Update app.py

app.py

@@ -1,28 +1,22 @@
-import cv2
-import gradio as gr
+import opencv2
 
-# Load the Haar cascade for face detection
-face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+# Load the cascade classifier for face detection
+face_cascade = opencv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
 
 # Load the input image
-img = cv2.imread('input_image.jpg')
+img = opencv2.imread('input_image.jpg')
 
 # Convert the image to grayscale
-gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+gray = opencv2.cvtColor(img, opencv2.COLOR_BGR2GRAY)
 
 # Detect faces in the grayscale image using the Haar cascade
 faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
 
-# Create a Gradio graph to display the input image and the detected faces
-graph = gr.Graph()
-
-# Add the input image to the graph
-graph.add_node(gr.ImageNode(img, 'Input Image'))
-
-# Add the detected faces to the graph
+# Draw rectangles around the detected faces
 for (x, y, w, h) in faces:
-    face_img = gray[y:y+h, x:x+w]
-    graph.add_node(gr.ImageNode(face_img, f'Face {x}, {y}'))
+    opencv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
 
-# Display the graph
-graph.display()
+# Display the output image with the detected faces
+opencv2.imshow('Output', img)
+opencv2.waitKey(0)
+opencv2.destroyAllWindows()