def main():
""" The main funtion that parses input arguments, calls the approrpiate
interpolation method and writes the output image"""
#Parse input arguments
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("-i",
"--image",
dest="image",
help="specify the name of the image",
metavar="IMAGE")
args = parser.parse_args()
#Load image
if args.image is None:
print("Please specify the name of image")
print("use the -h option to see usage information")
sys.exit(2)
else:
image_name = args.image.split(".")[0]
input_image = cv2.imread(args.image, 0)
h = input_image.shape
n = h[0] * h[1]
bin_img = bi.binary_image()
hist = bin_img.compute_histogram(input_image)
threshold = bin_img.find_optimal_threshold(hist, n)
print("Optimal threshold: ", threshold)
binary_img = bin_img.binarize(input_image, threshold)
display_image('w', binary_img)
outputDir = 'output/cellct/'
#Saving histogram to output directory
hist_fig = plt.plot(hist)
plt.savefig(outputDir + "hist.png")
output_image_name = outputDir + "binary_image_" + datetime.now().strftime(
"%m%d-%H%M%S") + ".jpg"
cv2.imwrite(output_image_name, binary_img)
#blobcoloring
cell_count_obj = cc.cell_counting()
regions, k, i = cell_count_obj.blob_coloring(binary_img)
[region, hist] = cell_count_obj.compute_statistics(regions, k, i)
cell_stats_img = cell_count_obj.mark_regions_image(region, hist)
output_image_name = outputDir + "cell_stats_" + datetime.now().strftime(
"%m%d-%H%M%S") + ".jpg"
cv2.imwrite(output_image_name, cell_stats_img)
def main():
""" The main funtion that parses input arguments, calls the approrpiate
interpolation method and writes the output image"""
#Parse input arguments
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("-i",
"--image",
dest="image",
help="specify the name of the image",
metavar="IMAGE")
args = parser.parse_args()
#Load image
if args.image is None:
print("Please specify the name of image")
print("use the -h option to see usage information")
sys.exit(2)
else:
image_name = args.image.split(".")[0]
input_image = cv2.imread(args.image, 0)
bin_img = bi.binary_image()
hist = bin_img.compute_histogram(input_image)
outputDir = 'output/cellct/'
outputDir_compress = 'output/Compression/'
#Saving histogram to output directory
hist_fig = plt.plot(hist)
plt.savefig(outputDir + "hist.png")
threshold = bin_img.find_optimal_threshold(hist)
print("Optimal threshold: ", threshold)
binary_img = bin_img.binarize(input_image)
output_image_name = outputDir + "binary_image_" + datetime.now().strftime(
"%m%d-%H%M%S") + ".jpg"
cv2.imwrite(output_image_name, binary_img)
#blobcoloring
cell_count_obj = cc.cell_counting()
regions = cell_count_obj.blob_coloring(binary_img)
stats = cell_count_obj.compute_statistics(regions)
cell_stats_img = cell_count_obj.mark_regions_image(binary_img, stats)
output_image_name = outputDir + "cell_stats_" + datetime.now().strftime(
"%m%d-%H%M%S") + ".jpg"
cv2.imwrite(output_image_name, cell_stats_img)
#Compression
rle_obj = rle.rle()
rle_code = rle_obj.encode_image(binary_img)
print("-------------- Runlength Code -------------------")
print(rle_code)
[height, width] = binary_img.shape
decoded_image = rle_obj.decode_image(rle_code, height, width)
output_image_name = outputDir_compress + "decoded_image_" + datetime.now(
).strftime("%m%d-%H%M%S") + ".jpg"
cv2.imwrite(output_image_name, decoded_image)