问题
Image Loading
To start I tried loading in .tif images using openCV but got the following error: Invalid bitsperpixel value read from TIFF header! Must be 1, 8, 16, 32 or 64. in function 'cv::TiffDecoder::readHeader'
Checked using a hex editor to determine the bit depth of the image and it seems to be 16bit.
After some trail and error I managed to find a solution using the code below. First loaded the image with the specialized tifffile library. Then saved it to a temporary file and finally loaded it back in again with openCV. (Directly trying to edit the image read from Tifffile using openCV functions didn't work)
raw_image = tifffile.imread(file)
raw_image <<= 4
name = random_string(10) + '.tif'
tifffile.imwrite(name, raw_image)
image = cv2.imread(file, 0)
os.remove(name)
Processing
As the purpose of my program is to detect and track the droplets present on the image, the periodic stripe pattern/interference makes the tracking algorithm struggle. In a previous question a solution was proposed and initially gave promising results: Removal of horizontal stripes using openCV2
Now after some more testing I'm getting the following result:
Succes depends on the files used.
The following link contains a folder with images it does work on and one with images it doesn't work on. https://drive.google.com/file/d/1ifsae0bl2CRoR2ydlQlf35FlzagmeryL/view?usp=sharing
Something worth mentioning is that this processing is done on a large set of images (+-5000). To speed things up I used multiprocessing as implemented below.
with concurrent.futures.ProcessPoolExecutor(max_workers=3) as executor:
results = executor.map(self.process_image, file_list)
for result in results:
self.image_array.append(result)
def process_image(self, file):
try:
# first the image is read by the tifffile library because openCV can't interpret the
# proprietary bit depth of the provided images. All the bits of the image are shifted
# to acquire an image with the right bit depth. A temporary file with a randomly generated
# name is created because openCV couldn't directly open the shifted image directly.
raw_image = tifffile.imread(file)
raw_image <<= 4
name = random_string(10) + '.tif'
tifffile.imwrite(name, raw_image)
image = cv2.imread(name, 0)
os.remove(name)
cv2.imshow('test', image)
cv2.waitKey(0)
# removal of periodic interference
image = self.remove_periodic_interference(image)
cv2.imshow('test', image)
cv2.waitKey(0)
# application of mask
image = self.aplly_mask(image)
cv2.imshow('test', image)
cv2.waitKey(0)
# image now ready for droplet detection
return image
except cv2.error as e:
print(e, 'check if the image files are present')
# removal of periodic stripe pattern in the lower half of the provided image
# using fourier analysis (fft = fast fourier transformation)
def remove_periodic_interference(self, img):
hh, ww = img.shape
# get min and max and mean values of img
img_min = np.amin(img)
img_max = np.amax(img)
img_mean = int(np.mean(img))
# pad the image to dimension a power of 2
hhh = math.ceil(math.log2(hh))
hhh = int(math.pow(2, hhh))
www = math.ceil(math.log2(ww))
www = int(math.pow(2, www))
imgp = np.full((hhh, www), img_mean, dtype=np.uint8)
imgp[0:hh, 0:ww] = img
# convert image to floats and do dft saving as complex output
dft = cv2.dft(np.float32(imgp), flags=cv2.DFT_COMPLEX_OUTPUT)
# apply shift of origin from upper left corner to center of image
dft_shift = np.fft.fftshift(dft)
# extract magnitude and phase images
mag, phase = cv2.cartToPolar(dft_shift[:, :, 0], dft_shift[:, :, 1])
# get spectrum
spec = np.log(mag) / 20
min, max = np.amin(spec, (0, 1)), np.amax(spec, (0, 1))
# threshold the spectrum to find bright spots
thresh = (255 * spec).astype(np.uint8)
thresh = cv2.threshold(thresh, 155, 255, cv2.THRESH_BINARY)[1]
# cover the center rows of thresh with black
yc = hhh // 2
cv2.line(thresh, (0, yc), (www - 1, yc), 0, 5)
# get the y coordinates of the bright spots
points = np.column_stack(np.nonzero(thresh))
print(points)
# create mask from spectrum drawing horizontal lines at bright spots
mask = thresh.copy()
for p in points:
y = p[0]
cv2.line(mask, (0, y), (www - 1, y), 255, 5)
# apply mask to magnitude such that magnitude is made black where mask is white
mag[mask != 0] = 0
# convert new magnitude and old phase into cartesian real and imaginary components
real, imag = cv2.polarToCart(mag, phase)
# combine cartesian components into one complex image
back = cv2.merge([real, imag])
# shift origin from center to upper left corner
back_ishift = np.fft.ifftshift(back)
# do idft saving as complex output
img_back = cv2.idft(back_ishift)
# combine complex components into original image again
img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
# crop to original size
img_back = img_back[0:hh, 0:ww]
# re-normalize to 8-bits in range of original
min, max = np.amin(img_back, (0, 1)), np.amax(img_back, (0, 1))
notched = cv2.normalize(img_back, None, alpha=img_min, beta=img_max, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
return notched
# application of mask to isolate the usable 'droplets' from each image
# TODO-- optimization --
# TODO-- adjust offset--
def aplly_mask(self, image):
th, threshed = cv2.threshold(image, 116, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C | cv2.ADAPTIVE_THRESH_MEAN_C)
# Find the min-area contour
cnts = cv2.findContours(threshed, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE, offset=(1, 1))[-2]
cnts = sorted(cnts, key=cv2.contourArea)
# Manual scaling of contours
for c in cnts:
M = cv2.moments(c)
if M["m00"] != 0:
cx = int(M["m10"] / M["m00"])
cy = int(M["m01"] / M["m00"])
else:
cx, cy = 0, 0
scale = 1.5
cnt_norm = c - [cx, cy]
cnt_scaled = cnt_norm * scale
cnt_scaled = cnt_scaled + [cx, cy]
cnt_scaled = cnt_scaled.astype(np.int32)
# Mask application
mask = np.zeros(image.shape[:2], np.uint8)
cv2.drawContours(mask, cnts, -1, 255, -1)
dst = cv2.bitwise_and(image, image, mask=mask)
dst = cv2.bitwise_not(dst, dst)
return dst
来源：https://stackoverflow.com/questions/61738477/fourier-analysis-of-tiff-images-using-opencv