Update main.py

main.py

@@ -1,42 +1,42 @@
-from fastapi import FastAPI,UploadFile
-from PIL import Image
-
-import mediapipe as mp
-import numpy as np
-import pandas as pd
-from io import BytesIO
-import onnxruntime as ort
-from Logs.detectnameanddistance import render
-
-
-
-FacesEmbedding=pd.read_csv("./Models/FacesMeanEmbeddings.csv",index_col=0)
-persons=list(FacesEmbedding.columns)
-model_path="./Models/FaceModelV5.onnx"
-EP_list = [ 'CPUExecutionProvider']
-Session = ort.InferenceSession(model_path,providers=EP_list)
-input_name = Session.get_inputs()[0].name
-output_name=Session.get_outputs()[0].name
-MediapipeModelPath=r"C:\Users\mf\Desktop\Final AI DIP Pro\mediapipemodels\face_landmarker.task"
-BaseOptions=mp.tasks.BaseOptions
-FaceLandMarker=mp.tasks.vision.FaceLandmarker
-FaceLandMarkerOptions=mp.tasks.vision.FaceLandmarkerOptions
-VisionRunningMode=mp.tasks.vision.RunningMode
-FaceLandMarkerResult=mp.tasks.vision.FaceLandmarkerResult
-options=FaceLandMarkerOptions(base_options=BaseOptions(model_asset_path=MediapipeModelPath),running_mode=VisionRunningMode.IMAGE)
-landmarker= FaceLandMarker.create_from_options(options)
-
-App=FastAPI()
-
-@App.post("/upload")
-async def detect(img:UploadFile):
-    
-    image=np.array(Image.open(BytesIO(img.file.read())))
-    mp_img=mp.Image(image_format=mp.ImageFormat.SRGB,data=image)
-    result=landmarker.detect(mp_img)
-    
-    if len(result.face_landmarks)==0:
-        return {"state":False,"message":"No Face Found","distance":0,"name":"null","x1":0,"x2":0,"y1":0,"y2":0}
-    x1,y1,x2,y2,name,distance=render(Session,input_name,output_name,FacesEmbedding,result,mp_img.numpy_view(),persons)
-    return {"state":True,"message":"null","distance":distance,"name":name,"x1":x1,"x2":x2,"y1":y1,"y2":y2}
-
+from fastapi import FastAPI,UploadFile
+from PIL import Image
+
+import mediapipe as mp
+import numpy as np
+import pandas as pd
+from io import BytesIO
+import onnxruntime as ort
+from Logs.detectnameanddistance import render
+
+
+
+FacesEmbedding=pd.read_csv("./Models/FacesMeanEmbeddings.csv",index_col=0)
+persons=list(FacesEmbedding.columns)
+model_path="./Models/FaceModelV5.onnx"
+EP_list = [ 'CPUExecutionProvider']
+Session = ort.InferenceSession(model_path,providers=EP_list)
+input_name = Session.get_inputs()[0].name
+output_name=Session.get_outputs()[0].name
+MediapipeModelPath="./Models/face_landmarker.task"
+BaseOptions=mp.tasks.BaseOptions
+FaceLandMarker=mp.tasks.vision.FaceLandmarker
+FaceLandMarkerOptions=mp.tasks.vision.FaceLandmarkerOptions
+VisionRunningMode=mp.tasks.vision.RunningMode
+FaceLandMarkerResult=mp.tasks.vision.FaceLandmarkerResult
+options=FaceLandMarkerOptions(base_options=BaseOptions(model_asset_path=MediapipeModelPath),running_mode=VisionRunningMode.IMAGE)
+landmarker= FaceLandMarker.create_from_options(options)
+
+App=FastAPI()
+
+@App.post("/upload")
+async def detect(img:UploadFile):
+    
+    image=np.array(Image.open(BytesIO(img.file.read())))
+    mp_img=mp.Image(image_format=mp.ImageFormat.SRGB,data=image)
+    result=landmarker.detect(mp_img)
+    
+    if len(result.face_landmarks)==0:
+        return {"state":False,"message":"No Face Found","distance":0,"name":"null","x1":0,"x2":0,"y1":0,"y2":0}
+    x1,y1,x2,y2,name,distance=render(Session,input_name,output_name,FacesEmbedding,result,mp_img.numpy_view(),persons)
+    return {"state":True,"message":"null","distance":distance,"name":name,"x1":x1,"x2":x2,"y1":y1,"y2":y2}
+