def main_ocean_wave():
if os.path.exists(outfile + "_ocean_wave_mask.png"):
return
imgsrc = kalgorithm.imgRead(repairfile).astype(np.float32)
sky = kalgorithm.imgRead(skyfile).astype(np.float32)
masksrc = kalgorithm.bgr2gray(kalgorithm.imgRead(repairmaskfile))
vline = findvline()
fisher_girl_mask = 255 - kalgorithm.bgr2gray(
kalgorithm.imgRead(outfile + "_fisher_girl_mask.png"))
# 背景减去 渔女，剩下 海平面。
outv = masksrc - masksrc * (fisher_girl_mask / 255.0)
outv[:vline, ...] = 0
kalgorithm.imgSave(outfile + "_ocean_wave_mask.png", outv)
outv[:vline, ...] = 255
outv[vline:, ...] = 0
outv = outv.astype(np.float32)
outv[:vline,
...] = outv[:vline, ...] * (1 -
(fisher_girl_mask[:vline, ...] / 255.0))
outv = np.clip(outv, 0, 255)
kalgorithm.imgSave(outfile + "_sky_mask.png", outv)
def main_replace_sky():
mask_fishergirl = outfile + "_fisher_girl_mask.png"
mask_sea = outfile + "_ocean_wave_mask.png"
mask_sky = outfile + "_sky_mask.png"
imgsrc = kalgorithm.imgRead(repairfile).astype(np.float32)
sky = kalgorithm.imgRead(skyfile).astype(np.float32)
mask_sky = kalgorithm.imgRead(mask_sky).astype(np.float32)
mask_sky = kalgorithm.meanFilter(mask_sky) # 均值滤波，接头处就不生硬了。
sky = kalgorithm.blInterpolate(sky, 0.33333333334, 0.33333333334)
print("imgsrc", imgsrc.shape, "sky",
sky.shape) # (744, 1100, 3) (618, 1100, 3)
print("mask_sky", mask_sky.shape) # (744, 1100, 3)
newsky = np.zeros((mask_sky.shape[0], mask_sky.shape[1], sky.shape[2]),
np.uint8)
newsky[:, :] = 0
newsky[:sky.shape[0], :sky.shape[1]] = sky
print("newsky", newsky.shape) # 搞成一样大，才可以做乘法，合成图片。
mask_sky = mask_sky / 255
print(newsky.shape, mask_sky.shape, imgsrc.shape)
finalimage = newsky * mask_sky + imgsrc * (1 - mask_sky)
kalgorithm.imgSave(outfile + "_sky_cloud.png", finalimage)
def main():
inputfile = r"./input_images/phase2/phase2_broken.jpg"
imgsrc = kalgorithm.imgRead(inputfile)
outputfile = r"./output_images/phase2/phase2_broken_sharpen_spatial.jpg"
img = kalgorithm.sharpenImage3d(imgsrc.copy())
kalgorithm.imgSave(outputfile, img)
def main_fisher_girl_mask():
if os.path.exists(outfile + "_fisher_girl_mask.png"):
return
imgsrc = kalgorithm.imgRead(repairfile).astype(np.float32)
sky = kalgorithm.imgRead(skyfile).astype(np.float32)
# 使用色彩追踪和形态学运算得到图像中感兴趣区域
# RGB > HSV
mask = BGR2HSV(imgsrc)
# color tracking
mask = get_mask(mask)
# masking
out = masking(imgsrc, mask) # 把太黑的一起识别出来，认为是陆地。主要识别小岛。
out = kalgorithm.bgr2gray(out)
mask = kalgorithm.thresholdOtsuBinarization(out).astype(np.float32) / 255
# closing，移除部分毛刺
mask = Morphology_Closing(mask, time=1) # 更多白区域，移除小黑点。
# opening，域女再变肥一点。
mask = Erode(mask, erodeTime=1)
# masking
out = masking(imgsrc, mask)
#display(out)
kalgorithm.imgSave(outfile + "_fisher_girl.png", out) # 把海岛准确识别出来了。
kalgorithm.imgSave(outfile + "_fisher_girl_mask.png", mask * 255)
def encode(srcInputFile, waterMarkFile, dstOutputFile):
srcimg = kalgorithm.imgRead(srcInputFile)
watermark = blInterpolate(kalgorithm.imgRead(waterMarkFile), 2, 2)
