# 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()
logging.debug("Focal length = %s", focalLength)
# -----------------
# convert to gray
# -----------------
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
# ------------------------------------------------------
# Smooth the image
#--------------------------------------------
# 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)
result = "\n{},{}".format(filename, prediction)
logging.info(result)