feat（新功能）: pose transform 部署

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

app/core/config.py

@@ -148,6 +148,7 @@ GRI_MODEL_URL = '10.1.1.240:10051'
 # Pose Transform service config
 
 PS_RABBITMQ_QUEUES = os.getenv("PS_RABBITMQ_QUEUES", f"PoseTransform{RABBITMQ_ENV}")
+PT_MODEL_URL = '10.1.1.243:10061'
 
 # SEG service config
 SEGMENTATION = {

app/service/generate_image/service_pose_transform.py

@@ -9,16 +9,19 @@
 """
 import json
 import logging
+import time
+from io import BytesIO
 
-import cv2
+import imageio
 import numpy as np
 import redis
 import tritonclient.grpc as grpcclient
 from PIL import Image
+from tritonclient.utils import np_to_triton_dtype
 
 from app.core.config import *
 from app.schemas.pose_transform import PoseTransformModel
-from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
+from app.service.generate_image.utils.pose_transform_upload import upload_gif, upload_video, upload_first_image
 from app.service.utils.oss_client import oss_get_image
 
 logger = logging.getLogger()
@@ -29,33 +32,48 @@ class PoseTransformService:
         if DEBUG is False:
             self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
             self.channel = self.connection.channel()
-        self.grpc_client = grpcclient.InferenceServerClient(url=GRI_MODEL_URL)
+        self.grpc_client = grpcclient.InferenceServerClient(url=PT_MODEL_URL)
         self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
         self.category = "pose_transform"
-        self.batch_size = 1
-        self.seed = "1"
         self.image_url = request_data.image_url
-        self.image = oss_get_image(bucket=self.image_url.split('/')[0], object_name=self.image_url[self.image_url.find('/') + 1:], data_type="cv2")
+        self.pose_num = request_data.pose_id
+        self.image = pre_processing_image(request_data.image_url)
         self.tasks_id = request_data.tasks_id
         self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
-        self.gen_product_data = {'tasks_id': self.tasks_id, 'status': 'SUCCESS', 'message': "success", 'gif_url': 'test/mannequin_name.png', 'video_url': 'test/mannequin_name.png', 'image_url': 'test/mannequin_name.png'}
+        self.pose_transform_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'gif_url': '', 'video_url': '', 'image_url': ''}
 
-        self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
+        self.redis_client.set(self.tasks_id, json.dumps(self.pose_transform_data))
         self.redis_client.expire(self.tasks_id, 600)
 
     def callback(self, result, error):
         if error:
-            self.gen_product_data['status'] = "FAILURE"
+            self.pose_transform_data['status'] = "FAILURE"
-            self.gen_product_data['message'] = str(error)
+            self.pose_transform_data['message'] = str(error)
-            self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
+            self.redis_client.set(self.tasks_id, json.dumps(self.pose_transform_data))
         else:
-            image = result.as_numpy("generated_inpaint_image")
+            result_data = np.squeeze(result.as_numpy("generated_image_list").astype(np.uint8))[:, :, :, ::-1]
-            image_result = Image.fromarray(np.squeeze(image.astype(np.uint8)))
+
-            image_url = upload_SDXL_image(image_result, user_id=self.user_id, category=f"{self.category}", file_name=f"{self.tasks_id}.png")
+            # 第一帧图像
-            self.gen_product_data['status'] = "SUCCESS"
+            first_image = Image.fromarray(result_data[0])
-            self.gen_product_data['message'] = "success"
+            first_image_url = upload_first_image(first_image, user_id=self.user_id, category=f"{self.category}_first_img", file_name=f"{self.tasks_id}.png")
-            self.gen_product_data['image_url'] = str(image_url)
+
-            self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
+            # 上传GIF
+            gif_buffer = BytesIO()
+            imageio.mimsave(gif_buffer, result_data, format='GIF', fps=5)
+            gif_buffer.seek(0)
+            gif_url = upload_gif(gif_buffer=gif_buffer, user_id=self.user_id, category=f"{self.category}_gif", file_name=f"{self.tasks_id}.gif")
+
+            # 上传video
+            video_url = upload_video(frames=result_data, user_id=self.user_id, category=f"{self.category}_video", file_name=f"{self.tasks_id}.mp4")
+
+            self.pose_transform_data['status'] = "SUCCESS"
+            self.pose_transform_data['message'] = "success"
+            self.pose_transform_data['gif_url'] = str(gif_url)
+            self.pose_transform_data['video_url'] = str(video_url)
+            self.pose_transform_data['image_url'] = str(first_image_url)
+
+
+            self.redis_client.set(self.tasks_id, json.dumps(self.pose_transform_data))
 
