feat design 预处理接口迁移
This commit is contained in:
zhouchengrong
320
app/service/design_pre_processing/service.py
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320
app/service/design_pre_processing/service.py
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import logging
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import time
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import cv2
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import numpy as np
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import torch
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from minio import Minio
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from pymilvus import connections, Collection
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from urllib3.exceptions import ResponseError
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import torch.nn.functional as F
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import tritonclient.grpc as grpcclient
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import io
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from app.core.config import *
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from app.service.design.utils.design_ensemble import get_keypoint_result
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class DesignPreprocessing:
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def __init__(self):
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self.minio_client = Minio(
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MINIO_URL,
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access_key=MINIO_ACCESS,
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secret_key=MINIO_SECRET,
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secure=MINIO_SECURE)
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# @ RunTime
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def pipeline(self, image_list):
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sketches_list = self.read_image(image_list)
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logging.info("read image success")
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bounding_box_sketches_list = self.bounding_box(sketches_list)
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logging.info("bounding box image success")
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super_resolution_list = self.super_resolution(bounding_box_sketches_list)
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logging.info("super_resolution_list image success")
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infer_sketches_list = self.infer_image(super_resolution_list)
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logging.info("infer image success")
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result = self.composing_image(infer_sketches_list)
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logging.info("Replenish white edge image success")
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for d in result:
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if 'image_obj' in d:
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del d['image_obj']
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if 'obj' in d:
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del d['obj']
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if 'keypoint_result' in d:
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del d['keypoint_result']
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return result
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def read_image(self, image_list):
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for obj in image_list:
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file = self.minio_client.get_object(obj['image_url'].split("/", 1)[0], obj['image_url'].split("/", 1)[1]).data
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image = cv2.imdecode(np.frombuffer(file, np.uint8), 1)
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if len(image.shape) == 2:
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image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
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elif image.shape[2] == 4: # 如果是四通道 mask
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image = image[:, :, :3]
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obj["image_obj"] = image
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return image_list
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# @ RunTime
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def bounding_box(self, image_list):
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for item in image_list:
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image = item['image_obj']
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# 使用Canny边缘检测来检测物体的轮廓
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edges = cv2.Canny(image, 50, 150)
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# 查找轮廓
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contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 初始化包围所有外接矩形的大矩形的坐标
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x_min, y_min, x_max, y_max = float('inf'), float('inf'), -1, -1
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# 遍历所有外接矩形，更新大矩形的坐标
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for contour in contours:
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x, y, w, h = cv2.boundingRect(contour)
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x_min = min(x_min, x)
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y_min = min(y_min, y)
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x_max = max(x_max, x + w)
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y_max = max(y_max, y + h)
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if IF_DEBUG_SHOW:
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image_with_big_rect = cv2.rectangle(image.copy(), (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
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cv2.imshow("bounding_box image", image_with_big_rect)
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cv2.waitKey(0)
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# 根据大矩形的坐标来裁剪原始图像
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if len(contours) > 0:
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cropped_image = image[y_min:y_max, x_min:x_max]
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item['obj'] = cropped_image # 新shape图像
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# 取消直接覆盖，新增size判断
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# try:
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# # 覆盖到minio
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# image_bytes = cv2.imencode(".jpg", cropped_image)[1].tobytes()
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# 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", )
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# print(f"Object '{item['image_url'].split('/', 1)[1]}' overwritten successfully.")
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# except ResponseError as err:
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# print(f"Error: {err}")
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else:
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item['obj'] = image
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return image_list
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def super_resolution(self, image_list):
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for item in image_list:
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# 判断 两边是否同时都小于512 因为此处做四倍超分
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if item['obj'].shape[0] <= 512 and item['obj'].shape[1] <= 512:
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# 如果任意一边小于256则超分
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if item['obj'].shape[0] <= 256 or item['obj'].shape[1] <= 256:
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# 超分
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img = item['obj'].astype(np.float32) / 255.
