scrap.md

import os import cv2 import funct.label_image.label_image as li import funct.opt_flow.opt_flow as of import tensorflow as tf import glob import image_slicer import numpy as np

print(glob.glob("./in/*.png"))

files_glob = glob.glob("./in/*.png")

n = 0

while n < len(files_glob) - 1:

image = cv2.imread(files_glob[n+1])

img_prev = cv2.imread(files_glob[n])

result = of.optical_flow(image, img_prev)

cv2.imwrite('./opt_flow_out/' + str(n)+'.png',result)

n = n + 1

for file in files_glob: image_cv2 = cv2.imread(file) # image_slicer.slice(file, 28) image = tf.read_file(file) label = li.label_image(image) # if(label == "road"): # print("hello") img = np.zeros(image.shape,dtype=np.uint8) img.fill(255) # or img[:] = 255 cv2.imwrite(file,img) else: cv2.imwrite(file,np.ones(image.shape,dtype=np.uint8)) print(file)

for file in files_glob:

image = cv2.imread("in/B.png")

img_prev = cv2.imread("in/A.png")

directory = os.fsencode("in")

for file in os.listdir(directory):

filename = os.fsdecode(file)

if filename.endswith(".png"):

path = "in/" + filename

# print(path)

# image = tf.read_file(path, "frame")

image = cv2.imread("in/B.png")

img_prev = cv2.imread("in/A.png")

# print(image)

# li.label_image(image)

result = of.optical_flow(image, img_prev)

cv2.imwrite('messigray.png',result)

# print(result)

# cv2.imshow('frame',image)