     def read_tasks_status(self):
         status_data = self.redis_client.get(self.tasks_id)
@@ -63,51 +81,92 @@ class PoseTransformService:
 
     def get_result(self):
         try:
-            image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
+            pose_num = [self.pose_num] * 1
-            image = cv2.resize(image, (512, 768))
+            pose_num_obj = np.array(pose_num, dtype="object").reshape((-1, 1))
-            images = [image.astype(np.uint8)] * self.batch_size
+            input_pose_num = grpcclient.InferInput("pose_num", pose_num_obj.shape, np_to_triton_dtype(pose_num_obj.dtype))
+            input_pose_num.set_data_from_numpy(pose_num_obj)
 
-            image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
+            image_files = [self.image.astype(np.uint8)] * 1
+            image_files_obj = np.array(image_files, dtype=np.uint8).reshape((-1, 768, 512, 3))
+            input_image_files = grpcclient.InferInput("image_file", image_files_obj.shape, "UINT8")
+            input_image_files.set_data_from_numpy(image_files_obj)
 
-            input_image = grpcclient.InferInput("input_image", image_obj.shape, "UINT8")
+            ctx = self.grpc_client.async_infer(model_name="animatex_1", inputs=[input_pose_num, input_image_files], callback=self.callback)
-
+            time_out = 6000
-            input_image.set_data_from_numpy(image_obj)
+            while time_out > 0:
-
+                pose_transform_data, _ = self.read_tasks_status()
-            inputs = [input_image]
+                if pose_transform_data['status'] in ["REVOKED", "FAILURE"]:
-            # ctx = self.grpc_client.async_infer(model_name=GRI_MODEL_NAME_OVERALL, inputs=inputs, callback=self.callback)
+                    ctx.cancel()
-
+                    break
-            # time_out = 600
+                elif pose_transform_data['status'] == "SUCCESS":
-            # while time_out > 0:
+                    break
-            #     gen_product_data, _ = self.read_tasks_status()
+                time_out -= 1
-            #     if gen_product_data['status'] in ["REVOKED", "FAILURE", "NO_FACE"]:
+                time.sleep(0.1)
-            #         ctx.cancel()
+            pose_transform_data, _ = self.read_tasks_status()
-            #         break
+            return pose_transform_data
-            #     elif gen_product_data['status'] == "SUCCESS":
-            #         break
-            #     time_out -= 1
-            #     time.sleep(0.1)
-            gen_product_data, _ = self.read_tasks_status()
-            return gen_product_data
         except Exception as e:
-            self.gen_product_data['status'] = "FAILURE"
+            self.pose_transform_data['status'] = "FAILURE"
-            self.gen_product_data['message'] = str(e)
+            self.pose_transform_data['message'] = str(e)
-            self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
+            self.redis_client.set(self.tasks_id, json.dumps(self.pose_transform_data))
             raise Exception(str(e))
         finally:
-            dict_gen_product_data, str_gen_product_data = self.read_tasks_status()
+            dict_pose_transform_data, str_pose_transform_data = self.read_tasks_status()
             if DEBUG is False:
-                self.channel.basic_publish(exchange='', routing_key=PS_RABBITMQ_QUEUES, body=str_gen_product_data)
+                self.channel.basic_publish(exchange='', routing_key=PS_RABBITMQ_QUEUES, body=str_pose_transform_data)
-            logger.info(f" [x] Sent to： {PS_RABBITMQ_QUEUES} data：@@@@  {json.dumps(dict_gen_product_data, indent=4)}")
+            logger.info(f" [x] Sent to： {PS_RABBITMQ_QUEUES} data：@@@@  {json.dumps(dict_pose_transform_data, indent=4)}")
 
 
 def infer_cancel(tasks_id):
     redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
     data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'}
-    gen_product_data = json.dumps(data)
+    pose_transform_data = json.dumps(data)
-    redis_client.set(tasks_id, gen_product_data)
+    redis_client.set(tasks_id, pose_transform_data)
     return data
 
