新建一个web服务器，用于接收照片，生成证件照

README.md

@@ -3,6 +3,8 @@
 # aiphoto
 最近要去办事情，很多地方都需要证件照，最近刚好在看AI，人脸识别，图形识别相关的知识，就打算利用这些技术开发一个证件照功能
 
+# 效果图
+
 # 文档
 
 通过文档可以快速上手和了解项目。

m_web/upload.py

@@ -0,0 +1,73 @@
+import os
+import tornado.web
+import shortuuid
+from u_2_net import my_u2net_test
+from to_background import to_background
+from to_background import to_standard_trimap
+from m_dlib import ai_crop
+
+# import PILImageMy as mypil
+
+
+class UploadHandler(tornado.web.RequestHandler):
+
+    def post(self, *args, **kwargs):
+
+        filename=shortuuid.uuid()
+        print(os.path.dirname(__file__))
+        parent_path = os.path.dirname(os.path.dirname(__file__))
+        filePath =""
+        # 查看上传文件的完整格式，files以字典形式返回
+        print(self.request.files)
+        # {'file1':
+        # [{'filename': '新建文本文档.txt', 'body': b'61 60 -83\r\n-445 64 -259', 'content_type': 'text/plain'}],
+        # 'file2':
+        filesDict = self.request.files
+        for inputname in filesDict:
+            # 第一层循环取出最外层信息，即input标签传回的name值
+            # 用过filename键值对对应，取出对应的上传文件的真实属性
+            http_file = filesDict[inputname]
+            for fileObj in http_file:
+
+                # 第二层循环取出完整的对象
+                # 取得当前路径下的 upfiles 文件夹+上fileObj.filename属性(即真实文件名)
+                filePath = os.path.join(parent_path, "static", filename+".jpg")
+                print(filePath)
+                with open(filePath, 'wb') as f:
+                    f.write(fileObj.body)
+
+
+
+        org_img = filePath
+
+        id_image = os.path.join(parent_path, "static", filename+"_meinv_id.png")
+        # 20200719
+        # 通过识别人脸关键点，裁剪图像
+        ai_crop.crop_photo(org_img,id_image )
+
+
+        print(org_img)
+        alpha_img = os.path.join(parent_path, "static", filename+"_alpha.png")
+        print(alpha_img)
+        alpha_resize_img = os.path.join(parent_path, "static", filename+"_alpha_resize.png")
+        print(alpha_resize_img)
+        #
+        # 通过u_2_net 获取 alpha
+        my_u2net_test.test_seg_trimap(id_image, alpha_img, alpha_resize_img)
+        #
+        # # 通过alpha 获取 trimap
+        trimap = os.path.join(parent_path, "static", filename+"_trimap_resize.png")
+        to_standard_trimap.to_standard_trimap(alpha_resize_img, trimap)
+        print(trimap)
+
+        id_image_org = os.path.join(parent_path, "static", filename+"_meinv_id_2in.png")
+
+        #
+        # 证件照添加蓝底纯色背景//"..\\aiphoto\\img\\meinv_trimap_resize.png"
+        #         to_standard_trimap.to_standard_trimap(alpha_resize_img, trimap)
+        to_background.to_background(id_image, trimap, id_image_org, "blue")
+        print(id_image_org)
+        self.write( "static/"+filename+"_meinv_id_2in.png")
+
+
+

m_web/web.py

@@ -0,0 +1,26 @@
+import tornado.ioloop
+import tornado.web
+import m_web.upload as upload
+import os
+
+
+class MainHandler(tornado.web.RequestHandler):
+    def get(self):
+        self.write("Hello, world")
+
+
+def make_app():
+    return tornado.web.Application([
+        (r"/", MainHandler),
+        (r"/eam/fileLocal/upload",    upload.UploadHandler),
+        (r"/eam/fileLocal/static",  tornado.web.StaticFileHandler, {"path": "/static"})
+    ],
+        static_path=os.path.dirname(os.path.dirname(__file__))+"/static"
+    )
+
+
+if __name__ == "__main__":
+    current_path = os.path.dirname(__file__)
+    app = make_app()
+    app.listen(8013)
+    tornado.ioloop.IOLoop.current().start()

u_2_net/my_u2net_test.py

@@ -7,7 +7,7 @@ from u_2_net.data_loader import RescaleT
 from u_2_net.data_loader import ToTensorLab
 from u_2_net.model import U2NET # full size version 173.6 MB
 from PIL import Image
-
+import os
 
 # normalize the predicted SOD probability map
 def normPRED(d):
@@ -44,7 +44,10 @@ def preprocess(image):
 def pre_net():
     # 采用n2net 模型数据
     model_name = 'u2net'
-    model_dir = '..\\aiphoto\\u_2_net\\saved_models/'+ model_name + '/' + model_name + '.pth'
+    path = os.path.dirname(__file__)
+    print(path)
+    model_dir = path+'/saved_models/'+ model_name + '/' + model_name + '.pth'
+    print(model_dir)
     print("...load U2NET---173.6 MB")
     net = U2NET(3,1)
     # 指定cpu