Update app.py

app.py

@@ -1,65 +1,22 @@
+import numpy as np
 import cv2
-import imutils
 import gradio as gr
-import numpy as np
-
-face_detector = cv2.CascadeClassifier("haar_cascades/haarcascade_frontalface_default.xml")
-
-def detect_faces(img, size, neighbours, scale):
-    frame = np.array(img) 
-    frame = frame[:, :, ::-1].copy()
-    frame = imutils.resize(frame, width=500)
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-
-    faceRects = face_detector.detectMultiScale(
-        gray, scaleFactor=scale, minNeighbors=neighbours, minSize=(size, size),
-        flags=cv2.CASCADE_SCALE_IMAGE)
-
-    box_data = []
-
-    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) 
-        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
-        }
-        }
-
-    re_im =cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    return re_im
-    
-image = gr.components.Image()
-out_im = gr.components.Image()
-
-size_slider = gr.components.Slider(minimum=5, maximum=50, value=30, step=5, label="MinSize in Pixel")
-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()
+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=1.1, 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), 2)
+     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.",
+ )
+iface.launch()