print(watermark.shape)
watermark = bgr2gray(watermark).astype(np.float)
watermark = 255 - watermark
watermark = thresholdOtsuBinarization(watermark)
#kalgorithm.imgShow(watermark)
#kalgorithm.imgShow(srcimg)
WH, WW = watermark.shape
IMGH, IMGW, IMGC = srcimg.shape
srcimg = srcimg // 4 * 4
for y in range(WH):
for x in range(WW):
#print(srcimg[y, x])
if watermark[y, x]:
srcimg[y, x, 0] = srcimg[y, x, 0] | 0x2
srcimg[y, x, 1] = srcimg[y, x, 1] | 0x2
srcimg[y, x, 2] = srcimg[y, x, 2] | 0x2
kalgorithm.imgSave(dstOutputFile, srcimg)
cv2.imwrite(dstOutputFile, srcimg, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
kalgorithm.imgShow(srcimg)
return
def main():
inputfile = r"./input_images/phase2/phase2_broken.jpg"
outputfile = r"./output_images/phase2/phase2_sharpen_frequency_gaussian.jpg"
img = cv2.imread(inputfile)
newimg = kalgorithm.gaussianHighFrequencyFilterImage(img)
# https://blog.csdn.net/qq_30815237/article/details/98655630
#newimg = kalgorithm.histEqualization(newimg) # 高频滤波后一般都要直方图均衡一下
kalgorithm.imgSave(outputfile, newimg)
kalgorithm.imgShow(newimg)
def main():
inputfile = r"./input_images/phase2/phase2_broken.jpg"
imgsrc = kalgorithm.imgRead(inputfile)
kalgorithm.pltHistAndImage(imgsrc.astype(np.uint8), "phase2_broken.Original")
img = kalgorithm.histEqualization(imgsrc.copy())
outfile = r"./output_images/phase2/phase2_broken_hist_equalization.jpg"
kalgorithm.imgSave(outfile, img)
kalgorithm.pltHistAndImage(img, "phase2_broken.histEqualization")
img = kalgorithm.histManipulation(imgsrc.copy())
outfile = r"./output_images/phase2/phase2_broken_hist_manipulation.jpg"
kalgorithm.imgSave(outfile, img)
kalgorithm.pltHistAndImage(img, "phase2_broken.histManipulation")
def mainfixfile():
srcfile = "./output_images/phase2/phase2_broken_nn.jpg.png"
dstfile = "./output_images/phase3/phase3_repair_original.png"
imgsrc = None
if not os.path.exists(dstfile):
img = kalgorithm.imgRead(srcfile)
H, W, C = img.shape
img = kalgorithm.nnInterpolateRound(img, int(H / 3), int(W / 3))
# 一上来就修复图片
# kalgorithm.imgSave(dstfile, img)
from phase2_broken_repair import mainfix
imgsrc = mainfix(img, dstfile, 240, onlyeasy=True)
else:
imgsrc = kalgorithm.imgRead(dstfile).astype(np.float32)
if not os.path.exists(dstfile + ".mask.png"):
out = calculateMask(imgsrc)
kalgorithm.imgSave(dstfile + ".mask.png", out)
# 分离出水平线
if not os.path.exists(repairmaskfile):
out = kalgorithm.imgRead(dstfile + ".mask.png").astype(np.float32)
out = kalgorithm.bgr2gray(out)
out = morphologyErodeLine(out, 1, linelen=40)
out = morphologyDilateLine(out, 1, linelen=40)
kalgorithm.imgSave(dstfile + ".mask.line.png", out)
# 根据水平线，矫正原图。
angle = findAngle(out) # 找到偏移角度。
print("angle", angle)
imgsrc = kalgorithm.imgRead(dstfile).astype(np.float32)
imgsrc = kalgorithm.affineRotation(imgsrc, angle)
# 修复边缘。
while maskfill(imgsrc):
pass
kalgorithm.imgSave(repairfile, imgsrc)
out = calculateMask(imgsrc)
kalgorithm.imgSave(repairmaskfile, out)
