362 lines
20 KiB
Python
362 lines
20 KiB
Python
import logging
|
||
import time
|
||
|
||
import cv2
|
||
import numpy as np
|
||
import torch
|
||
from minio import Minio
|
||
from pymilvus import connections, Collection
|
||
from urllib3.exceptions import ResponseError
|
||
import torch.nn.functional as F
|
||
import tritonclient.grpc as grpcclient
|
||
import io
|
||
|
||
from app.core.config import *
|
||
from app.service.design.utils.design_ensemble import get_keypoint_result
|
||
from app.service.utils.oss_client import oss_get_image, oss_upload_image
|
||
|
||
|
||
class DesignPreprocessing:
|
||
# def __init__(self):
|
||
# self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
|
||
|
||
# @ RunTime
|
||
def pipeline(self, image_list):
|
||
sketches_list = self.read_image(image_list)
|
||
logging.info("read image success")
|
||
|
||
bounding_box_sketches_list = self.bounding_box(sketches_list)
|
||
logging.info("bounding box image success")
|
||
|
||
super_resolution_list = self.super_resolution(bounding_box_sketches_list)
|
||
logging.info("super_resolution_list image success")
|
||
|
||
infer_sketches_list = self.infer_image(super_resolution_list)
|
||
logging.info("infer image success")
|
||
|
||
result = self.composing_image(infer_sketches_list)
|
||
logging.info("Replenish white edge image success")
|
||
|
||
for d in result:
|
||
if 'image_obj' in d:
|
||
del d['image_obj']
|
||
if 'obj' in d:
|
||
del d['obj']
|
||
if 'keypoint_result' in d:
|
||
del d['keypoint_result']
|
||
return result
|
||
|
||
def read_image(self, image_list):
|
||
for obj in image_list:
|
||
# file = self.minio_client.get_object(obj['image_url'].split("/", 1)[0], obj['image_url'].split("/", 1)[1]).data
|
||
# image = cv2.imdecode(np.frombuffer(file, np.uint8), 1)
|
||
image = oss_get_image(bucket=obj['image_url'].split("/", 1)[0], object_name=obj['image_url'].split("/", 1)[1], data_type="cv2")
|
||
if len(image.shape) == 2:
|
||
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
|
||
elif image.shape[2] == 4: # 如果是四通道 mask
|
||
image = image[:, :, :3]
|
||
obj["image_obj"] = image
|
||
return image_list
|
||
|
||
# @ RunTime
|
||
def bounding_box(self, image_list):
|
||
for item in image_list:
|
||
image = item['image_obj']
|
||
# 使用Canny边缘检测来检测物体的轮廓
|
||
edges = cv2.Canny(image, 50, 150)
|
||
# 查找轮廓
|
||
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||
# 初始化包围所有外接矩形的大矩形的坐标
|
||
x_min, y_min, x_max, y_max = float('inf'), float('inf'), -1, -1
|
||
# 遍历所有外接矩形，更新大矩形的坐标
|
||
for contour in contours:
|
||
x, y, w, h = cv2.boundingRect(contour)
|
||
x_min = min(x_min, x)
|
||
y_min = min(y_min, y)
|
||
x_max = max(x_max, x + w)
|
||
y_max = max(y_max, y + h)
|
||
|
||
if IF_DEBUG_SHOW:
|
||
image_with_big_rect = cv2.rectangle(image.copy(), (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
|
||
cv2.imshow("bounding_box image", image_with_big_rect)
|
||
cv2.waitKey(0)
|
||
|
||
# 根据大矩形的坐标来裁剪原始图像
|
||
if len(contours) > 0:
|
||
cropped_image = image[y_min:y_max, x_min:x_max]
|
||
item['obj'] = cropped_image # 新shape图像
|
||
# 取消直接覆盖，新增size判断
|
||
# try:
|
||
# # 覆盖到minio
|
||
# image_bytes = cv2.imencode(".jpg", cropped_image)[1].tobytes()
|
||
# self.minio_client.put_object(item['image_url'].split("/", 1)[0], item['image_url'].split("/", 1)[1], io.BytesIO(image_bytes), len(image_bytes), content_type="image/jpeg", )
