，如果使用新加坡地域的模型，需要将base_url替换为：https://dashscope-intl.aliyuncs.com/compatible-mode/v1
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+)
+
+pdf_path = "C:/Users/Administrator/Desktop/43610236590 (3).pdf"
+
+def pdf_to_images(pdf_path, output_dir='./pdf_pages', dpi=150):
+    """
+    将PDF文件逐页转为PNG图片
+    :param pdf_path: PDF文件路径
+    :param output_dir: 输出目录，默认当前目录下pdf_pages文件夹
+    :param dpi: 渲染分辨率，默认150
+    :return: 生成的图片路径列表
+    """
+    os.makedirs(output_dir, exist_ok=True)
+    doc = fitz.open(pdf_path)
+    image_paths = []
+    for page_num in range(len(doc)):
+        page = doc.load_page(page_num)
+        mat = fitz.Matrix(dpi / 72, dpi / 72)
+        pix = page.get_pixmap(matrix=mat)
+        output_path = os.path.join(output_dir, f"page_{page_num + 1}.png")
+        pix.save(output_path)
+        image_paths.append(output_path)
+    doc.close()
+    return image_paths
+
+
+def download_image(image_url, save_path='output.png'):
+    try:
+        response = requests.get(image_url, stream=True, timeout=300)  # 设置超时
+        response.raise_for_status()  # 如果HTTP状态码不是200，则引发异常
+        with open(save_path, 'wb') as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+        print(f"图像已成功下载到: {save_path}")
+
+    except requests.exceptions.RequestException as e:
+        print(f"图像下载失败: {e}")
+    
+def encode_file(file_path):
+    mime_type, _ = mimetypes.guess_type(file_path)
+    if not mime_type or not mime_type.startswith("image/"):
+        raise ValueError("不支持或无法识别的图像格式")
+    with open(file_path, "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
+    return f"data:{mime_type};base64,{encoded_string}"
+
+image_paths = pdf_to_images(pdf_path)
+image_base64 = encode_file(image_paths[0])
+# 获取图片分辨率，并在提示中要求模型按比例（相对宽高的0-1浮点数）返回坐标
+img_w, img_h = Image.open(image_paths[0]).size
+print(f"页面尺寸: {img_w}x{img_h} 像素")
+
+def safe_extract_json(text: str):
+    """从模型返回文本中尽可能鲁棒地提取JSON对象。"""
+    # 直接尝试解析
+    try:
+        return json.loads(text)
+    except Exception:
+        pass
+
+    # 尝试提取首尾花括号之间的内容
+    start = text.find('{')
+    end = text.rfind('}')
+    if start != -1 and end != -1 and end > start:
+        candidate = text[start:end+1]
+        try:
+            return json.loads(candidate)
+        except Exception:
+            # 将单引号替换为双引号再试一次（有些模型会返回单引号JSON样式）
+            candidate2 = candidate.replace("'", '"')
+            try:
+                return json.loads(candidate2)
+            except Exception:
+                return None
+    return None
+
+def normalize_bbox(bbox, img_w, img_h):
+    """
+    规范化bbox为像素坐标 [x1,y1,x2,y2]，并保证边界有效。
+    支持：
+    - 像素坐标；
+    - 归一化坐标（0-1）；
+    - [x1,y1,w,h] 形式（若x2<=x1或y2<=y1则按宽高处理）。
+    """
+    if not isinstance(bbox, (list, tuple)) or len(bbox) != 4:
+        return None
+    x1, y1, x2, y2 = bbox
+
+    # 如果可能是归一化坐标
+    if 0 <= x1 <= 1 and 0 <= y1 <= 1 and 0 <= x2 <= 1 and 0 <= y2 <= 1:
+        x1 = int(round(x1 * img_w))
+        y1 = int(round(y1 * img_h))
+        x2 = int(round(x2 * img_w))
+        y2 = int(round(y2 * img_h))
+    else:
+        # 像素坐标或 [x1,y1,w,h]
+        x1 = int(round(x1))
+        y1 = int(round(y1))
+        x2 = int(round(x2))
+        y2 = int(round(y2))
+
+        # 如果x2<=x1或y2<=y1，尝试将其视作宽高
+        if x2 <= x1 or y2 <= y1:
+            x2 = x1 + max(1, x2)
+            y2 = y1 + max(1, y2)
+
+    # 修正边界并加上少量边距
+    if x1 > x2:
+        x1, x2 = x2, x1
+    if y1 > y2:
+        y1, y2 = y2, y1
+
+    # 边距按短边的2%（最少5px，最多30px）
+    short_side = min(img_w, img_h)
+    margin = max(5, min(30, int(0.02 * short_side)))
+    x1 = max(0, x1 - margin)
+    y1 = max(0, y1 - margin)
+    x2 = min(img_w - 1, x2 + margin)
+    y2 = min(img_h - 1, y2 + margin)
+    return [x1, y1, x2, y2]
+
+def erase_regions_on_image(image_path: str, coords_map: dict, save_path: str):
+    img = Image.open(image_path).convert('RGB')
+    draw = ImageDraw.Draw(img)
+    w, h = img.size
+
+    for key, bbox in coords_map.items():
+        nb = normalize_bbox(bbox, w, h)
+        if nb is None:
+            print(f"坐标格式错误，跳过 {key}: {bbox}")
+            continue
+        x1, y1, x2, y2 = nb
+        draw.rectangle([x1, y1, x2, y2], fill=(255, 255, 255))
+        print(f"已抹除 {key} 区域: {nb}")
+
+    os.makedirs(os.path.dirname(save_path), exist_ok=True)
+    img.save(save_path)
+    print(f"清理后的图片已保存: {save_path}")
+
+def draw_debug_boxes(image_path: str, coords_map: dict, save_path: str):
+    """在原图上绘制预测的矩形框用于人工核对。"""
+    img = Image.open(image_path).convert('RGB')
+    draw = ImageDraw.Draw(img)
+    w, h = img.size
+    for key, bbox in coords_map.items():
+        nb = normalize_bbox(bbox, w, h)
+        if nb is None:
+            print(f"跳过无法解析的坐标: {key} -> {bbox}")
+            continue
+        x1, y1, x2, y2 = nb
+        draw.rectangle([x1, y1, x2, y2], outline=(255, 0, 0), width=3)
+        draw.text((x1, max(0, y1 - 16)), key, fill=(255, 0, 0))
+    os.makedirs(os.path.dirname(save_path), exist_ok=True)
+    img.save(save_path)
+    print(f"调试框已生成: {save_path}")
+
+def images_to_pdf(image_paths, output_pdf):
+    os.makedirs(os.path.dirname(output_pdf), exist_ok=True)
+    pil_images = [Image.open(p).convert('RGB') for p in image_paths]
+    if not pil_images:
+        raise RuntimeError("没有需要写入PDF的图片")
+    first = pil_images[0]
+    rest = pil_images[1:]
+    first.save(output_pdf, save_all=True, append_images=rest)
+    print(f"已生成PDF: {output_pdf}")
+
+completion = client.chat.completions.create(
+    model="qwen3-vl-plus", # 此处以qwen3-vl-plus为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/models
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": image_base64
+                    },
+                },
+                {"type": "text", "text": f"图像分辨率为{img_w}x{img_h}像素。坐标系定义：以原始图像左上角为原点(0,0)，x向右增加，y向下增加；不要使用任何预处理(缩放或加黑边)产生的坐标。请仅返回这两个文本的矩形框坐标，且必须是归一化到[0,1]的浮点数(相对于原始图像宽高)，返回格式严格为压缩JSON、无任何解释：{{\"AGN\": [x1_rel, y1_rel, x2_rel, y2_rel], \"UCLINK LOGISITICS LTD\": [x3_rel, y3_rel, x4_rel, y4_rel]}}。"},
+            ],
+        },
+    ],
+    temperature=0.1,
+)
+
+raw_text = completion.choices[0].message.content
+print(raw_text)
+result = safe_extract_json(raw_text)
+if result is None or not isinstance(result, dict):
+    raise RuntimeError("模型返回内容无法解析为JSON坐标，请检查返回格式。")
+
+# 只处理第一页：将抹除后的图片写入 output/cleaned_page_1.png，然后重新生成PDF
+cleaned_dir = os.path.join("./output")
+cleaned_first = os.path.join(cleaned_dir, "cleaned_page_1.png")
+debug_first = os.path.join(cleaned_dir, "debug_page_1.png")
+draw_debug_boxes(image_paths[0], result, debug_first)
+erase_regions_on_image(image_paths[0], result, cleaned_first)
+
+# 合成PDF：第一页使用清理后的图片，其余页沿用原图
+final_images = [cleaned_first] + image_paths[1:]
+images_to_pdf(final_images, os.path.join(cleaned_dir, "cleaned.pdf"))
+