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sample = np.transpose(img if img.shape[2] == 1 else img[:, :, [2, 1, 0]], (2, 0, 1))
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sample = torch.from_numpy(sample).float().unsqueeze(0).numpy()
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inputs = [
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grpcclient.InferInput("input", sample.shape, datatype="FP32")
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]
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inputs[0].set_data_from_numpy(sample)
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triton_client = grpcclient.InferenceServerClient(url=SR_TRITON_URL)
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result = triton_client.infer(model_name=SR_MODEL_NAME, inputs=inputs)
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result_image = result.as_numpy(f'output')[0]
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sr_output = torch.tensor(result_image)
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output = sr_output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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if output.ndim == 3:
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output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
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output = (output * 255.0).round().astype(np.uint8)
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item['obj'] = output
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try:
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# 覆盖到minio
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image_bytes = cv2.imencode(".jpg", item['obj'])[1].tobytes()
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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", )
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print(f"Object '{item['image_url'].split('/', 1)[1]}' overwritten successfully.")
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except ResponseError as err:
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print(f"Error: {err}")
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return image_list
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# @ RunTime
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def infer_image(self, image_list):
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for sketch in image_list:
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# 小写
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image_category = sketch['image_category'].lower()
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# 判断上下装
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sketch['site'] = 'up' if image_category in ['blouse', 'outwear', 'dress', 'tops'] else 'down'
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# 推理得到keypoint
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sketch['keypoint_result'] = self.keypoint_cache(sketch)
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if IF_DEBUG_SHOW:
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debug_show_image = sketch['obj'].copy()
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points_list = []
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point_size = 1
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point_color = (0, 0, 255) # BGR
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thickness = 4 # 可以为 0 、4、8
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for i in sketch['keypoint_result'].values():
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points_list.append((int(i[1]), int(i[0])))
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for point in points_list:
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cv2.circle(debug_show_image, point, point_size, point_color, thickness)
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cv2.imshow("", debug_show_image)
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cv2.waitKey(0)
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# # 关键点在上部则推理seg
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# if sketch["site"] == "up":
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# # 判断seg缓存是否存在,是否与当前图片shape一致
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# seg_result = self.search_seg_result(sketch["image_id"], sketch["obj"].shape)
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# if seg_result is False:
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# # 推理seg + 保存
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# seg_result = get_seg_result(sketch['image_id'], sketch['obj'])
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return image_list
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# @ RunTime
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def composing_image(self, image_list):
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for image in image_list:
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if image['site'] == 'down':
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image_width = image['obj'].shape[1]
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waist_width = image['keypoint_result']['waistband_right'][1] - image['keypoint_result']['waistband_left'][1]
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scale = 0.4
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if waist_width / scale >= image['obj'].shape[1]:
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add_width = int((waist_width / scale - image_width) / 2)
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ret = cv2.copyMakeBorder(image['obj'], 0, 0, add_width, add_width, cv2.BORDER_CONSTANT, value=(256, 256, 256))
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if IF_DEBUG_SHOW:
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cv2.imshow("composing_image", ret)
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cv2.waitKey(0)
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image_bytes = cv2.imencode(".jpg", ret)[1].tobytes()
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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}"
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else:
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image_bytes = cv2.imencode(".jpg", image['obj'])[1].tobytes()
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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}"
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else:
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scale = 0.4
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image_width = image['obj'].shape[1]
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waist_width = image['keypoint_result']['armpit_right'][1] - image['keypoint_result']['armpit_left'][1]
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if waist_width / scale >= image_width:
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add_width = int((waist_width / scale - image_width) / 2)
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ret = cv2.copyMakeBorder(image['obj'], 0, 0, add_width, add_width, cv2.BORDER_CONSTANT, value=(256, 256, 256))
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if IF_DEBUG_SHOW:
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cv2.imshow("composing_image", ret)
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cv2.waitKey(0)
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image_bytes = cv2.imencode(".jpg", ret)[1].tobytes()
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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}"
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else:
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image_bytes = cv2.imencode(".jpg", image['obj'])[1].tobytes()
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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}"
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return image_list
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@staticmethod
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def select_seg_result(image_id, image_obj):
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try:
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# 如果shape不匹配 返回false
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result = np.load(f"seg_result/{image_id}.npy").astype(np.int64)
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if result.shape[1] == image_obj.shape[0] and result.shape[2] == image_obj.shape[1]:
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return result
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else:
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return False
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except FileNotFoundError as e:
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logging.warning(f"{image_id} Image segmentation results cache file does not exist : {e}")
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return False
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@staticmethod
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def search_seg_result(image_id, ori_shape):
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try:
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# connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT)
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# collection = Collection(MILVUS_TABLE_SEG) # Get an existing collection.
