nossy_mask = cv2.imread("assets/Dog_Filter_assets/dog_filter_nose_mask.png")
detect = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
shape = None
x = 0
y = 0
w = 0
h = 0
(le1, le2) = face_utils.FACIAL_LANDMARKS_IDXS["left_eyebrow"]
(re1, re2) = face_utils.FACIAL_LANDMARKS_IDXS["right_eyebrow"]
(n1, n2) = (31, 36)
while True:
output = live()
output = imutils.resize(output, width=500)
gray = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY)
rects = detect(gray, 1)
for (i, rect) in enumerate(rects):
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
(x, y, w, h) = face_utils.rect_to_bb(rect)
left = shape[le1:le2]
right = shape[re1:re2]
nose = shape[n1:n2]
dy = right[3][1] - right[1][1]
dx = right[3][0] - right[1][0]
re_angle = calc_angle(dx, dy)
args = vars(ap.parse_args())
# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
# initialize the video stream and allow the cammera sensor to warmup
print("[INFO] starting video stream...")
# vs = VideoStream(src=0).start()
# time.sleep(2.0)
# loop over the frames from the video stream
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = live()
frame = imutils.resize(frame, width=400)
# grab the frame dimensions and convert it to a blob
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,
(300, 300), (104.0, 177.0, 123.0))
# pass the blob through the network and obtain the detections and
# predictions
net.setInput(blob)
detections = net.forward()
# loop over the detections
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the
from live_cam import live
fourcc = cv2.VideoWriter_fourcc(*'XVID')
res = cv2.VideoWriter('Results/output.avi', fourcc, 20.0, (640, 480))
cv2.namedWindow("Sort", cv2.WINDOW_NORMAL)
# img = cv2.imread("Test/"+sys.argv[1])
def midpoint(ptA, ptB):
return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
while True:
img = live()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
canny = cv2.Canny(gray, 100, 220)
canny = cv2.dilate(canny, None, iterations=1)
canny = cv2.erode(canny, None, iterations=1)
# thresh = cv2.threshold(gray, 110, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(canny.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
out = img.copy()
print(len(cnts))
import time import cv2 import numpy as np import imutils from live_cam import live # time.sleep(2) count = 0 background = 0 for i in range(60): background =live() background = np.flip(background,axis=1) while True: img = live() count+=1 img = np.flip(img,axis=1) hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) lower_red = np.array([0, 125, 50]) upper_red = np.array([10, 255,255]) mask1 = cv2.inRange(hsv, lower_red, upper_red) lower_red = np.array([170, 120, 70]) upper_red = np.array([180, 255, 255]) mask2 = cv2.inRange(hsv, lower_red, upper_red) mask1 = mask1 + mask2 mask1 = cv2.morphologyEx(mask1, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8)) mask1 = cv2.morphologyEx(mask1, cv2.MORPH_DILATE, np.ones((3, 3), np.uint8))