|
||
# print(f"Object '{item['image_url'].split('/', 1)[1]}' overwritten successfully.")
|
||
# except ResponseError as err:
|
||
# print(f"Error: {err}")
|
||
else:
|
||
item['obj'] = image
|
||
return image_list
|
||
|
||
def super_resolution(self, image_list):
|
||
for item in image_list:
|
||
# 判断 两边是否同时都小于512 因为此处做四倍超分
|
||
if item['obj'].shape[0] <= 512 and item['obj'].shape[1] <= 512:
|
||
# 如果任意一边小于256则超分
|
||
if item['obj'].shape[0] <= 256 or item['obj'].shape[1] <= 256:
|
||
# 超分
|
||
img = item['obj'].astype(np.float32) / 255.
|
||
sample = np.transpose(img if img.shape[2] == 1 else img[:, :, [2, 1, 0]], (2, 0, 1))
|
||
sample = torch.from_numpy(sample).float().unsqueeze(0).numpy()
|
||
inputs = [
|
||
grpcclient.InferInput("input", sample.shape, datatype="FP32")
|
||
]
|
||
inputs[0].set_data_from_numpy(sample)
|
||
triton_client = grpcclient.InferenceServerClient(url=SR_TRITON_URL)
|
||
result = triton_client.infer(model_name=SR_MODEL_NAME, inputs=inputs)
|
||
result_image = result.as_numpy(f'output')[0]
|
||
sr_output = torch.tensor(result_image)
|
||
output = sr_output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
|
||
if output.ndim == 3:
|
||
output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
|
||
output = (output * 255.0).round().astype(np.uint8)
|
||
item['obj'] = output
|
||
try:
|
||
# 覆盖到minio
|
||
image_bytes = cv2.imencode(".jpg", item['obj'])[1].tobytes()
|
||
# self.minio_client.put_object(item['image_url'].split("/", 1)[0], item['image_url'].split("/", 1)[1], io.BytesIO(image_bytes), len(image_bytes), content_type="image/jpeg", )
|
||
bucket_name = item['image_url'].split("/", 1)[0]
|
||
object_name = item['image_url'].split("/", 1)[1]
|
||
oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
|
||
print(f"Object '{item['image_url'].split('/', 1)[1]}' overwritten successfully.")
|
||
except ResponseError as err:
|
||
print(f"Error: {err}")
|
||
return image_list
|
||
|
||
# @ RunTime
|
||
def infer_image(self, image_list):
|
||
for sketch in image_list:
|
||
# 小写
|
||
image_category = sketch['image_category'].lower()
|
||
# 判断上下装
|
||
sketch['site'] = 'up' if image_category in ['blouse', 'outwear', 'dress', 'tops'] else 'down'
|
||
# 推理得到keypoint
|
||
sketch['keypoint_result'] = self.keypoint_cache(sketch)
|
||
|
||
if IF_DEBUG_SHOW:
|
||
debug_show_image = sketch['obj'].copy()
|
||
points_list = []
|
||
point_size = 1
|
||
point_color = (0, 0, 255) # BGR
|
||
thickness = 4 # 可以为 0 、4、8
|
||
for i in sketch['keypoint_result'].values():
|
||
points_list.append((int(i[1]), int(i[0])))
|
||
for point in points_list:
|
||
cv2.circle(debug_show_image, point, point_size, point_color, thickness)
|
||
cv2.imshow("", debug_show_image)
|
||
cv2.waitKey(0)
|
||
# # 关键点在上部则推理seg
|
||
# if sketch["site"] == "up":
|
||
# # 判断seg缓存是否存在,是否与当前图片shape一致
|
||
# seg_result = self.search_seg_result(sketch["image_id"], sketch["obj"].shape)
|
||
# if seg_result is False:
|
||
# # 推理seg + 保存
|
||
# seg_result = get_seg_result(sketch['image_id'], sketch['obj'])
|
||
return image_list
|
||
|
||
# @ RunTime
|
||
def composing_image(self, image_list):
|
||
for image in image_list:
|
||
''' 比例相同 整合上下装代码'''
|
||
image_width = image['obj'].shape[1]
|
||
