Update app.py

app.py

@@ -1,15 +1,30 @@
-import numpy as np
-import cv2
 import gradio as gr
+import numpy as np 
+import cv2
 from PIL import Image
-def detect_faces(image):
- image_np= np.array(image)
- gray_image= cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
- face_cascade= cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
- faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.74, minNeighbors=5, minSize=(30, 30))
- for (x, y, w, h) in faces:
-     cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 9)
- return image_np
-
-iface= gr.Interface(fn=detect_faces,inputs="image",outputs="image",title="Face Detection",description="Upload an image, and the model will detect faces and draw bounding boxes around them.",)
+
+def detect_faces(image , slider ) : 
+    # detect faces 
+    # convert image in to numpy array
+    image_np = np.array(image)
+    # convert image into gray 
+    gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
+    # use detectmultiscale function to detect faces using haar cascade 
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +     "haarcascade_frontalface_default.xml")
+    faces = face_cascade.detectMultiScale(gray_image,   scaleFactor=slider, minNeighbors=5, minSize=(30, 30))
+    # draw rectangle along detected faces
+    for (x, y, w, h) in faces:
+        cv2.rectangle(image_np, (x, y), (x+w, y+h), (255, 0, 0), 5)        
+    
+    return image_np , len(faces)
+
+# slider = gr.Slider(minimum=1, maximum=2, step=.1, label="Adjust the ScaleFactor")
+
+iface = gr.Interface(  fn=detect_faces,
+    inputs=["image",gr.Slider(minimum=1, maximum=2, step=.1, label="Adjust the ScaleFactor")],
+    outputs=["image", gr.Label("faces count ")] ,
+    title="Face Detection",
+    description="Upload an image,and the model will detect faces and draw bounding boxes around them.",
+    )
+
 iface.launch()