diff --git a/app/service/attribute/service_att_recognition.py b/app/service/attribute/service_att_recognition.py index ddcfd1c..1251891 100644 --- a/app/service/attribute/service_att_recognition.py +++ b/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, x, y = mmcv.imresize(img, img_scale, return_scale=True) - scale_factor.append(x) - scale_factor.append(y) + img = cv2.resize(img, img_scale) 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 diff --git a/app/service/attribute/service_category_recognition.py b/app/service/attribute/service_category_recognition.py index fb997e9..f917af2 100644 --- a/app/service/attribute/service_category_recognition.py +++ b/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, x, y = mmcv.imresize(img, img_scale, return_scale=True) - scale_factor.append(x) - scale_factor.append(y) + img = cv2.resize(img, img_scale) 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 diff --git a/app/service/design/utils/design_ensemble.py b/app/service/design/utils/design_ensemble.py index 58f0ec9..f4f6a34 100644 --- a/app/service/design/utils/design_ensemble.py +++ b/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) diff --git a/app/service/generate_image/utils/image_processing.py b/app/service/generate_image/utils/image_processing.py index dd64ace..af36188 100644 --- a/app/service/generate_image/utils/image_processing.py +++ b/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, x, y = mmcv.imresize(img, img_scale, return_scale=True) - scale_factor.append(x) - scale_factor.append(y) + img = cv2.resize(img, img_scale) 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, x, y = mmcv.imresize(img, img_scale, return_scale=True) - scale_factor.append(x) - scale_factor.append(y) + img = cv2.resize(img, img_scale) 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