waist_width = image['keypoint_result']['waistband_right'][1] - image['keypoint_result']['waistband_left'][1]
|
||
scale = 0.4
|
||
if waist_width / scale >= image_width:
|
||
add_width = int((waist_width / scale - image_width) / 2)
|
||
ret = cv2.copyMakeBorder(image['obj'], 0, 0, add_width, add_width, cv2.BORDER_CONSTANT, value=(256, 256, 256))
|
||
if IF_DEBUG_SHOW:
|
||
cv2.imshow("composing_image", ret)
|
||
cv2.waitKey(0)
|
||
image_bytes = cv2.imencode(".jpg", ret)[1].tobytes()
|
||
# image['show_image_url'] = f"{image['image_url'].split('/', 1)[0]}/{self.minio_client.put_object(image['image_url'].split('/', 1)[0], image['image_url'].split('/', 1)[1].replace('.', '-show.'), io.BytesIO(image_bytes), len(image_bytes), content_type='image/jpeg').object_name}"
|
||
bucket_name = image['image_url'].split('/', 1)[0]
|
||
object_name = image['image_url'].split('/', 1)[1]
|
||
oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
|
||
image['show_image_url'] = f"{bucket_name}/{object_name}"
|
||
else:
|
||
image_bytes = cv2.imencode(".jpg", image['obj'])[1].tobytes()
|
||
# image['show_image_url'] = f"{image['image_url'].split('/', 1)[0]}/{self.minio_client.put_object(image['image_url'].split('/', 1)[0], image['image_url'].split('/', 1)[1].replace('.', '-show.'), io.BytesIO(image_bytes), len(image_bytes), content_type='image/jpeg').object_name}"
|
||
bucket_name = image['image_url'].split('/', 1)[0]
|
||
object_name = image['image_url'].split('/', 1)[1]
|
||
oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
|
||
image['show_image_url'] = f"{bucket_name}/{object_name}"
|
||
|
||
# if image['site'] == 'down':
|
||
# image_width = image['obj'].shape[1]
|
||
# waist_width = image['keypoint_result']['waistband_right'][1] - image['keypoint_result']['waistband_left'][1]
|
||
# scale = 0.4
|
||
# if waist_width / scale >= image_width:
|
||
# add_width = int((waist_width / scale - image_width) / 2)
|
||
# ret = cv2.copyMakeBorder(image['obj'], 0, 0, add_width, add_width, cv2.BORDER_CONSTANT, value=(256, 256, 256))
|
||
# if IF_DEBUG_SHOW:
|
||
# cv2.imshow("composing_image", ret)
|
||
# cv2.waitKey(0)
|
||
# image_bytes = cv2.imencode(".jpg", ret)[1].tobytes()
|
||
# # image['show_image_url'] = f"{image['image_url'].split('/', 1)[0]}/{self.minio_client.put_object(image['image_url'].split('/', 1)[0], image['image_url'].split('/', 1)[1].replace('.', '-show.'), io.BytesIO(image_bytes), len(image_bytes), content_type='image/jpeg').object_name}"
|
||
# bucket_name = image['image_url'].split('/', 1)[0]
|
||
# object_name = image['image_url'].split('/', 1)[1]
|
||
# oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
|
||
# image['show_image_url'] = f"{bucket_name}/{object_name}"
|
||
# else:
|
||
# image_bytes = cv2.imencode(".jpg", image['obj'])[1].tobytes()
|
||
# # image['show_image_url'] = f"{image['image_url'].split('/', 1)[0]}/{self.minio_client.put_object(image['image_url'].split('/', 1)[0], image['image_url'].split('/', 1)[1].replace('.', '-show.'), io.BytesIO(image_bytes), len(image_bytes), content_type='image/jpeg').object_name}"
|
||
# bucket_name = image['image_url'].split('/', 1)[0]
|
||
# object_name = image['image_url'].split('/', 1)[1]
|
||
# oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
|
||
# image['show_image_url'] = f"{bucket_name}/{object_name}"
|
