committed

app.py

@@ -1,5 +1,6 @@
 import tensorflow as tf
 import gradio as gr
+import cv2
 
 json_file=open(r"dark_s.json","r")
 loaded_model_json=json_file.read()
@@ -20,6 +21,24 @@ loaded_model2 = tf.keras.models.model_from_json(loaded_model_json)
 loaded_model2.load_weights("wrinkl.h5")
 
 
+def image_bounder(img1):
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +'haarcascade_frontalface_default.xml')
+    eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +'haarcascade_eye.xml')
+    gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+    for (x,y,w,h) in faces:
+        cv2.rectangle(img1,(x,y),(x+w,y+h),(255,255,0),2)
+        roi_gray = gray[y:y+h, x:x+w]
+        roi_color = img1[y:y+h, x:x+w]
+
+        eyes = eye_cascade.detectMultiScale(roi_gray)
+
+        for (ex,ey,ew,eh) in eyes:
+            cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,127,255),2)
+
+    return img1
+
 
 def classifier(Imgarr):
     l = []
@@ -45,10 +64,13 @@ def classifier(Imgarr):
     else:
         l.append("wrinkles on face")
 
-    return Imgarr
+    return "Predictions are : "+str(l)
+
+def out(Imgarr):
+    return [image_bounder(Imgarr),classifier(Imgarr)]
 
-interface = gr.Interface(classifier,gr.inputs.Image(shape=(300,300)),
-                          outputs = "image",
+interface = gr.Interface(out,gr.inputs.Image(shape=(300,300)),
+                          outputs = ["image","text"],
                           description="Classifier of Aging Signs of Images",
                           title="Aging Signs Classifier",
                           examples=[['42.jpg'],['778.jpg'],['1836.png'],['71.jpg'],['56.jpg']])

requirements.txt

@@ -1,3 +1,5 @@
 tensorflow
 gradio
-numpy
+numpy
+cv2
+opencv-python