def produce_frames(frames):
bg = np.array(Image.open('bg.jpg'))
bg = cv2.cvtColor(bg, cv2.COLOR_BGR2RGB)
bg_s = np.array(Image.open('bg_s.jpg'))
bg_s = cv2.cvtColor(bg_s, cv2.COLOR_BGR2RGB)
frames_generated = []
for frame in frames:
res1 = cv2.bitwise_and(frame, bg)
res2 = cv2.bitwise_and(cv2.bitwise_not(frame), bg_s)
res = cv2.bitwise_xor(res1, res2)
frames_generated.append(res)
return frames_generated
import numpy as np
from cv2 import cv2
#Create 300*300 numpy array and 250*250 white rectangle inside it
rectangle = np.zeros((300, 300), dtype="uint8")
cv2.rectangle(rectangle, (25, 25), (275, 275), 255, -1)
cv2.imshow("rectangle", rectangle)
#Create 300*300 numpy array and 150 radius white circle inside it
circle = np.zeros((300, 300), dtype="uint8")
cv2.circle(circle, (150, 150), 150, 255, -1)
cv2.imshow("circle", circle)
# performing the bitwise oprations
bitwiseAnd = cv2.bitwise_and(rectangle, circle)
cv2.imshow("AND", bitwiseAnd)
cv2.waitKey(0)
bitwiseOr = cv2.bitwise_or(rectangle, circle)
cv2.imshow("OR", bitwiseOr)
cv2.waitKey(0)
bitwiseXor = cv2.bitwise_xor(rectangle, circle)
cv2.imshow("XOR", bitwiseXor)
cv2.waitKey(0)
bitwiseNot = cv2.bitwise_not(rectangle, circle)
cv2.imshow("Not", bitwiseNot)
cv2.waitKey(0)
from cv2 import cv2
import numpy as np
img1 = cv2.imread("bib.png")
img2 = cv2.imread("ml.png")
bit_and = cv2.bitwise_and(img2, img1)
bit_or = cv2.bitwise_or(img2, img1)
bit_xor = cv2.bitwise_xor(img1, img2)
bit_not = cv2.bitwise_not(img1)
bit_not2 = cv2.bitwise_not(img2)
cv2.imshow("img1", img1)
cv2.imshow("img2", img2)
cv2.imshow("bit_and", bit_and)
cv2.imshow("bit_or", bit_or)
cv2.imshow("bit_xor", bit_xor)
cv2.imshow("bit_not", bit_not)
cv2.imshow("bit_not2", bit_not2)
cv2.waitKey(0)
from cv2 import cv2 as cv
import numpy as np
blank = np.zeros((400, 400), dtype='uint8')
rectangle = cv.rectangle(blank.copy(), (30, 30), (370, 370), 255, -1)
circle = cv.circle(blank.copy(), (200, 200), 200, 255, -1)
cv.imshow('Rectangle', rectangle)
cv.imshow('Circle', circle)
#bitwise AND. Exactly what you imagine
bitwiseAND = cv.bitwise_and(rectangle, circle)
cv.imshow('bitWiseAND', bitwiseAND)
#bitwise OR
bitwiseOR = cv.bitwise_or(rectangle, circle)
cv.imshow('bitWiseOR', bitwiseOR)
#bitwise XOR
bitwiseXOR = cv.bitwise_xor(rectangle, circle)
cv.imshow('bitwiseXOR', bitwiseXOR)
#bitwise NOT
bitwiseNOT = cv.bitwise_not(bitwiseXOR)
cv.imshow('bitwisenot', bitwiseNOT)
cv.waitKey(0)
from cv2 import cv2
import numpy as np
img = np.zeros((460, 819, 3), np.uint8)
#print(img.shape)
cv2.rectangle(img, (400, 0), (500, 230), (255, 255, 255), -1)
img2 = cv2.imread('img1.png')
#print(img2.shape)
#bitwise operations
bitand = cv2.bitwise_and(img, img2)
bitnot = cv2.bitwise_not(img)
bitor = cv2.bitwise_or(img, img2)
bitxor = cv2.bitwise_xor(img, img2)
cv2.imshow('image', img)
cv2.imshow('image2', img2)
cv2.imshow('bitand', bitand)
cv2.imshow('bitnot', bitnot)
cv2.imshow('bitor', bitor)
cv2.imshow('bitxor', bitxor)
cv2.waitKey(0)
cv2.destroyAllWindows()
**lk_params)
# Select good points
if p1[st == 1] is not None:
good_new = p1[st == 1]
if p0[st == 1] is not None:
good_old = p0[st == 1]
# draw the tracks
for i, (new, old) in enumerate(zip(good_new, good_old)):
a, b = new.ravel()
c, d = old.ravel()
# mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
frame = cv2.circle(frame, (a, b), 5, color[i].tolist(), -1)
# img = cv2.add(frame,mask)
img1 = frame
cur_diff = cv2.bitwise_xor(frame_gray, old_gray)
# diff = diff * number_mask
# frame = cv2.circle(frame,(diff.mean(axis=1), diff.mean(axis=0)),8,0,-1)
# ret, diff = cv2.threshold(diff, 100, 255, cv2.THRESH_BINARY) ###이진화
# contours, image = cv2.findContours(diff,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
if cur_diff.any():