## 计算海平面的那条线。准确分离。
out = morphologyErodeLine(out, 1, linelen=40)
out = morphologyDilateLine(out, 3, linelen=80)
kalgorithm.imgSave(repairmaskfile + ".mask.line.png", out)
def decode(dstOutputFile, recoverWaterMark):
srcimg = kalgorithm.imgRead(dstOutputFile)
#srcimg = srcimg % 4 / 3 * 255
IMGH, IMGW, IMGC = srcimg.shape
for y in range(IMGH):
for x in range(IMGW):
a, b, c = srcimg[y, x, 0], srcimg[y, x, 1], srcimg[y, x, 2]
if (a % 4 in (2, 3)) and (b % 4 in (2, 3)) and (c % 4 in (2, 3)):
srcimg[y, x] = 255
else:
srcimg[y, x] = 0
#srcimg = blInterpolate(srcimg, 0.2, 0.2)
kalgorithm.imgShow(srcimg)
kalgorithm.imgSave(recoverWaterMark, srcimg)
return
def phase1_optional(inputImgPath: str, outImgPath: str):
img = kalgorithm.imgRead(inputImgPath)
img = 255 - img
img = cropImage(img) # 剪裁
kalgorithm.imgSave(outImgPath + ".1.jpg", img)
img = rotateImage(img) # 旋转
kalgorithm.imgSave(outImgPath + ".2.jpg", img)
img = cropImage(img) # 再剪裁
kalgorithm.imgSave(outImgPath, img)
def mainfix(imgsrc, outputfile, threshold=250, onlyeasy=False, savemask=False):
imggray = kalgorithm.bgr2gray(imgsrc).astype(np.uint8)
imggray = kalgorithm.thresholdBinarization(imggray, threshold)
#kalgorithm.imgShow(imggray)
if savemask:
kalgorithm.imgSave(outputfile + ".mask.png", imggray)
if not onlyeasy:
mask = imggray.copy().astype(bool, copy=False)
outimg = imgsrc.copy()
pyheal.inpaint(outimg, mask, 5)
kalgorithm.imgSave(outputfile + ".heal.png", outimg)
# 根据 mask 对原图进行临近填充
maskfill(imggray, imgsrc)
kalgorithm.imgSave(outputfile, imgsrc)
return imgsrc
def main():
inputfile = r"./input_images/phase2/phase2_broken.jpg"
imgsrc = kalgorithm.imgRead(inputfile)
# 均值滤波器
meanFilterOutput = r"./output_images/phase2/phase2_broken_mean_filter.jpg"
img = kalgorithm.meanFilter(imgsrc.copy(), ksize=5)
kalgorithm.imgSave(meanFilterOutput, img)
# 中值滤波
medianFilterOutput = r"./output_images/phase2/phase2_broken_median_filter.jpg"
img = kalgorithm.medianFilter(imgsrc.copy(), ksize=5)
kalgorithm.imgSave(medianFilterOutput, img)
# 高斯滤波
gaussianFilterOutput = r"./output_images/phase2/phase2_broken_gaussian_filter.jpg"
img = kalgorithm.gaussianFilter(imgsrc.copy(), ksize=5)
kalgorithm.imgSave(gaussianFilterOutput, img)
def maintask(xcode):
# 在前面结果的基础上，继续工作。
prefile = r"./output_images/phase3/phase3_sky.jpg_sky_cloud.png"
imgsrc = kalgorithm.imgRead(prefile).astype(np.float32)
mask_fishergirl = inputfile + "_fisher_girl_mask.png"
mask_sea = inputfile + "_ocean_wave_mask.png"
mask_sky = inputfile + "_sky_mask.png"
sky = kalgorithm.imgRead(skyfile).astype(np.float32)
mask_sea = kalgorithm.imgRead(mask_sea).astype(np.float32)
sky = kalgorithm.blInterpolate(sky, 0.33333333334, 0.33333333334)
# 做一个天空的镜像
skydst = sky.copy()
skyH, skyW, skyC = sky.shape
for y in range(skyH):
for x in range(skyW):
skydst[y, x] = sky[skyH - 1 - y, skyW - 1 - x]
sky = skydst
print("imgsrc", imgsrc.shape, "sky",
sky.shape) # (744, 1100, 3) (618, 1100, 3)