ccs_base/wizard/image-to-coordinate.py

+import os
+from openai import OpenAI
+import requests
+import mimetypes
+import base64
+import fitz  # PyMuPDF
+import json
+from PIL import Image, ImageDraw
+
+client = OpenAI(
+    # 若没有配置环境变量，请用阿里云百炼API Key将下行替换为：api_key="sk-xxx",
+    # 新加坡和北京地域的API Key不同。获取API Key：https://help.aliyun.com/zh/model-studio/get-api-key
+    api_key='sk-e41914f0d9c94035a5ae1322e9a61fb1',
+    # 以下是北京地域base_url，如果使用新加坡地域的模型，需要将base_url替换为：https://dashscope-intl.aliyuncs.com/compatible-mode/v1
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+)
+
+pdf_path = "C:/Users/Administrator/Desktop/43610281036.pdf"
+
+def pdf_to_images(pdf_path, output_dir='./pdf_pages', dpi=150):
+    """
+    将PDF文件逐页转为PNG图片
+    :param pdf_path: PDF文件路径
+    :param output_dir: 输出目录，默认当前目录下pdf_pages文件夹
+    :param dpi: 渲染分辨率，默认150
+    :return: 生成的图片路径列表
+    """
+    os.makedirs(output_dir, exist_ok=True)
+    doc = fitz.open(pdf_path)
+    image_paths = []
+    for page_num in range(len(doc)):
+        page = doc.load_page(page_num)
+        mat = fitz.Matrix(dpi / 72, dpi / 72)
+        pix = page.get_pixmap(matrix=mat)
+        output_path = os.path.join(output_dir, f"page_{page_num + 1}.png")
+        pix.save(output_path)
+        image_paths.append(output_path)
+    doc.close()
+    return image_paths
+
+
+def download_image(image_url, save_path='output.png'):
+    try:
+        response = requests.get(image_url, stream=True, timeout=300)  # 设置超时
+        response.raise_for_status()  # 如果HTTP状态码不是200，则引发异常
+        with open(save_path, 'wb') as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+        print(f"图像已成功下载到: {save_path}")
+
+    except requests.exceptions.RequestException as e:
+        print(f"图像下载失败: {e}")
+    
+def encode_file(file_path):
+    mime_type, _ = mimetypes.guess_type(file_path)
+    if not mime_type or not mime_type.startswith("image/"):
+        raise ValueError("不支持或无法识别的图像格式")
+    with open(file_path, "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
+    return f"data:{mime_type};base64,{encoded_string}"
+
+image_paths = pdf_to_images(pdf_path)
+image_base64 = encode_file(image_paths[0])
+# 获取图片分辨率，并在提示中要求模型按比例（相对宽高的0-1浮点数）返回坐标
+img_w, img_h = Image.open(image_paths[0]).size
+print(f"页面尺寸: {img_w}x{img_h} 像素")
+
+def safe_extract_json(text: str):
+    """从模型返回文本中尽可能鲁棒地提取JSON对象。"""
+    # 直接尝试解析
+    try:
+        return json.loads(text)
+    except Exception:
+        pass
+
+    # 尝试提取首尾花括号之间的内容
+    start = text.find('{')
+    end = text.rfind('}')
+    if start != -1 and end != -1 and end > start:
+        candidate = text[start:end+1]
+        try:
+            return json.loads(candidate)
+        except Exception:
+            # 将单引号替换为双引号再试一次（有些模型会返回单引号JSON样式）
+            candidate2 = candidate.replace("'", '"')
+            try:
+                return json.loads(candidate2)
+            except Exception:
+                return None
+    return None
+
+def normalize_bbox(bbox, img_w, img_h):
+    """
+    规范化bbox为像素坐标 [x1,y1,x2,y2]，并保证边界有效。
+    支持：
+    - 像素坐标；
+    - 归一化坐标（0-1）；
+    - [x1,y1,w,h] 形式（若x2<=x1或y2<=y1则按宽高处理）。
+    """
+    if not isinstance(bbox, (list, tuple)) or len(bbox) != 4:
+        return None
+    x1, y1, x2, y2 = bbox
+
+    # 如果可能是归一化坐标
+    if 0 <= x1 <= 1 and 0 <= y1 <= 1 and 0 <= x2 <= 1 and 0 <= y2 <= 1:
+        x1 = int(round(x1 * img_w))
+        y1 = int(round(y1 * img_h))
+        x2 = int(round(x2 * img_w))
+        y2 = int(round(y2 * img_h))
+    else:
+        # 像素坐标或 [x1,y1,w,h]
+        x1 = int(round(x1))
+        y1 = int(round(y1))
+        x2 = int(round(x2))
+        y2 = int(round(y2))
+
+        # 如果x2<=x1或y2<=y1，尝试将其视作宽高
+        if x2 <= x1 or y2 <= y1:
+            x2 = x1 + max(1, x2)
+            y2 = y1 + max(1, y2)
+
+    # 修正边界并加上少量边距
+    if x1 > x2:
+        x1, x2 = x2, x1
+    if y1 > y2:
+        y1, y2 = y2, y1
+
