feat 新增 process lookbooks 接口

fix

app/service/lookbooks/utils/image_utils.py

@@ -0,0 +1,152 @@
+from typing import Optional, Type, List, Dict, Any
+import hashlib
+import base64
+import io
+import os
+
+from PIL import Image
+import torch
+from torchvision import transforms
+
+
+def generate_text_id(text):
+    return hashlib.md5(text.encode('utf-8')).hexdigest()
+
+
+def get_file_hash(file_path):
+    with open(file_path, 'rb') as f:
+        return hashlib.md5(f.read()).hexdigest()
+
+
+def resize_image(img, max_size=512):
+    """调整图片大小，保持原始比例，使长宽均不超过max_size，并保存到output_path"""
+    width, height = img.size
+    if width > max_size or height > max_size:
+        # 保持图片的宽高比
+        if width > height:
+            new_width = max_size
+            new_height = int(max_size * height / width)
+        else:
+            new_height = max_size
+            new_width = int(max_size * width / height)
+
+        # 进行图片缩放
+        img_resized = img.resize((new_width, new_height), Image.LANCZOS)
+    else:
+        img_resized = img.copy()  # 如果图片本身符合条件，则不进行缩放
+
+    return img_resized
+
+
+def base64_encode_image(image_path):
+    try:
+        image_path = image_path.replace("\\", "/")
+
+        with Image.open(image_path) as img:
+            # 如果图像模式不是 RGB，转换为 RGB
+            if img.mode != 'RGB':
+                img = img.convert('RGB')
+            # 获取当前图片的宽和高
+            img_resized = resize_image(img)
+            # 将PIL Image对象编码为base64字符串。
+            buffered = io.BytesIO()
+            img_resized.save(buffered, format="JPEG")  # 保存调整后的图像到内存流
+            return base64.b64encode(buffered.getvalue()).decode("utf-8")
+    except FileNotFoundError as e:
+        print(f"File not found: {image_path}")
+        print(f"Absolute path: {os.path.abspath(image_path)}")
+        print(f"Current working directory: {os.getcwd()}")
+        return None
+    except Exception as e:
+        print(f"Error processing image {image_path}: {str(e)}")
+        return None
+
+
+def get_possible_image_paths(path):
+    image_path = path.split(".")
+    # Image path might contain file extension (e.g. .jpg),
+    # In this case, we want the path without the extension
+    image_path = image_path if len(image_path) == 1 else image_path[:-1]
+    for ext in (".jpg", ".jpeg", ".png", ".ppm", ".bmp", ".pgm", ".tif", ".tiff", ".webp"):
+        image_ext = ".".join(image_path) + ext
+        if os.path.isfile(image_ext):
+            path = image_ext
+            break
+    return path
+
+
+def preprocess(image_urls: List[str]):
+    transform_pipeline = transforms.Compose([
+        # 缩放图像尺寸到 256x256
+        transforms.Resize((256, 256)),
+        # 从图像中心裁剪 224x224
+        transforms.CenterCrop(224),
+        # 将 PIL 图像或 NumPy ndarray 转换为 FloatTensor
+        transforms.ToTensor(),
+        # 对图像进行标准化
+        transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
+                             std=[0.26862954, 0.26130258, 0.27577711])
+    ])
+    image_data = []
+    if len(image_urls) == 0:
+        return None
+    for url in image_urls:
+        path = get_possible_image_paths(url)
+        try:
+            image = Image.open(path).convert("RGB")
+        except FileNotFoundError as e:
+            print(f"File not found: {path}, {e}")
+            continue
+        image_tensor = transform_pipeline(image)
+        image_data.append(image_tensor)
+    image_data = torch.stack(image_data, dim=0)
+    return image_data.numpy()
+
+
+def is_image_accessible(path: str) -> bool:
+    if not os.path.exists(path):
+        return False
+    try:
+        with Image.open(path) as img:
+            img.verify()  # 验证图像文件
+        return True
+    except (IOError, SyntaxError):
+        return False
+
+def create_image_grid(image_paths, grid_size=(2, 2)):
+    # 打开所有图片
+    images = [Image.open(path) for path in image_paths]
+
+    # 确定每个图片的大小
+    max_width = max(img.width for img in images)
+    max_height = max(img.height for img in images)
+
+    # 创建一个新的空白图片
+    grid_width = grid_size[0] * max_width
+    grid_height = grid_size[1] * max_height
+    grid_image = Image.new('RGB', (grid_width, grid_height), color="white")
+
+    # 将图片粘贴到网格中
+    for i, img in enumerate(images):
+        x = (i % grid_size[0]) * max_width
+        y = (i // grid_size[0]) * max_height
+
+        # 计算缩放比例，保持原始宽高比
+        ratio = min(max_width / img.width, max_height / img.height)
+        new_size = (int(img.width * ratio), int(img.height * ratio))
+
+        # 调整大小并保持宽高比
+        img_resized = img.resize(new_size, Image.LANCZOS)
+
+        # 计算居中位置
+        paste_x = x + (max_width - new_size[0]) // 2
+        paste_y = y + (max_height - new_size[1]) // 2
+
+        # 粘贴到网格中
+        grid_image.paste(img_resized, (paste_x, paste_y))
+
+    # 将图片转换为字节流
+    img_byte_arr = io.BytesIO()
+    grid_image.save(img_byte_arr, format='PNG')
+    img_byte_arr = img_byte_arr.getvalue()
+    return img_byte_arr

app/service/lookbooks/utils/openai_utils.py

@@ -0,0 +1,31 @@
+import time
+
+
+def wait_for_job_completion(client, batch_job_id, timeout=6000, interval=10):
+    """
+    等待批处理作业完成。
+
+    参数:
+    - client: 批处理作业客户端对象
+    - batch_job_id: 批处理作业的ID
+    - timeout: 最大等待时间（秒）
+    - interval: 检查状态的间隔时间（秒）
+
+    返回:
+    - True 如果作业完成，False 如果超时
+    """
+    start_time = time.time()
+    while time.time() - start_time < timeout:
+        batch_job = client.batches.retrieve(batch_job_id)
+        if batch_job.status == 'completed':
+            print("Batch job completed successfully.")
+            return True
+        elif batch_job.status == 'failed':
+            print("Batch job failed.")
+            return False
+        else:
+            print(f"Current status: {batch_job.status}. Waiting for completion...")
+            time.sleep(interval)
+
+    print("Timeout reached, job did not complete in time.")
+    return False