'''======================Sky Region Detection Part======================'''
# Get new RDD: Sky Region for each image
images_sky_region = images_d2.map(lambda x: ImageProcess.checkSkyRegion(x[1]))
# Combine sky_region_color and sky_region_edge
skyRegionPixelCount = (np.zeros((HEIGHT, WIDTH), np.uint8), np.zeros((HEIGHT, WIDTH), np.uint8), 0)
pixel = sc.accumulator(skyRegionPixelCount, MatrixAccumulatorParam())
images_sky_region.foreach(lambda x: pixel.add(x))
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)
