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import cv2
import numpy as np
class CaesarFaceDetection:
def __init__(self) -> None:
# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
prototxt_path = "CaesarFaceDetection/weights/deploy.prototxt.txt"
# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel
model_path = "CaesarFaceDetection/weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"
# load Caffe model
self.model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)
def detect_face(self,image, showtext=False,snapcropface=False):
h, w = image.shape[:2]
# preprocess the image: resize and performs mean subtraction
blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))
# set the image into the input of the neural network
self.model.setInput(blob)
# perform inference and get the result
output = np.squeeze(self.model.forward())
font_scale = 1.0
for i in range(0, output.shape[0]):
# get the confidence
confidence = output[i, 2]
# if confidence is above 50%, then draw the surrounding box
if confidence > 0.5:
# get the surrounding box cordinates and upscale them to original image
box = output[i, 3:7] * np.array([w, h, w, h])
# convert to integers
start_x, start_y, end_x, end_y = box.astype(np.int)
# draw the rectangle surrounding the face
start_point = (start_x, start_y)
end_point = (end_x, end_y)
if snapcropface == True:
factor_add = 20
crop_img = image[start_y- factor_add:end_y+ factor_add, start_x- factor_add:end_x + factor_add]
return crop_img
#cv2.imshow("cropped", crop_img)
#cv2.waitKey(0)
cv2.rectangle(image,start_point,end_point, color=(255, 0, 0), thickness=2)
# draw text as well
if showtext == True:
cv2.putText(image, f"{confidence*100:.2f}%", (start_x, start_y-5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 0, 0), 2)
if snapcropface != True:
return image
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