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# collection.load()
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# start_time = time.time()
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# res = collection.query(
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# expr=f"seg_id == {image_id}",
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# offset=0,
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# limit=10,
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# output_fields=["seg_cache"],
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# )
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# logging.info(f"search seg cache time ： {time.time() - start_time}")
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# if len(res):
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# vector = np.reshape(res[0]['seg_cache'] + res[1]['seg_cache'], (224, 224))
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# array_2d_exact = F.interpolate(torch.tensor(vector).unsqueeze(0).unsqueeze(0), size=ori_shape, mode='bilinear', align_corners=False)
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# array_2d_exact = array_2d_exact.squeeze().numpy()
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# return array_2d_exact
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# else:
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return False
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except Exception as e:
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logging.warning(f"{image_id} Image segmentation results cache file does not exist : {e}")
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return False
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def keypoint_cache(self, sketch):
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try:
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# connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT)
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# collection = Collection(MILVUS_TABLE_KEYPOINT) # Get an existing collection.
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# collection.load()
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start_time = time.time()
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# res = collection.query(
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# expr=f"keypoint_id == {sketch['image_id']}",
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# offset=0,
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# limit=1,
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# output_fields=["keypoint_cache", "keypoint_site"],
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# )
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res = []
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logging.info(f"search keypoint time : {time.time() - start_time}")
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if len(res) == 0:
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# 没有结果 直接推理拿结果 并保存
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keypoint_infer_result = self.infer_keypoint_result(sketch)
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return self.save_keypoint_cache(sketch, keypoint_infer_result)
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elif res[0]["keypoint_site"] == "all" or res[0]["keypoint_site"] == sketch['site']:
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# 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
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return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, np.array(res[0]['keypoint_vector']).astype(int).reshape(12, 2).tolist()))
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elif res[0]["keypoint_site"] != sketch['site']:
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# 需要的类型和查询到的不一致，则更新类型为all
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keypoint_infer_result = self.infer_keypoint_result(sketch)
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return self.update_keypoint_cache(sketch, keypoint_infer_result, res[0]['keypoint_vector'])
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except Exception as e:
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logging.info(f"search keypoint cache milvus error {e}")
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return False
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# @ RunTime
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def infer_keypoint_result(self, sketch):
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keypoint_infer_result = get_keypoint_result(sketch["obj"], sketch['site']) # 推理结果
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return keypoint_infer_result
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@staticmethod
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# @ RunTime
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def save_keypoint_cache(sketch, keypoint_infer_result):
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if sketch['site'] == "down":
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zeros = np.zeros(20, dtype=int)
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result = np.concatenate([zeros, keypoint_infer_result.flatten()])
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else:
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zeros = np.zeros(4, dtype=int)
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result = np.concatenate([keypoint_infer_result.flatten(), zeros])
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data = [
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[int(sketch['image_id'])],
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[sketch['site']],
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[result.tolist()]
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]
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try:
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# connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT)
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start_time = time.time()
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# collection = Collection(MILVUS_TABLE_KEYPOINT) # Get an existing collection.
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# mr = collection.insert(data)
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# logging.info(f"save keypoint time : {time.time() - start_time}")
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return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
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except Exception as e:
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logging.info(f"save keypoint cache milvus error : {e}")
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return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
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@staticmethod
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def update_keypoint_cache(sketch, infer_result, search_result):
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if sketch['site'] == "up":
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# 需要的是up 即推理出来的是up 那么查询的就是down
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result = np.concatenate([infer_result.flatten(), search_result[-4:]])
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else:
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# 需要的是down 即推理出来的是down 那么查询的就是up
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result = np.concatenate([search_result[:20], infer_result.flatten()])
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data = [
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[int(sketch['image_id'])],
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["all"],
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[result.tolist()]
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]
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try:
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# connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT)
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start_time = time.time()
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# collection = Collection(MILVUS_TABLE_KEYPOINT) # Get an existing collection.
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# mr = collection.upsert(data)
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# logging.info(f"save keypoint time : {time.time() - start_time}")
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return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
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except Exception as e:
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logging.info(f"save keypoint cache milvus error : {e}")
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return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
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