logging.info("find_aurora_hsv : imagePaths is %s", imagePaths)
# --------------------------------------
# Loop over the input dataset of images
# --------------------------------------
for imagePath in imagePaths:
if args.list :
if not os.path.isfile(imagePath) :
sys.exit("imagePath " + imagePath + " does not exist")
# load the image, describe the image, update the list of data
#image = cv2.imread(imagePath)
issimg = ISSIMAGE(imagePath)
# resize the image
# def resize(image, width = None, height = None, inter = cv2.INTER_AREA)
issimg.resize(800)
if (blurImage) :
#resized = cv2.GaussianBlur(resized, (5,5), 0)
#filtered = cv2.bilateralFilter(resized, 9, 75, 75) # preserves edges better
filtered = cv2.bilateralFilter(issimg.image, 9, 75, 75) # preserves edges better
else:
filtered = resized.copy()
if (doOpening) :
# n = 3
# n = 5
# --------------------------------------
for filename in imagePaths:
if args.list:
if not os.path.isfile(filename):
sys.exit("filename " + filename + " does not exist")
# Read image
img = cv2.imread(filename)
# resize the image
# def resize(image, width = None, height = None, inter = cv2.INTER_AREA)
resized = resize(img, width=500)
# initialize ISSIMAGE class for setting up the image
xx = ISSIMAGE(filename)
xx.resize()
xx.show()
# ----------------------
# Get the sun elevation
# ----------------------
sunElev = xx.get_sun_elev()
logging.debug("Sun elevation = %s", sunElev)
# ----------------------
# Get the focal length
# ----------------------
focalLength = xx.get_focal_length()
cv2.resizeWindow('d12-d10', 640, 426)
# --------------------------------------
# Loop over the input dataset of images
# --------------------------------------
for ii in range(1, len(imagePaths), 1):
filename = imagePaths[ii] # will need to change this later
if not os.path.isfile(filename):
sys.exit("filename " + filename + " does not exist")
logging.info("Filename = %s", filename)
# set up the ISSIMAGE object
issimg0 = ISSIMAGE(imagePaths[ii-1])
issimg1 = ISSIMAGE(imagePaths[ii])
issimg2 = ISSIMAGE(imagePaths[ii+1])
logging.info("comparing %s %s %s", imagePaths[ii-1], imagePaths[ii], imagePaths[ii+1])
# Smooth the image
if (doSmoothing):
filtered0 = cv2.bilateralFilter(issimg0.image, 9, 75, 75)
filtered1 = cv2.bilateralFilter(issimg1.image, 9, 75, 75)
filtered2 = cv2.bilateralFilter(issimg2.image, 9, 75, 75)
else:
filtered0 = issimg0.image.copy()
filtered1 = issimg1.image.copy()
filtered2 = issimg2.image.copy()
# -----------------------------------------------------
imagePaths = list(paths.list_images(args.dataset))
# sort the filenames
imagePaths = sorted(imagePaths)
logging.debug("imagePaths is %s", imagePaths)
# --------------------------------------
# Loop over the input dataset of images
# --------------------------------------
for imagePath in imagePaths:
if args.list:
if not os.path.isfile(imagePath):
sys.exit("imagePath " + imagePath + " does not exist")
# initialize ISSIMAGE class for setting up the image
xx = ISSIMAGE(imagePath)
xx.resize()
xx.show()
# plot histogram for this image
if showHist:
xx.plot_color_hist()
cv2.waitKey(0)
cv2.destroyAllWindows()
#-----------------------------
desc = HSVColorTexture()
#--------------------------------------------
# Loop over images in the query data set :
#--------------------------------------------
with open(resultsName, 'wb') as csvfile:
# --------------------------------------
# Loop over the input dataset of images
# --------------------------------------
for filename in imagePaths:
# for imagePath2 in imagePaths2:
# # Grab the image and classify it
# # Set up the ISSIMAGE object
issimg = ISSIMAGE(filename)
"""Only look at images with sun elevation > minSunElev"""
if issimg.get_sun_elev() > minSunElev:
continue
filename = os.path.basename(filename)
# read the image - lrm
logging.info("Reading test file %s", filename)
image = cv2.imread(filename)
# describe the image
features = desc.describe(issimg.image)
# Run model prediction on the features
prediction = model.predict(features)
logging.info("Prediction is %s", prediction)
logging.info("lightning_detection_nadir : blackLower = %s", blackLower)
logging.info("lightning_detection_nadir : blackUpper = %s", blackUpper)
# --------------------------------------
# Loop over the input dataset of images
# --------------------------------------
for filename in imagePaths:
if not os.path.isfile(filename):
sys.exit("filename " + filename + " does not exist")
logging.info("Filename = %s", filename)
# set up the ISSIMAGE object
issimg = ISSIMAGE(filename)
"""Only look at images with sun elevation > minSunElev"""
# if sun elev larger, skip to next file
if issimg.get_sun_elev() > minSunElev:
logging.info("SunElev too large, skipping")
continue
# -------------------
# Resize the image
# -------------------
issimg.resize(800)
# Smooth the image
if (doSmoothing):
filtered = cv2.bilateralFilter(issimg.image, 9, 75, 75)
logging.info("imagePath is %s", imagePaths)
# --------------------------------------
# Loop over the input dataset of images
# --------------------------------------
for filename in imagePaths:
#if args.list :
if not os.path.isfile(filename):
sys.exit("filename " + filename + " does not exist")
# Read image
#im = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
#im = cv2.imread(filename)
issimg = ISSIMAGE(filename)
# -------------------
# Resize the image
# -------------------
#resized = resize(im, width=500)
issimg.resize(800)
# Smooth the image
if (doSmoothing):
#filtered = cv2.bilateralFilter(resized, 9, 75, 75)
filtered = cv2.bilateralFilter(issimg.image, 9, 75, 75)
else:
filtered = resized.copy()