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Logs/detectnameanddistance.py

@@ -1,51 +1,52 @@
-import cv2
+from PIL import Image
 import numpy as np
 def render(Session,input_name,output_name,FacesEmbedding,results,FaceImage,persons):
-    for res in results.face_landmarks:
+    res = results.face_landmarks[0]
+    x_=int(res[145].x*FaceImage.shape[1])
+    y_=int(res[145].y*FaceImage.shape[0])
+    x2_=int(res[374].x*FaceImage.shape[1])
+    y2_=int(res[374].y*FaceImage.shape[0])
+    w=np.sqrt((x_-x2_)**2+(y_-y2_)**2)
+    W=6.3
+    f = 840
+    d = (W * f) / w
+    x=int(res[356].x*FaceImage.shape[1])
+    y=int(res[152].y*FaceImage.shape[0])
+    x2=int(res[162].x*FaceImage.shape[1])
+    y2=int(res[338].y*FaceImage.shape[0])
+    if x<FaceImage.shape[1]-10:
+        x+=10
+    if y>FaceImage.shape[0]-10:
+        y+=10
+    if x2>10:
+        x2-=10
+    if y2>10:
+        y2-=10
+    
+    
+    modelimg=FaceImage[np.abs(y2):np.abs(y),np.abs(x2):np.abs(x)]
+    
+    
+    if modelimg.size<9:
+        return x_,y_,x2_,y2,"Image Size is Zero ",d
+    modelimg=np.array(Image.fromarray(modelimg.astype("uint8")).resize((224,224)))
+    
+    modelimg=modelimg/255
+    
+    distances=[]
+    
+    for index,name in enumerate(persons):
+        output=np.squeeze(Session.run([output_name],{f"{input_name}":np.expand_dims(modelimg,axis=0).astype(np.float16)})[0])
+        personimpeding=FacesEmbedding[name].values
+        distance=np.sum(np.power(output-personimpeding,2))
+        distances.append(distance)
+    name=persons[np.argmin(distances)]
+    distance=distances[np.argmin(distances)]
+    if distance <0.3:
         
-        x_=int(res[145].x*FaceImage.shape[1])
-        y_=int(res[145].y*FaceImage.shape[0])
-        x2_=int(res[374].x*FaceImage.shape[1])
-        y2_=int(res[374].y*FaceImage.shape[0])
-        w=np.sqrt((x_-x2_)**2+(y_-y2_)**2)
-        W=6.3
-        f = 840
-        d = (W * f) / w
-        x=int(res[356].x*FaceImage.shape[1])
-        y=int(res[152].y*FaceImage.shape[0])
-        x2=int(res[162].x*FaceImage.shape[1])
-        y2=int(res[338].y*FaceImage.shape[0])
-        if x<FaceImage.shape[1]-10:
-            x+=10
-        if y>FaceImage.shape[0]-10:
-            y+=10
-        if x2>10:
-            x2-=10
-        if y2>10:
-            y2-=10
+        return x_,y_,x2_,y2,name,d
         
+    else:
+        return x_,y_,x2_,y2,"UnKnow",d
         
-        modelimg=FaceImage[y2:y,x2:x]
-        
-        
-        if modelimg.size<9:
-            continue
-        modelimg=cv2.resize(modelimg,(224,224)).astype(np.float32)
-        modelimg=modelimg/255
-        
-        distances=[]
-        if d>0:
-            for index,name in enumerate(persons):
-                output=np.squeeze(Session.run([output_name],{f"{input_name}":np.expand_dims(modelimg,axis=0).astype(np.float16)})[0])
-                personimpeding=FacesEmbedding[name].values
-                distance=np.sum(np.power(output-personimpeding,2))
-                distances.append(distance)
-            name=persons[np.argmin(distances)]
-            distance=distances[np.argmin(distances)]
-            if distance <0.3:
-                return x_,y_,x2_,y2,name,d
-                
-            else:
-                return x_,y_,x2_,y2,"UnKnow",d
-                
 

main.py

@@ -1,6 +1,6 @@
 from fastapi import FastAPI,UploadFile
 from PIL import Image
-import cv2
+
 import mediapipe as mp
 import numpy as np
 import pandas as pd
@@ -17,7 +17,7 @@ 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"
+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
@@ -36,18 +36,7 @@ async def detect(img:UploadFile):
     result=landmarker.detect(mp_img)
     
     if len(result.face_landmarks)==0:
-        return {"state":False,"message":"No Face Found","distance":0,"name":"null"}
+        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}
 
-# def getHighScore(Scores):
-    
-#     if type(Scores)==dict:
-#         return False
-#     scorep=0.00
-#     label="0-10"
-#     for Score_ in Scores:
-#         if Score_["score"] > scorep:
-#             scorep=float(Score_["score"])
-#             label=Score_["label"]
-#     return label

requirements.txt

@@ -1,8 +1,6 @@
 fastapi
 mediapipe
 numpy
-opencv_contrib_python
-opencv_python
 pandas
 uvicorn
 Requests