def readTxt(latitude, longitude, sm):
# read each line of weather data
# transfer the time to sun angles
# save the data
f = open("testseattle.txt", "r")
lines = f.readlines()
data = np.zeros([4709, 7])
i = 0
for line in lines:
month = int(line.splitlines()[0].split(",")[0])
date = int(line.splitlines()[0].split(",")[1])
time = float(line.splitlines()[0].split(",")[2])
dir = int(line.splitlines()[0].split(",")[3])
dif = int(line.splitlines()[0].split(",")[4])
al, az = time_to_sun_angles.timeToAltitudeAzimuth(
date, month, time, latitude, longitude, sm)
if (dir > 10) or (dif > 10):
data[i] = np.array([dir, dif, al, az, month, date, time])
i = i + 1
return data
def read_one_image(log_process, log_base, sky_map, input_dim, output_dim,
dir_ab4, dir_ab0, dir_sky, dir_npy, file_name):
# load an image (numpy arrays) from a txt file
im = np.load(dir_ab4 + file_name).astype(np.float64)
im_resized = cv2.resize(im, (IMAGE_SIZE[1], IMAGE_SIZE[0]))
# im_resized = im
sun_im = np.load(dir_ab0 + file_name[:-4] + "_ab0.npy").astype(np.float64)
sun_im_resized = cv2.resize(sun_im, (IMAGE_SIZE[1], IMAGE_SIZE[0]))
# sun_im_resized = sun_im
sky_im = np.load(dir_sky + file_name).astype(np.float64)
sky_im_resized = cv2.resize(sky_im, (IMAGE_SIZE[1], IMAGE_SIZE[0]))
# sky_im_resized = sky_im
# create a new image frame and store all image information in this frame
image_frame = np.zeros(
(IMAGE_SIZE[1] * IMAGE_SIZE[0], input_dim + output_dim),
dtype='float32')
file = file_name.split('/')[0]
# get month, date and time from file name
month, date, standard_time = get_month_date(file)
dir, dif = get_dir_dif((file))
# read image pixel by pixel, reorganize the data
# return image with shape(PIXELS, 6(px, py, altitude, azimuth, ave_luminance(empty for now), real_luminance))
for i in range(IMAGE_SIZE[1] * IMAGE_SIZE[0]):
index1, index2 = np.unravel_index(i, (IMAGE_SIZE[0], IMAGE_SIZE[1]))
full_im_lum = im_resized[index1, index2]
sun_patch_lum = sun_im_resized[index1, index2]
sky_lum = sky_im_resized[index1, index2]
if log_process == True:
if full_im_lum < 1:
image_frame[i, -1] = sys.float_info.min
else:
image_frame[i, -1] = math.log(full_im_lum,
log_base) # save sun patch image
if sun_patch_lum < 1:
image_frame[i, -2] = sys.float_info.min
else:
image_frame[i, -2] = math.log(sun_patch_lum,
log_base) # save sun patch image
if sky_map:
if sky_lum < 1:
image_frame[i, -3] = sys.float_info.min
else:
image_frame[i, -3] = math.log(
sky_lum, log_base) # save sky luminance panorama image
else:
image_frame[i, -1] = full_im_lum
image_frame[i, -2] = sun_patch_lum
if sky_map:
image_frame[i, -3] = sky_lum
image_frame[i, 0:2] = index1, index2 # save px, py
image_frame[i, 2:4] = time_to_sun_angles.timeToAltitudeAzimuth(
date, month, standard_time, LATITUDE, LONGITUDE,
SM) # save altitude, azimuth
image_frame[i, 4:6] = dir, dif # save direct and diffuse irradiances
# save this image
dir_path = dir_npy + file_name + '.npy'
np.save(dir_path, image_frame)
return image_frame