Live visualisation of various facial recognition algorithms.
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

99 lines
3.0 KiB

# import face_recognition
import cv2
from skimage.feature import hog
from skimage import data, exposure
import matplotlib.pyplot as plt
import numpy as np
import dlib
import logging
import time
imagefile = "Marjo.jpg"
prototxt = "dnn/face_detector/opencv_face_detector.pbtxt"
prototxt = "dnn/face_detector/deploy.prototxt"
model = "dnn/face_detector/res10_300x300_ssd_iter_140000_fp16.caffemodel"
confidence_threshold = .0
logger = logging.getLogger('dnn')
image = cv2.imread(imagefile)
# rows = open(args["labels"]).read().strip().split("\n")
# classes = [r[r.find(" ") + 1:].split(",")[0] for r in rows]
logger.info("[INFO] loding model...")
net = cv2.dnn.readNetFromCaffe(prototxt, model)
logger.info("Loaded")
video_capture = cv2.VideoCapture(2)
while True:
# Grab a single frame of video
ret, image = video_capture.read()
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,
(300, 300), (104.0, 177.0, 123.0))
net.setInput(blob)
start = time.time()
detections = net.forward()
end = time.time()
logger.debug(f"classification took {end-start:.5} seconds")
# idxs = np.argsort(detections[0])[::-1][:5]
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the
# prediction
confidence = detections[0, 0, i, 2]
# compute the (x, y)-coordinates of the bounding box for the
# object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# we always draw
# First we crop the sub-rect from the image
sub_img = image[startY:endY, startX:endX]
rect_img = sub_img.copy()
width = 2
cv2.rectangle(rect_img, (0, 0),
(sub_img.shape[1]-int(width/2), sub_img.shape[0]-int(width/2)),
(0, 0, 255), width)
# white_rect = np.ones(sub_img.shape, dtype=np.uint8) * 255
# At least 10% opacity
alpha = max(.1, confidence)
res = cv2.addWeighted(sub_img, 1-alpha, rect_img, alpha, 1.0)
# Putting the image back to its position
image[startY:endY, startX:endX] = res
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > confidence_threshold:
# draw the bounding box of the face along with the associated
# probability
text = "{:.2f}%".format(confidence * 100)
y = startY - 10 if startY - 10 > 10 else startY + 10
# cv2.rectangle(image, (startX, startY), (endX, endY),
# (0, 0, 255), 2)
cv2.putText(image, text, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
# show the output image
cv2.imshow("Output", image)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()