Added initial open cv iamge editing

app.py

@@ -3,6 +3,7 @@ import gradio as gr
 import numpy as np
 from deepface import DeepFace
 from pymongo.mongo_client import MongoClient
+import cv2
 
 credentials = "jamshaid:jamshaid19gh"
 
@@ -89,8 +90,21 @@ def predict_image(image):
           print("5")
 
         identities.append({"name":name , "facial_area":target_embedding_obj["facial_area"]})  
-    
-    return str(identities)
+
+    for identity in identities:    
+        # Draw the rectangle on the image
+        x = identity["facial_area"]["x"]
+        y = identity["facial_area"]["y"]
+        w = identity["facial_area"]["w"]
+        h = identity["facial_area"]["h"]
+        cv2.rectangle(image, (x,y), (x+w,y+h), (0, 0, 255), 2)
+
+        # Define the text position
+        text_position = (x, y+h+30)
+
+        # Add the text to the image
+        cv2.putText(image, identity["name"], text_position, cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255,0 ), 2)
+    return image
 
 
 # image_input = gr.inputs.Image(shape=(160,160))
@@ -118,8 +132,8 @@ interface1 = gr.Interface(
 
 # Create Gradio interfaces for image input and output
 image_input2 = gr.inputs.Image(shape=(None, None))
-# output_image = gr.outputs.Image(type="numpy")
-output_image = gr.outputs.Textbox()
+output_image = gr.outputs.Image(type="numpy")
+# output_image = gr.outputs.Textbox()
 
 # Create the Gradio interface for image input and output
 interface2 = gr.Interface(
@@ -136,9 +150,9 @@ interface2 = gr.Interface(
 # interface.add_view(interface2, "Predict", "Get identity of person")
 
 gr.TabbedInterface(
-    [interface1 , interface2],
+    [interface2 , interface1],
     tab_names=["Predict Persons","Add new Person"]
-).queue().launch(   )
+).queue().launch()
 
 # Launch the Gradio interface
 interface.launch()

requirements.txt

@@ -1,3 +1,4 @@
 numpy
 deepface
-pymongo
+pymongo
+opencv-python