Update app.py

app.py

@@ -1,46 +1,60 @@
-import cv2
-import imutils
 import gradio as gr
+
+
 import numpy as np
 
-face_detector = cv2.CascadeClassifier("haar_cascades/haarcascade_frontalface_default.xml")
+import cv2
+
+from PIL import Image
+
+
+
+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
+
+
 
-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)
+slider = gr.Slider(minimum=1, maximum=2, step=.1, label="Adjust the ScaleFactor")
 
-    faceRects = face_detector.detectMultiScale(gray, scaleFactor=scale, minNeighbors=neighbours, minSize=(size, size),flags=cv2.CASCADE_SCALE_IMAGE)
 
-    box_data = []
 
-    class_labels = {
-        0: "face"
-    }
+iface = gr.Interface( fn=detect_faces,
 
-    for (x,y,w,h) in faceRects:
-        frame = cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
+inputs=["image","slider"],
 
-        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)
+outputs="image",
 
-    predictions = {"predictions": {"box_data": box_data,"class_labels": class_labels } }
+title="Face Detection using Haar Cascade Classifier ",
 
-    re_im =cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    return re_im
-    
-image = gr.components.Image()
-out_im = gr.components.Image()
+description="Upload an image,and the model will detect faces and draw bounding boxes around them.",
 
-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,title="Haar Cascade Object Detection",).launch()
-  
+iface.launch()