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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() |