import cv2
import time
from yolo.detector import YOLO
from fingertip import Fingertips
hand = YOLO(weights='weights/yolo.h5', threshold=0.5)
fingertip = Fingertips(model='vgg', weights='weights/vgg16.h5')
cam = cv2.VideoCapture(0)
while True:
tic = time.time()
ret, image = cam.read()
if ret is False:
print('camera not found! :( ')
break
tl, br = hand.detect(image)
if tl or br is not None:
xmin, ymin, xmax, ymax = int(tl[0]), int(tl[1]), int(br[0]), int(br[1])
cropped_image = image[ymin:ymax, xmin:xmax]
cols, rows, _ = cropped_image.shape # heights, width, channel
resized_image = cv2.resize(cropped_image, (128, 128))
position = fingertip.classify(image=resized_image)
for i in range(0, len(position), 2):
position[i] = (position[i]) * rows
position[i + 1] = (position[i + 1]) * cols
for i in range(0, len(position), 2):
import cv2
import numpy as np
from yolo.detector import YOLO
from compare.net.dtfd import dtfd
hand = YOLO('../../weights/yolo.h5', threshold=0.3)
fingertip = dtfd()
fingertip.load_weights('../../compare/weights/dtfd.h5')
def classify(img):
""" Fingertip detection """
global fingertip
img = img / 255.0
img = np.expand_dims(img, axis=0)
keys = fingertip.predict(img)
keys = keys[0]
return keys
user = 2
transformation = 'translation'
f_count = 0
cam = cv2.VideoCapture('../../data/' + transformation + '/' + str(user) + '.mp4')
f = open('../../data/' + transformation + '/' + transformation + '_' + str(user) + '.txt', 'r')
lines = f.readlines()
f.close()
xt = []
yt = []
import os
import cv2
import time
import numpy as np
import imgaug as ia
from imgaug import augmenters as iaa
from yolo.detector import YOLO
from compare.net.dtfd import dtfd
hand = YOLO(weights='../weights/yolo.h5', threshold=0.5)
fingertip = dtfd()
fingertip.load_weights('weights/dtfd.h5')
def classify(img):
""" Fingertip detection """
global fingertip
img = img / 255.0
img = np.expand_dims(img, axis=0)
keys = fingertip.predict(img)
keys = keys[0]
return keys
def flip_horizontal(img, keys):
""" Flipping """
aug = iaa.Sequential([iaa.Fliplr(1.0)])
seq_det = aug.to_deterministic()
keys = ia.KeypointsOnImage([
ia.Keypoint(x=keys[0], y=keys[1]),
ia.Keypoint(x=keys[2], y=keys[3]),