feat

fix  重写所有resize代码，mmcv替换为cv

app/service/attribute/service_att_recognition.py

@@ -107,12 +107,8 @@ class AttributeRecognition:
     @staticmethod
     def preprocess(img):
         img = mmcv.imread(img)
-        ori_shape = img.shape[:2]
         img_scale = (224, 224)
-        scale_factor = []
+        img = cv2.resize(img, img_scale)
-        img, x, y = mmcv.imresize(img, img_scale, return_scale=True)
-        scale_factor.append(x)
-        scale_factor.append(y)
         img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
         preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
         return preprocessed_img

app/service/attribute/service_category_recognition.py

@@ -41,10 +41,7 @@ class CategoryRecognition:
         img = mmcv.imread(img)
         # ori_shape = img.shape[:2]
         img_scale = (224, 224)
-        scale_factor = []
+        img = cv2.resize(img, img_scale)
-        img, x, y = mmcv.imresize(img, img_scale, return_scale=True)
-        scale_factor.append(x)
-        scale_factor.append(y)
         img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
         preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
         return preprocessed_img

app/service/design/utils/design_ensemble.py

@@ -27,7 +27,10 @@ from app.core.config import *
 def keypoint_preprocess(img_path):
     img = mmcv.imread(img_path)
     img_scale = (256, 256)
-    img, w_scale, h_scale = mmcv.imresize(img, img_scale, return_scale=True)
+    h, w = img.shape[:2]
+    img = cv2.resize(img, img_scale)
+    w_scale = img_scale[0] / w
+    h_scale = img_scale[1] / h
     img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
     preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
     return preprocessed_img, (w_scale, h_scale)
@@ -80,7 +83,6 @@ def seg_preprocess(img_path):
         img_scale_h = 1024
     # 如果图片size任意一边 大于 1024， 则会resize 成1024
     if ori_shape != (img_scale_w, img_scale_h):
-        # TODO 取消代码中所有 关于mmcv的resize
         # mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
         img = cv2.resize(img, (img_scale_h, img_scale_w))
     img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
@@ -130,12 +132,12 @@ def key_point_show(image_path, key_point_result=None):
 
 
 if __name__ == '__main__':
-    image = cv2.imread("./14162b58-f259-4833-98cb-89b9b496b251.jfif")
+    image = cv2.imread("9070101c-e5be-49b5-9602-4113a968969b.png")
     a = get_keypoint_result(image, "up")
     new_list = []
     print(list)
     for i in a[0]:
         new_list.append((int(i[0]), int(i[1])))
-    key_point_show("./14162b58-f259-4833-98cb-89b9b496b251.jfif", new_list)
+    key_point_show("9070101c-e5be-49b5-9602-4113a968969b.png", new_list)
     # a = get_seg_result(1, image)
     print(a)

app/service/generate_image/utils/image_processing.py

@@ -1,15 +1,14 @@
 import logging
 import time
 
+import cv2
 import mmcv
 import numpy as np
 import torch
 import tritonclient.http as httpclient
-import torch.nn.functional as F
-from app.core.config import *
-import cv2
 
-from app.service.generate_image.utils.upload_sd_image import upload_png_sd, upload_stain_png_sd, upload_face_png_sd
+from app.core.config import *
+from app.service.generate_image.utils.upload_sd_image import upload_stain_png_sd, upload_face_png_sd
 
 logger = logging.getLogger()
 
@@ -18,10 +17,7 @@ def seg_preprocess(img_path):
     img = mmcv.imread(img_path)
     ori_shape = img.shape[:2]
     img_scale = ori_shape
-    scale_factor = []
+    img = cv2.resize(img, img_scale)
-    img, x, y = mmcv.imresize(img, img_scale, return_scale=True)
-    scale_factor.append(x)
-    scale_factor.append(y)
     img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
     preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
     return preprocessed_img, ori_shape
@@ -105,6 +101,7 @@ def seg_infer_image(image_obj):
     seg_result = seg_postprocess(inference_output1, ori_shape)
     return seg_result
 
+
 # def seg_postprocess(output, ori_shape):
 #     seg_logit = F.interpolate(output, size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False)
 #     seg_logit = F.softmax(seg_logit, dim=1)
@@ -120,6 +117,7 @@ def seg_postprocess(output, ori_shape):
     # seg_pred = output.cpu().numpy()
     return output[0]
 
+
 def remove_background(image):
     image_obj, mask = get_mask(image)
     seg_result = seg_infer_image(image_obj)
@@ -245,12 +243,8 @@ def stain_detection(image, user_id, category, tasks_id, spot_size=100):
 def generate_category_recognition(image, gender):
     def preprocess(img):
         img = mmcv.imread(img)
-        # ori_shape = img.shape[:2]
         img_scale = (224, 224)
-        scale_factor = []
+        img = cv2.resize(img, img_scale)
-        img, x, y = mmcv.imresize(img, img_scale, return_scale=True)
-        scale_factor.append(x)
-        scale_factor.append(y)
         img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
         preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
         return preprocessed_img