images_count = pixel.value[2]
# Generate sky-region-binary-image
beta1 = 0.75
beta2 = 0.5
resultPic = ImageProcess.getSkyRegionMask(pixel.value[0], pixel.value[1], images_count * beta1, images_count * beta2)
# Erode the sky-region-binary-image and mark three largest (if any) contour area.
kernel = np.ones((5, 5), np.uint8)
mask, cnts_new = ImageProcess.markDisjointSkyRegion(resultPic, kernel)
# If detect more than one disjoint contour area, calculate correlation coefficient of each area.
contour_num = len(cnts_new)
if contour_num > 1:
mask_b = sc.broadcast(mask)
images_coefficient = images_d2.flatMap(lambda x: ImageProcess.get_R_Minus_B_Value(x[1], mask_b.value))
coef_list = images_coefficient.groupByKey().mapValues(list).take(3)
print (coef_list)
ImageProcess.disjointRegionProcess(coef_list, contour_num, mask)
# Get Final Sky Region Mask
final_sky_region_mask = ImageProcess.generateFinalSkyRegion(mask, kernel, contour_num)
cv2.imshow('final_sky_region', final_sky_region_mask)
cv2.waitKey(0)
sky_region_mask = sc.broadcast(final_sky_region_mask)
