feat（新功能）: batch generate relight 入参回参修改

fix（修复bug）:
docs（文档变更）:
refactor（重构）:
test(增加测试):

app/service/generate_batch_image/service_batch_generate_relight_image.py

@@ -18,7 +18,7 @@ from celery import Celery
 from tritonclient.utils import np_to_triton_dtype
 
 from app.core.config import *
-from app.schemas.generate_image import BatchGenerateRelightImageModel
+from app.schemas.generate_image import BatchGenerateRelightImageModel, RelightItemModel
 from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
 from app.service.utils.oss_client import oss_get_image
 
@@ -34,55 +34,58 @@ category = "relight_image"
 
 @celery_app.task
 def batch_generate_relight(batch_request_data):
+    batch_size = len(batch_request_data['batch_data_list'])
     logger.info(f"batch_generate_relight batch_request_data: {json.dumps(batch_request_data, indent=4)}")
+    batch_tasks_id = batch_request_data['batch_tasks_id']
+    user_id = batch_request_data['user_id']
+    result_data_list = []
     negative_prompt = 'lowres, bad anatomy, bad hands, cropped, worst quality'
-    direction = batch_request_data['direction']
     seed = "1"
-    prompt = batch_request_data['prompt']
-    product_type = batch_request_data['product_type']
-    image_url = batch_request_data['image_url']
-    image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url.split('/', 1)[1], data_type="cv2")
-    tasks_id = batch_request_data['tasks_id']
-    user_id = tasks_id.rsplit('-', 1)[1]
-    batch_size = batch_request_data['batch_size']
 
-    prompts = [prompt] * 1
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    image = cv2.resize(image, (512, 768))
-    images = [image.astype(np.uint8)] * 1
-    seeds = [seed] * 1
-    nagetive_prompts = [negative_prompt] * 1
-    directions = [direction] * 1
+    for i, data in enumerate(batch_request_data['batch_data_list']):
+        direction = data['direction']
 
-    if product_type == 'single':
-        text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
-        image_obj = np.array(images, dtype=np.uint8).reshape((-1, 768, 512, 3))
-        na_text_obj = np.array(nagetive_prompts, dtype="object").reshape((-1, 1))
-        seed_obj = np.array(seeds, dtype="object").reshape((-1, 1))
-        direction_obj = np.array(directions, dtype="object").reshape((-1, 1))
-    else:
-        text_obj = np.array(prompts, dtype="object").reshape((1))
-        image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
-        na_text_obj = np.array(nagetive_prompts, dtype="object").reshape((1))
-        seed_obj = np.array(seeds, dtype="object").reshape((1))
-        direction_obj = np.array(directions, dtype="object").reshape((1))
-    input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
-    input_image = grpcclient.InferInput("input_image", image_obj.shape, "UINT8")
-    input_natext = grpcclient.InferInput("negative_prompt", na_text_obj.shape, np_to_triton_dtype(na_text_obj.dtype))
-    input_seed = grpcclient.InferInput("seed", seed_obj.shape, np_to_triton_dtype(seed_obj.dtype))
-    input_direction = grpcclient.InferInput("direction", direction_obj.shape, np_to_triton_dtype(direction_obj.dtype))
+        prompt = data['prompt']
+        product_type = data['product_type']
+        image_url = data['image_url']
+        image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url.split('/', 1)[1], data_type="cv2")
+        tasks_id = data['tasks_id']
 
-    input_text.set_data_from_numpy(text_obj)
-    input_image.set_data_from_numpy(image_obj)
-    input_natext.set_data_from_numpy(na_text_obj)
-    input_seed.set_data_from_numpy(seed_obj)
-    input_direction.set_data_from_numpy(direction_obj)
+        prompts = [prompt] * 1
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        image = cv2.resize(image, (512, 768))
+        images = [image.astype(np.uint8)] * 1
+        seeds = [seed] * 1
+        nagetive_prompts = [negative_prompt] * 1
+        directions = [direction] * 1
 
