def threshold(img, gauss_kernel=11, gammaFactor=0.45, threshold = 50):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
k = gauss_kernel
gray_filtred = gamma.correction(gray,gammaFactor)
gray_filtred = cv2.GaussianBlur(gray_filtred, (k, k), 0)
edge_filtred = cv2.threshold(gray_filtred,threshold,maxval=1,type=cv2.THRESH_BINARY_INV)[1]
vis_filtred = img.copy()
vis_filtred[edge_filtred != 0] = (0, 255, 0)
return edge_filtred,vis_filtred
def adaptiveThreshold(img, gauss_kernel=11, gammaFactor=0.45, C = -3):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
k = gauss_kernel
gray_filtred = gamma.correction(gray,gammaFactor)
gray_filtred = cv2.GaussianBlur(gray_filtred, (k, k), 0)
Cval = C
edge_filtred = cv2.adaptiveThreshold(gray_filtred,maxValue=1,adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C,thresholdType=cv2.THRESH_BINARY,blockSize=11,C=Cval)
vis_filtred = img.copy()
vis_filtred[edge_filtred != 0] = (0, 255, 0)
return edge_filtred,vis_filtred
def canny(img, gauss_kernel=11, gammaFactor=0.45):
"""
:param img:
"""
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
k = gauss_kernel
gray_filtred = gamma.correction(gray,gammaFactor)
gray_filtred = cv2.GaussianBlur(gray_filtred, (k, k), 0)
thrs1 = 20
kernel = 9
ratio = 3
edge_filtred = cv2.Canny(gray_filtred, thrs1, thrs1 * ratio, kernel)
# zaznaczenie wyniku wyznaczania krawedzi
vis_filtred = img.copy()
# vis_filtred /= 2
vis_filtred[edge_filtred != 0] = (0, 255, 0)
return edge_filtred,vis_filtred