 
+def pre_processing_image(image_url):
+    image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL")
+    # 目标图片的尺寸
+    target_width = 512
+    target_height = 768
+
+    # 原始图片的尺寸
+    original_width, original_height = image.size
+
+    # 计算宽度和高度的缩放比例
+    width_ratio = target_width / original_width
+    height_ratio = target_height / original_height
+
+    # 选择较小的缩放比例，确保图片能完整放入目标图片中
+    scale_ratio = min(width_ratio, height_ratio)
+
+    # 计算调整后的尺寸
+    new_width = int(original_width * scale_ratio)
+    new_height = int(original_height * scale_ratio)
+
+    # 调整图片大小
+    resized_image = image.resize((new_width, new_height))
+
+    # 创建一个 512x768 的透明图片
+    result_image = Image.new("RGBA", (target_width, target_height), (255, 255, 255, 0))
+
+    # 计算需要粘贴的位置，使图片居中
+    x_offset = (target_width - new_width) // 2
+    y_offset = (target_height - new_height) // 2
+
+    # 将调整大小后的图片粘贴到透明图片上
+    if resized_image.mode == "RGBA":
+        result_image.paste(resized_image, (x_offset, y_offset), mask=resized_image.split()[3])
+    else:
+        result_image.paste(resized_image, (x_offset, y_offset))
+    result_image = result_image.convert("RGB")
+    image = np.array(result_image)
+
+    # image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
+
+    return image
+
+
 if __name__ == '__main__':
     rd = PoseTransformModel(
         tasks_id="123-89",

app/service/generate_image/utils/pose_transform_upload.py

@@ -0,0 +1,68 @@
+import io
+import logging
+
+import imageio
+import numpy as np
+# import boto3
+from minio import Minio
+
+from app.core.config import *
+from app.service.utils.new_oss_client import oss_upload_image
+
+minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
+
+
+def upload_first_image(image, user_id, category, file_name):
+    try:
+        image_data = io.BytesIO()
+        image.save(image_data, format='PNG')
+        image_data.seek(0)
+        image_bytes = image_data.read()
+        object_name = f'{user_id}/{category}/{file_name}'
+        req = oss_upload_image(oss_client=minio_client, bucket=GI_MINIO_BUCKET, object_name=object_name, image_bytes=image_bytes)
+        image_url = f"aida-users/{object_name}"
+        return image_url
+    except Exception as e:
+        logging.warning(f"upload_png_mask runtime exception : {e}")
+
+
+def upload_gif(gif_buffer, user_id, category, file_name):
+    try:
+        object_name = f'{user_id}/{category}/{file_name}'
+        req = minio_client.put_object(
+            "aida-users",
+            object_name,
+            gif_buffer,
+            length=gif_buffer.getbuffer().nbytes,
+            content_type="image/gif"
+        )
+        return f"aida-users/{object_name}"
+    except Exception as e:
+        logging.warning(f"upload_gif runtime exception : {e}")
+
+
+def upload_video(frames, user_id, category, file_name):
+    try:
+        video_buffer = io.BytesIO()
+        with imageio.get_writer(video_buffer, format='mp4', fps=24) as writer:
+            for frame in frames:
+                writer.append_data(frame)
+        video_buffer.seek(0)
+
+        object_name = f'{user_id}/{category}/{file_name}'
+        # 上传视频流到MinIO
+        minio_client.put_object(
+            bucket_name="aida-users",
+            object_name=object_name,
+            data=video_buffer,
+            length=video_buffer.getbuffer().nbytes,
+            content_type='video/mp4'
+        )
+        return f"aida-users/{object_name}"
+    except Exception as e:
+        logging.warning(f"upload_video runtime exception : {e}")
+
+
+if __name__ == '__main__':
+    images = np.random.randint(0, 256, size=(4, 512, 512, 3), dtype=np.uint8)
+    print(upload_video(images, user_id=89, category='test', file_name="1.mp4"))