-    inputs = [input_text, input_natext, input_image, input_seed, input_direction]
-    image_url_list = []
-    for i in range(batch_size):
+        if product_type == 'single':
+            text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
+            image_obj = np.array(images, dtype=np.uint8).reshape((-1, 768, 512, 3))
+            na_text_obj = np.array(nagetive_prompts, dtype="object").reshape((-1, 1))
+            seed_obj = np.array(seeds, dtype="object").reshape((-1, 1))
+            direction_obj = np.array(directions, dtype="object").reshape((-1, 1))
+        else:
+            text_obj = np.array(prompts, dtype="object").reshape((1))
+            image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
+            na_text_obj = np.array(nagetive_prompts, dtype="object").reshape((1))
+            seed_obj = np.array(seeds, dtype="object").reshape((1))
+            direction_obj = np.array(directions, dtype="object").reshape((1))
+        input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
+        input_image = grpcclient.InferInput("input_image", image_obj.shape, "UINT8")
+        input_natext = grpcclient.InferInput("negative_prompt", na_text_obj.shape, np_to_triton_dtype(na_text_obj.dtype))
+        input_seed = grpcclient.InferInput("seed", seed_obj.shape, np_to_triton_dtype(seed_obj.dtype))
+        input_direction = grpcclient.InferInput("direction", direction_obj.shape, np_to_triton_dtype(direction_obj.dtype))
+
+        input_text.set_data_from_numpy(text_obj)
+        input_image.set_data_from_numpy(image_obj)
+        input_natext.set_data_from_numpy(na_text_obj)
+        input_seed.set_data_from_numpy(seed_obj)
+        input_direction.set_data_from_numpy(direction_obj)
+
+        inputs = [input_text, input_natext, input_image, input_seed, input_direction]
         try:
-            if batch_request_data['product_type'] == "single":
+            if data['product_type'] == "single":
                 result = grpc_client.infer(model_name=GRI_MODEL_NAME_SINGLE, inputs=inputs, priority=100)
                 image = result.as_numpy("generated_relight_image")
             else:
@@ -121,18 +124,29 @@ def batch_generate_relight(batch_request_data):
                 logger.error(e)
         if isinstance(image_result, Image.Image):
             image_url = upload_SDXL_image(image_result, user_id=user_id, category=f"{category}", file_name=f"{tasks_id}-batch-{i}.png")
-            image_url_list.append(image_url)
+            data['relight_img'] = image_url
+
+            result_data_list.append(data)
         else:
             image_url = image_result
-        if DEBUG is False:
-            if i + 1 < batch_size:
-                publish_status(tasks_id, f"{i + 1}/{batch_size}", image_url)
-                logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | image_url：{image_url}")
-                # print(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | image_url：{image_url}")
-            else:
-                publish_status(tasks_id, f"OK", image_url_list)
-                logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：OK | image_url：{image_url}")
-                # print(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：OK | image_url：{image_url}")
+            data['relight_img'] = image_url
+            result_data_list.append(data)
+
+        # 发送每条结果
+        if DEBUG:
+            logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}")
+            print(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}")
+        else:
+            publish_status(tasks_id, f"{i + 1}/{batch_size}", image_url)
+            logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | image_url：{image_url}")
+    # 任务完成，发送所有数据结果
+    if DEBUG:
+        print(result_data_list)
+        logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}")
+        print(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}")
+    else:
+        publish_status(batch_tasks_id, f"OK", result_data_list)
+        logger.info(f" [x]Queue : {BATCH_GRI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}")
 
 
 def publish_status(task_id, progress, result):
@@ -151,12 +165,24 @@ def publish_status(task_id, progress, result):
 
 if __name__ == '__main__':
     rd = BatchGenerateRelightImageModel(
-        tasks_id="123-89",
-        # prompt="beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
-        prompt="Colorful black",
-        image_url='aida-users/89/clothing_seg/283c5c82-1a92-11f0-b72a-0242ac150002.png',
-        direction="Right Light",
-        product_type="overall",
-        batch_size=10
+        batch_tasks_id="abcd",
+        user_id="89",
+        batch_data_list=[
+            RelightItemModel(
+                tasks_id="123-5464",
+                product_type="overall",
+                image_url="aida-users/89/product_image/02894523-19b5-46eb-a9c6-2f512f5fec84-0-89.png",
+                prompt="Colorful black",
+                direction="Right Light",
+            ),
+            RelightItemModel(
+                tasks_id="123-5464123",
+                product_type="overall",
+                image_url="aida-users/89/product_image/02894523-19b5-46eb-a9c6-2f512f5fec84-0-89.png",
+                direction="Right Light",
+                prompt="Colorful black",
+            )
+        ]
     )
+
     batch_generate_relight(rd.dict())