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
# nn = the neighborhood size
# For finding the limb, want the image to be very smooth
# -------------------------------------------------------
nn = 11 # Get less edges w/ higher n
#nn = 5
#gray = cv2.GaussianBlur(gray, (nn, nn), 0)
opened4 = cv2.morphologyEx(opened3, cv2.MORPH_OPEN, kernel)
opened = opened4.copy() # to make sure I'm using the right one
else:
opened = filtered.copy()
# Need to mask out by color, then get blobs
# Convert to HSV color
if (doHSV):
hsvImage = cv2.cvtColor(opened, cv2.COLOR_BGR2HSV)
else:
hsvImage = opened.copy()
# Dilate the binary image to compare binary pixel values
focalLength = issimg.get_focal_length()
# Algorithm won't work on very large focal lengths :
# if (focalLength > 200) :
# logging.info("Focal length > 200, skipping")
# continue
# The attribute .size is the diameter of the blob, not its area!!!!!!!!!!
# Size determines the diameter of the meaningful keypoint neighborhood.
# # You can use that size and roughly calculate the area of the blob.
# intitialize min/max area
params.minArea = 50.
params.maxArea = 10000.
kNum = 7