print("mask_sea", mask_sea.shape) # (744, 1100, 3)
# 把天空镜像搞成一样大，才可以做乘法。
newsky = np.zeros((mask_sea.shape[0], mask_sea.shape[1], sky.shape[2]),
np.uint8)
newsky[:, :] = 0
peak = findvline() + 1 # 前面算出来了这个分割位置
print("peak", peak)
newsky[peak:, :] = sky[:newsky.shape[0] - peak, :]
print("newsky", newsky.shape)
#display(newsky)
# 水面 云彩 倒影
mask_sea[:peak, ...] = 0
mask_sea = kalgorithm.bgr2gray(mask_sea)
# 不要边缘（前面自己写的边缘修复算法存在些许缺陷）
mask_sea[..., :5] = 0
mask_sea[..., mask_sea.shape[1] - 5:] = 0
# 海水搞大一点，倒影更明显一点。
if not os.path.exists(outfile + "_mask_sea.png"):
mask_sea = morphologyDilate(mask_sea, 1) # 膨胀
mask_sea = morphologyDilateLine(mask_sea, 2, linelen=3) # 水平膨胀
mask_sea = kalgorithm.meanFilter2D(mask_sea, K_size=7) # 滤波
mask_sea[:peak + 1, ...] = 0 # 膨胀后的边界清理
mask_sea = kalgorithm.meanFilter2D(mask_sea) # 滤波
kalgorithm.imgSave(outfile + "_mask_sea.png", mask_sea)
#display(mask_sea)
mask_sea = kalgorithm.imgRead(outfile + "_mask_sea.png").astype(
np.float32) / 255
print(imgsrc.shape, mask_sea.shape)
outv = ((newsky * mask_sea * 0.35) + # 下面部分，天空倒影
(imgsrc * mask_sea * 0.4) + # 下面部分，原图
(imgsrc * (1 - mask_sea) * 0.8)) # 其他部分
H, W, C = outv.shape
GSIZE = H
GaussianMask = gaussianMask(outv, dr=GSIZE / 4)
if xcode:
hsv = kalgorithm.bgr2hsv(outv)
print("outv", outv.shape, outv[0, 0])
for y in range(H):
for x in range(W):
hsv[y, x, 0] = xcode #(35*2+hsv[y, x, 0])/3
hsv[y, x, 2] = hsv[y, x, 2] * GaussianMask[y, x]
outv = kalgorithm.hsv2bgr(hsv, outv)
#display(outv)
else:
print("outv", outv.shape, outv[0, 0])
for y in range(H):
for x in range(W):
outv[y, x, 2] = outv[y, x, 2] * GaussianMask[y, x]
outv[y, x, 1] = outv[y, x, 1] * GaussianMask[y, x]
outv[y, x, 0] = outv[y, x, 0] * GaussianMask[y, x]
#display(outv)
# 降低画面曝光度，伽马变换
if True: # 鱼女剪影
imgsrc = imgsrc #alg.gammaCorrection(imgsrc, g=0.5)
print(imgsrc.shape, imgsrc[0, 0])
hsv = kalgorithm.bgr2hsv(imgsrc)
print(imgsrc.shape, imgsrc[0, 0])
H, W, C = imgsrc.shape
for y in range(H):
for x in range(W):
hsv[y, x, 2] = hsv[y, x, 2] / 3
imgsrc = kalgorithm.hsv2bgr(hsv, imgsrc)
#display(imgsrc)
# 渔女剪影效果
# 背景调暗，渔女调整的更暗。
outvgril = imgsrc #alg.gammaCorrection(outv, g=0.3)
mask_fishergirl = 255 - kalgorithm.imgRead(mask_fishergirl)
mask_fishergirl = kalgorithm.meanFilter(mask_fishergirl)
mask_fishergirl = mask_fishergirl.astype(np.float) / 255.
outv = outv * (1 - mask_fishergirl) + outvgril * mask_fishergirl
outv = np.clip(outv, 0, 255)
# 对最终结果再微调。
if True:
pass # 哎，算了，就这样吧。
#cv2.putText(outv, "{}".format(xcode), (10, 40), cv2.FONT_HERSHEY_SIMPLEX,
# 1, (0, 255, 255), 1, cv2.LINE_AA)
if xcode:
kalgorithm.imgSave(outfile + "_glod.png", outv)
else:
kalgorithm.imgSave(outfile, outv)
def nnInterpolateProc(imgsrc, imgdst, scale):
img = kalgorithm.nnInterpolate(imgsrc, scale, scale)
kalgorithm.imgSave(imgdst, img)
def phase1(inputImgPath: str, outImgPath: str):
img = kalgorithm.imgRead(inputImgPath)
img = 255 - img
kalgorithm.imgSave(outImgPath, img)
