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