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if (!fork()) { | |
cv::imshow("debug", frame_image); | |
cv::waitKey(0); | |
exit(0); | |
} |
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my_tuple = ('p','r','o','g','r','a','m','i','z') | |
print("og forward:") | |
print("og: ",my_tuple[2:4]) | |
print("og: ",my_tuple[-7:4]) | |
print("og: ",my_tuple[-7:-5]) | |
print("og: ",my_tuple[2:-5]) | |
print("og backwards (go):") | |
print("go: ",my_tuple[3:1:-1]) |
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#Method 1, pythonic | |
img_res = np.zeros(image.shape, np.uint8) #create the image | |
img_res[:, :] = (0, 0, 200) #color it red | |
idx = (mask != 0) | |
img_res[idx] = image[idx] | |
#Method 2 OpenCV | |
img_red = np.zeros(image.shape, np.uint8) #create the image | |
img_red[:, :] = (0, 0, 200) #color it red | |
fg = cv2.bitwise_and(image, image, mask=mask) #isolate the segmented region |
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cap = cv2.VideoCapture('/opt/home/garybradski/data/matte_this/shot02/Tornado.mp4') | |
length = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT)) | |
print "len Tornado = ", length | |
cv2.namedWindow('Tornado', cv2.WINDOW_NORMAL) | |
cv2.resizeWindow('Tornado',960,540) | |
# seek | |
assert cap.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, 8000) | |
n = 0 | |
wait_key = 30 | |
while True: |
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import argparse | |
import logging | |
parser.add_argument('--loglevel', help='Logging level: NONE, DEBUG 10, INFO 20, WARNING 30, ERROR 40, CRITICAL 50. Default: OFF 100', type=str, default='WARNING' ) | |
args = parser.parse_args() | |
logging.basicConfig() | |
logger = logging.getLogger() | |
if(args.loglevel == 'OFF'): logger.setLevel(100) | |
elif(args.loglevel == 'DEBUG'): logger.setLevel(logging.DEBUG) | |
elif(args.loglevel == 'INFO'): logger.setLevel(logging.INFO) |
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import cv2 | |
# Read the images | |
foreground = cv2.imread("puppets.png") | |
background = cv2.imread("ocean.png") | |
alpha = cv2.imread("puppets_alpha.png") | |
# Convert uint8 to float | |
foreground = foreground.astype(float) | |
background = background.astype(float) |
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void alphaBlend(Mat& foreground, Mat& background, Mat& alpha, Mat& outImage) | |
{ | |
// Find number of pixels. | |
int numberOfPixels = foreground.rows * foreground.cols * foreground.channels(); | |
// Get floating point pointers to the data matrices | |
float* fptr = reinterpret_cast<float*>(foreground.data); | |
float* bptr = reinterpret_cast<float*>(background.data); | |
float* aptr = reinterpret_cast<float*>(alpha.data); | |
float* outImagePtr = reinterpret_cast<float*>(outImage.data); |
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import cv2 | |
import argparse | |
import numpy as np | |
import os | |
import logging | |
#GLOBAL | |
logging.basicConfig() #Fire up the logger | |
logger = logging.getLogger() |
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import cv2 | |
from matplotlib import pyplot as plt | |
. . . | |
#DO PYTHON MATPLOTLIB IMAGE DISPLAY | |
plt.imshow(image_seg) | |
plt.draw() | |
plt.show() # , plt.draw(), plt.show() | |
plt.pause(0.01) |
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def text_to_floatlist(pathfile): | |
f = open(pathfile, 'r') | |
line = f.readline().strip('[]') | |
f.close() | |
return [float(x) for x in line.split()] | |
#Use | |
rect = text_to_floatlist(seg_bb[frame_count]) | |
if(len(rect) < 4): | |
logger.critical("Length of rectangle not long enough %d",int(len(rect)) ) |
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