+    # 边距按短边的2%（最少5px，最多30px）
+    short_side = min(img_w, img_h)
+    margin = max(5, min(30, int(0.02 * short_side)))
+    x1 = max(0, x1 - margin)
+    y1 = max(0, y1 - margin)
+    x2 = min(img_w - 1, x2 + margin)
+    y2 = min(img_h - 1, y2 + margin)
+    return [x1, y1, x2, y2]
+
+def erase_regions_on_image(image_path: str, coords_map: dict, save_path: str):
+    img = Image.open(image_path).convert('RGB')
+    draw = ImageDraw.Draw(img)
+    w, h = img.size
+
+    for key, bbox in coords_map.items():
+        nb = normalize_bbox(bbox, w, h)
+        if nb is None:
+            print(f"坐标格式错误，跳过 {key}: {bbox}")
+            continue
+        x1, y1, x2, y2 = nb
+        draw.rectangle([x1, y1, x2, y2], fill=(255, 255, 255))
+        print(f"已抹除 {key} 区域: {nb}")
+
+    os.makedirs(os.path.dirname(save_path), exist_ok=True)
+    img.save(save_path)
+    print(f"清理后的图片已保存: {save_path}")
+
+def draw_debug_boxes(image_path: str, coords_map: dict, save_path: str):
+    """在原图上绘制预测的矩形框用于人工核对。"""
+    img = Image.open(image_path).convert('RGB')
+    draw = ImageDraw.Draw(img)
+    w, h = img.size
+    for key, bbox in coords_map.items():
+        nb = normalize_bbox(bbox, w, h)
+        if nb is None:
+            print(f"跳过无法解析的坐标: {key} -> {bbox}")
+            continue
+        x1, y1, x2, y2 = nb
+        draw.rectangle([x1, y1, x2, y2], outline=(255, 0, 0), width=3)
+        draw.text((x1, max(0, y1 - 16)), key, fill=(255, 0, 0))
+    os.makedirs(os.path.dirname(save_path), exist_ok=True)
+    img.save(save_path)
+    print(f"调试框已生成: {save_path}")
+
+def images_to_pdf(image_paths, output_pdf):
+    os.makedirs(os.path.dirname(output_pdf), exist_ok=True)
+    pil_images = [Image.open(p).convert('RGB') for p in image_paths]
+    if not pil_images:
+        raise RuntimeError("没有需要写入PDF的图片")
+    first = pil_images[0]
+    rest = pil_images[1:]
+    first.save(output_pdf, save_all=True, append_images=rest)
+    print(f"已生成PDF: {output_pdf}")
+
+completion = client.chat.completions.create(
+    model="qwen3-vl-plus", # 此处以qwen3-vl-plus为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/models
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": image_base64
+                    },
+                },
+                {"type": "text", "text": f"图像分辨率为{img_w}x{img_h}像素。坐标系定义：以原始图像左上角为原点(0,0)，x向右增加，y向下增加；不要使用任何预处理(缩放或加黑边)产生的坐标。请仅返回这两个文本的矩形框坐标，且必须是归一化到[0,1]的浮点数(相对于原始图像宽高)，返回格式严格为压缩JSON、无任何解释：{{\"AGN\": [x1_rel, y1_rel, x2_rel, y2_rel], \"UCLINK LOGISITICS LTD\": [x3_rel, y3_rel, x4_rel, y4_rel]}}。"},
+            ],
+        },
+    ],
+)
+
+raw_text = completion.choices[0].message.content
+print(raw_text)
+result = safe_extract_json(raw_text)
+if result is None or not isinstance(result, dict):
+    raise RuntimeError("模型返回内容无法解析为JSON坐标，请检查返回格式。")
+
+# 只处理第一页：将抹除后的图片写入 output/cleaned_page_1.png，然后重新生成PDF
+cleaned_dir = os.path.join("./output")
+cleaned_first = os.path.join(cleaned_dir, "cleaned_page_1.png")
+debug_first = os.path.join(cleaned_dir, "debug_page_1.png")
+draw_debug_boxes(image_paths[0], result, debug_first)
+erase_regions_on_image(image_paths[0], result, cleaned_first)
+
+# 合成PDF：第一页使用清理后的图片，其余页沿用原图
+final_images = [cleaned_first] + image_paths[1:]
+images_to_pdf(final_images, os.path.join(cleaned_dir, "cleaned.pdf"))
+

ccs_connect_tiktok/models/cc_bill_loading.py

@@ -351,6 +351,7 @@ class CcBl(models.Model):
     bl_sync_log_ids = fields.One2many('cc.bl.sync.log', 'bl_id', string='Bill Of Loading Sync Logs')
     # 增加提单状态操作时间：取最新一条提单节点同步信息的操作时间
     process_time = fields.Datetime(string='Process Time', compute='_compute_process_time', store=True)
+    push_remark = fields.Text('Push Remark')
 
     def change_state_by_ship_package(self):
         """

ccs_connect_tiktok/wizard/__init__.py

@@ -3,4 +3,5 @@
 
 from . import batch_input_ship_package_statu_wizard
 from . import update_bl_status_wizard
+from . import batch_get_pod_info_wizard