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| from __future__ import division | |
| import cv2 | |
| import numpy as np | |
| from skimage import morphology | |
| np.seterr(divide='ignore', invalid='ignore') | |
| """This is the profiled code, very fast, takes 0.25s""" | |
| def fakePad(Image, Mask, iterations=50): | |
| """ | |
| add an extra padding around the front mask | |
| :param Image: | |
| :param Mask: | |
| :param iterations: | |
| :return: DilatedImg | |
| """ | |
| if len(Image.shape) == 3: ##for RGB Image | |
| """for RGB Images""" | |
| Mask0 = Mask.copy() | |
| height, width = Mask0.shape[:2] | |
| Mask0[0, :] = 0 # np.zeros(width) | |
| Mask0[-1, :] = 0 # np.zeros(width) | |
| Mask0[:, 0] = 0 # np.zeros(height) | |
| Mask0[:, -1] = 0 # np.zeros(height) | |
| # Erodes the mask to avoid weird region near the border. | |
| structureElement1 = morphology.disk(5) | |
| Mask0 = cv2.morphologyEx(Mask0, cv2.MORPH_ERODE, structureElement1, iterations=1) | |
| # DilatedImg = Img_green_reverse * Mask | |
| DilatedImg = cv2.bitwise_and(Image, Image, mask=Mask0) | |
| OldMask = Mask0.copy() | |
| filter = np.ones((3, 3)) | |
| filterRows, filterCols = np.where(filter > 0) | |
| filterRows = filterRows - 1 | |
| filterCols = filterCols - 1 | |
| structureElement2 = morphology.diamond(1) | |
| for i in range(0, iterations): | |
| NewMask = cv2.morphologyEx(OldMask, cv2.MORPH_DILATE, structureElement2, iterations=1) | |
| pixelIndex = np.where(NewMask - OldMask) # [rows, cols] | |
| imgValues = np.zeros((len(pixelIndex[0]), len(filterRows), 3)) | |
| for k in range(len(filterRows)): | |
| filterRowIndexes = pixelIndex[0] - filterRows[k] | |
| filterColIndexes = pixelIndex[1] - filterCols[k] | |
| selectMask0 = np.bitwise_and(np.bitwise_and(filterRowIndexes < height, filterRowIndexes >= 0), | |
| np.bitwise_and(filterColIndexes < width, filterColIndexes >= 0)) | |
| selectMask1 = OldMask[filterRowIndexes[selectMask0], filterColIndexes[selectMask0]] > 0 | |
| selectedPositions = [filterRowIndexes[selectMask0][selectMask1], | |
| filterColIndexes[selectMask0][selectMask1]] | |
| imgValues[np.arange(len(pixelIndex[0]))[selectMask0][selectMask1], k, :] = DilatedImg[ | |
| selectedPositions[0], | |
| selectedPositions[1], :] | |
| DilatedImg[pixelIndex[0], pixelIndex[1], :] = np.sum(imgValues, axis=1) // np.sum(imgValues > 0, axis=1) | |
| OldMask = NewMask | |
| return DilatedImg | |
| ######################################################################## | |
| else: #for green channel only | |
| """for green channel only""" | |
| Mask0 = Mask.copy() | |
| height, width = Mask0.shape | |
| Mask0[0, :] = 0 # np.zeros(width) | |
| Mask0[-1, :] = 0 # np.zeros(width) | |
| Mask0[:, 0] = 0 # np.zeros(height) | |
| Mask0[:, -1] = 0 # np.zeros(height) | |
| # Erodes the mask to avoid weird region near the border. | |
| structureElement1 = morphology.disk(5) | |
| Mask0 = cv2.morphologyEx(Mask0, cv2.MORPH_ERODE, structureElement1, iterations=1) | |
| # DilatedImg = Img_green_reverse * Mask | |
| DilatedImg = cv2.bitwise_and(Image, Image, mask=Mask0) | |
| OldMask = Mask0.copy() | |
| filter = np.ones((3, 3)) | |
| filterRows, filterCols = np.where(filter > 0) | |
| filterRows = filterRows - 1 | |
| filterCols = filterCols - 1 | |
| structureElement2 = morphology.diamond(1) | |
| for i in range(0, iterations): | |
| NewMask = cv2.morphologyEx(OldMask, cv2.MORPH_DILATE, structureElement2, iterations=1) | |
| pixelIndex = np.where(NewMask - OldMask) # [rows, cols] | |
| imgValues = np.zeros((len(pixelIndex[0]), len(filterRows))) | |
| for k in range(len(filterRows)): | |
| filterRowIndexes = pixelIndex[0] - filterRows[k] | |
| filterColIndexes = pixelIndex[1] - filterCols[k] | |
| selectMask0 = np.bitwise_and(np.bitwise_and(filterRowIndexes < height, filterRowIndexes >= 0), | |
| np.bitwise_and(filterColIndexes < width, filterColIndexes >= 0)) | |
| selectMask1 = OldMask[filterRowIndexes[selectMask0], filterColIndexes[selectMask0]] > 0 | |
| selectedPositions = [filterRowIndexes[selectMask0][selectMask1], filterColIndexes[selectMask0][selectMask1]] | |
| imgValues[np.arange(len(pixelIndex[0]))[selectMask0][selectMask1], k] = DilatedImg[selectedPositions[0], selectedPositions[1]] | |
| DilatedImg[pixelIndex[0], pixelIndex[1]] = np.sum(imgValues, axis=1) / np.sum(imgValues > 0, axis=1) | |
| OldMask = NewMask | |
| return DilatedImg | |