p1, st, err = cv2.calcOpticalFlowPyrLK(old_diff, cur_diff, p0, None,
**lk_params)
# Select good points
if p1[st == 1] is not None:
good_new = p1[st == 1]
if p0[st == 1] is not None:
good_old = p0[st == 1]
# draw the tracks
for i, (new, old) in enumerate(zip(good_new, good_old)):
a, b = new.ravel()
from cv2 import cv2
import numpy as np
img1 = cv2.imread("./sources/bitwise-1.png")
img2 = cv2.imread("./sources/bitwise-2.png")
bit_and = cv2.bitwise_and(img2,img1)
bit_or = cv2.bitwise_or(img2,img1)
bit_xor = cv2.bitwise_xor(img1,img2)
bit_xor2 = cv2.bitwise_xor(img2,img1)
cv2.imshow("birlesim",bit_and)
cv2.imshow("birlesim or",bit_or)
cv2.imshow("birlesim xor",bit_xor)
cv2.imshow("birlesim xor2",bit_xor2)
cv2.waitKey(0)
cv2.rectangle(square, (50, 50), (250, 250), 255, -2)
cv2.imshow("Square", square)
cv2.waitKey()
# Making a ellipse
ellipse = np.zeros((300, 300), np.uint8)
cv2.ellipse(ellipse, (150, 150), (150, 150), 30, 0, 180, 255, -1)
cv2.imshow("Ellipse", ellipse)
cv2.waitKey()
cv2.destroyAllWindows()
# Shows only were they intersect
And = cv2.bitwise_and(square, ellipse)
cv2.imshow("AND", And)
cv2.waitKey()
# Shows where either square or ellipse is
bitwiseOr = cv2.bitwise_or(square, ellipse)
cv2.imshow("OR", bitwiseOr)
# Shows where either exist by itself
bitwiseXor = cv2.bitwise_xor(square, ellipse)
cv2.imshow("XOR", bitwiseXor)
cv2.waitKey()
# Shows everything that isn't part of the square
bitwiseNot_sq = cv2.bitwise_not(square)
cv2.imshow("NOT - square", bitwiseNot_sq)
cv2.waitKey()
def find_text(image):
"""attempt to isolate the text in the image.
:param image: input image
:type image: cv2 image
:return: contours, cropped image, if text is found
:rtype: cv2 cnts, cv2 image, boolean
"""
# convert to GRAYSCALE
g_label = convert.bgr_to_gray(image)
# binarise the image
_, thresh = cv2.threshold(g_label, 127, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# detect edges
canny = features.apply_canny(thresh, 175, 190)
# blur
blur = filters.median_blur(canny, 3)
# xor between canny and blur to attempt to remove some fo the lines from the diamond
text_im = cv2.bitwise_xor(canny, blur)
# blur again
text_blur = filters.apply_gaussian_blur(text_im, 3)
# attemtp to dilate the text so it is one big blob
kernel_rect = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 1))
dilated = cv2.dilate(text_blur, kernel_rect, iterations=6)
# attempt to remove anythin gthat is not veritcal or horizontal i.e. lines form the diamond
kernel_cross = cv2.getStructuringElement(cv2.MORPH_CROSS, (7, 7))
erosion = cv2.erode(dilated, kernel_cross, iterations=2)
# threshold the image again
_, thresh_dilated = cv2.threshold(erosion, 127, 255, cv2.THRESH_BINARY)
# blur again
td_blur = filters.median_blur(thresh_dilated, 9)
# dilate the text into itself
kernel_rect2 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 2))
td_dilate = cv2.dilate(td_blur, kernel_rect2, iterations=10)
for _ in range(12):
td_dilate = filters.apply_gaussian_blur(td_dilate, 5)
td_dilate = cv2.dilate(td_blur, kernel_rect2, iterations=10)
kernel_rect3 = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 8))
td_dilate = cv2.dilate(td_dilate, kernel_rect3, iterations=3)
# find contours that encompass the areas text could be
_, cnts, _ = cv2.findContours(td_dilate.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# report whether contours weere foun
if cnts:
found = True
# optimise the contours found to only include the important ones
bbox = find_optimal_components_subset(cnts, td_dilate)
# crop to the optimal height, keep the entire width
im = utils.crop_to_bbox(image, bbox, ignore='x')
else:
found = False
im = image
return im, found
