Update app.py

app.py

@@ -3,7 +3,8 @@ import imutils
 import gradio as gr
 import numpy as np
 
-face_detector = cv2.CascadeClassifier("haar_cascades/haarcascade_frontalface_default.xml")
+# ✅ FIXED LINE
+face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 
 def detect_faces(img, size, neighbours, scale):
     frame = np.array(img) 
@@ -12,39 +13,26 @@ def detect_faces(img, size, neighbours, scale):
     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
     faceRects = face_detector.detectMultiScale(
-        gray, scaleFactor=1.1, minNeighbors=5)
+        gray, scaleFactor=scale, minNeighbors=int(neighbours), minSize=(int(size), int(size))
+    )
 
     box_data = []
+    class_labels = {0: "face"}
 
-    class_labels = {
-        0: "face"
-    }
-
-    for (x,y,w,h) in faceRects:
-        frame = cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
-
-        midX = int(x+w/2)
-        midY = int(y+h/2) 
+    for (x, y, w, h) in faceRects:
+        frame = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
+        midX = int(x + w / 2)
+        midY = int(y + h / 2)
         box = {
-                    "position": {
-                        "middle": [midX, midY],
-                        "width": float(w),
-                        "height": float(h)
-                    },
-                    "domain" : "pixel",
-                    "class_id" : 0
-                }
-        box_data.append(box)
-
-    predictions = {"predictions": {
-            "box_data": box_data,
-            "class_labels": class_labels
-        }
+            "position": {"middle": [midX, midY], "width": float(w), "height": float(h)},
+            "domain": "pixel",
+            "class_id": 0
         }
+        box_data.append(box)
 
-    re_im =cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    re_im = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
     return re_im
-    
+
 image = gr.components.Image()
 out_im = gr.components.Image()
 
@@ -52,13 +40,10 @@ size_slider = gr.components.Slider(minimum=5, maximum=50, value=30, step=5, labe
 neighbour_slider = gr.components.Slider(minimum=1, maximum=20, value=5, step=1, label="Min Number of Neighbours")
 scale_slider = gr.components.Slider(minimum=1.1, maximum=2.0, value=1.3, step=0.1, label="Scale Factor")
 
-description = """Face Detection with Haar Cascades using OpenCV"""
-
-
 Iface = gr.Interface(
     fn=detect_faces,
     inputs=[image, size_slider, neighbour_slider, scale_slider],
     outputs=out_im,
-    #examples=[["data/9_Press_Conference_Press_Conference_9_86.jpg"], ["data/12_Group_Group_12_Group_Group_12_39.jpg"], ["data/31_Waiter_Waitress_Waiter_Waitress_31_55.jpg"]],
     title="Haar Cascade Object Detection",
-).launch()
+    description="Face Detection with Haar Cascades using OpenCV"
+).launch()