||
# else:
|
||
# image_width = image['obj'].shape[1]
|
||
# waist_width = image['keypoint_result']['waistband_right'][1] - image['keypoint_result']['waistband_left'][1]
|
||
# scale = 0.4
|
||
# if waist_width / scale >= image_width:
|
||
# add_width = int((waist_width / scale - image_width) / 2)
|
||
# ret = cv2.copyMakeBorder(image['obj'], 0, 0, add_width, add_width, cv2.BORDER_CONSTANT, value=(256, 256, 256))
|
||
# if IF_DEBUG_SHOW:
|
||
# cv2.imshow("composing_image", ret)
|
||
# cv2.waitKey(0)
|
||
# image_bytes = cv2.imencode(".jpg", ret)[1].tobytes()
|
||
# # image['show_image_url'] = f"{image['image_url'].split('/', 1)[0]}/{self.minio_client.put_object(image['image_url'].split('/', 1)[0], image['image_url'].split('/', 1)[1].replace('.', '-show.'), io.BytesIO(image_bytes), len(image_bytes), content_type='image/jpeg').object_name}"
|
||
# bucket_name = image['image_url'].split('/', 1)[0]
|
||
# object_name = image['image_url'].split('/', 1)[1]
|
||
# oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
|
||
# image['show_image_url'] = f"{bucket_name}/{object_name}"
|
||
# else:
|
||
# image_bytes = cv2.imencode(".jpg", image['obj'])[1].tobytes()
|
||
# # image['show_image_url'] = f"{image['image_url'].split('/', 1)[0]}/{self.minio_client.put_object(image['image_url'].split('/', 1)[0], image['image_url'].split('/', 1)[1].replace('.', '-show.'), io.BytesIO(image_bytes), len(image_bytes), content_type='image/jpeg').object_name}"
|
||
# bucket_name = image['image_url'].split('/', 1)[0]
|
||
# object_name = image['image_url'].split('/', 1)[1]
|
||
# oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
|
||
# image['show_image_url'] = f"{bucket_name}/{object_name}"
|
||
return image_list
|
||
|
||
@staticmethod
|
||
def select_seg_result(image_id, image_obj):
|
||
try:
|
||
# 如果shape不匹配 返回false
|
||
result = np.load(f"seg_result/{image_id}.npy").astype(np.int64)
|
||
if result.shape[1] == image_obj.shape[0] and result.shape[2] == image_obj.shape[1]:
|
||
return result
|
||
else:
|
||
return False
|
||
except FileNotFoundError as e:
|
||
logging.warning(f"{image_id} Image segmentation results cache file does not exist : {e}")
|
||
return False
|
||
|
||
@staticmethod
|
||
def search_seg_result(image_id, ori_shape):
|
||
try:
|
||
# connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT)
|
||
# collection = Collection(MILVUS_TABLE_SEG) # Get an existing collection.
|
||
# collection.load()
|
||
# start_time = time.time()
|
||
# res = collection.query(
|
||
# expr=f"seg_id == {image_id}",
|
||
# offset=0,
|
||
# limit=10,
|
||
# output_fields=["seg_cache"],
|
||
# )
|
||
# logging.info(f"search seg cache time ： {time.time() - start_time}")
|
||
|
||
# if len(res):
|
||
# vector = np.reshape(res[0]['seg_cache'] + res[1]['seg_cache'], (224, 224))
|
||
# array_2d_exact = F.interpolate(torch.tensor(vector).unsqueeze(0).unsqueeze(0), size=ori_shape, mode='bilinear', align_corners=False)
|
||
# array_2d_exact = array_2d_exact.squeeze().numpy()
|
||
# return array_2d_exact
|
||
# else:
|
||
return False
|
||
except Exception as e:
|
||
logging.warning(f"{image_id} Image segmentation results cache file does not exist : {e}")
|
||
return False
|
||
|
||
def keypoint_cache(self, sketch):
|
||
try:
|
||
# connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT)
|
||
# collection = Collection(MILVUS_TABLE_KEYPOINT) # Get an existing collection.
|
||
# collection.load()
|
||
start_time = time.time()
|
||
# res = collection.query(
|
||
# expr=f"keypoint_id == {sketch['image_id']}",
|
||
# offset=0,
|
||
# limit=1,
|
||
# output_fields=["keypoint_cache", "keypoint_site"],
|
||
# )
|
||
res = []
|
||
logging.info(f"search keypoint time : {time.time() - start_time}")
|
||
if len(res) == 0:
|
||
# 没有结果 直接推理拿结果 并保存
|
||
keypoint_infer_result = self.infer_keypoint_result(sketch)
|
||
return self.save_keypoint_cache(sketch, keypoint_infer_result)
|
||
elif res[0]["keypoint_site"] == "all" or res[0]["keypoint_site"] == sketch['site']:
|
||
# 需要的类型和查询的类型一致，或者查询的类型为all 则直接返回查询的结果
|
||
return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, np.array(res[0]['keypoint_vector']).astype(int).reshape(12, 2).tolist()))
|
||
elif res[0]["keypoint_site"] != sketch['site']:
|
||
# 需要的类型和查询到的不一致，则更新类型为all
|
||
keypoint_infer_result = self.infer_keypoint_result(sketch)
|
||
return self.update_keypoint_cache(sketch, keypoint_infer_result, res[0]['keypoint_vector'])
|
||
except Exception as e:
|
||
logging.info(f"search keypoint cache milvus error {e}")
|
||
return False
|
||
|
||
# @ RunTime
|
||
def infer_keypoint_result(self, sketch):
|
||
keypoint_infer_result = get_keypoint_result(sketch["obj"], sketch['site']) # 推理结果
|
||
return keypoint_infer_result
|
||
|
||
@staticmethod
|
||
# @ RunTime
|
||
def save_keypoint_cache(sketch, keypoint_infer_result):
|
||
if sketch['site'] == "down":
|
||
zeros = np.zeros(20, dtype=int)
|
||
result = np.concatenate([zeros, keypoint_infer_result.flatten()])
|
||
else:
|
||
zeros = np.zeros(4, dtype=int)
|
||
result = np.concatenate([keypoint_infer_result.flatten(), zeros])
|
||
data = [
|
||
[int(sketch['image_id'])],
|
||
[sketch['site']],
|
||
[result.tolist()]
|
||
]
|
||
try:
|
||
# connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT)
|
||
start_time = time.time()
|
||
# collection = Collection(MILVUS_TABLE_KEYPOINT) # Get an existing collection.
|
||
# mr = collection.insert(data)
|
||
# logging.info(f"save keypoint time : {time.time() - start_time}")
|
||
return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
|
||
except Exception as e:
|
||
logging.info(f"save keypoint cache milvus error : {e}")
|
||
return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
|
||
|
||
@staticmethod
|
||
def update_keypoint_cache(sketch, infer_result, search_result):
|
||
if sketch['site'] == "up":
|
||
# 需要的是up 即推理出来的是up 那么查询的就是down
|
||
result = np.concatenate([infer_result.flatten(), search_result[-4:]])
|
||
else:
|
||
# 需要的是down 即推理出来的是down 那么查询的就是up
|
||
result = np.concatenate([search_result[:20], infer_result.flatten()])
|
||
data = [
|
||
[int(sketch['image_id'])],
|
||
["all"],
|
||
[result.tolist()]
|
||
]
|
||
try:
|
||
# connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT)
|
||
start_time = time.time()
|
||
# collection = Collection(MILVUS_TABLE_KEYPOINT) # Get an existing collection.
|
||
# mr = collection.upsert(data)
|
||
# logging.info(f"save keypoint time : {time.time() - start_time}")
|
||
return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
|
||
except Exception as e:
|
||
logging.info(f"save keypoint cache milvus error : {e}")
|
||
return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
|