sbrl-github/research-rainfallradar
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in this entire blasted project I have yet to get the rotation of anything correct....!

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Starbeamrainbowlabs 2022-11-11 18:58:45 +00:00
parent 4b2e418ddc
commit 54ae88b1b4
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 16 additions and 6 deletions
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3
aimodel/src/lib/ai/model_rainfallwater_mono.py
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@ -20,7 +20,8 @@ def model_rainfallwater_mono(metadata, shape_water_out, model_arch_enc="convnext
Returns: Returns:
tf.keras.Model: The new model, freshly compiled for your convenience! :D tf.keras.Model: The new model, freshly compiled for your convenience! :D
""" """
rainfall_channels, rainfall_width, rainfall_height = metadata["rainfallradar"] # shape = [channels, width, height] rainfall_channels, rainfall_height, rainfall_width = metadata["rainfallradar"] # shape = [channels, height, weight]
# BUG: We somehow *still* have the rainfall radar data transposed incorrectly! I have no idea how this happened. dataset_mono fixes it with (another) transpose
print("RAINFALL channels", rainfall_channels, "width", rainfall_width, "height", rainfall_height) print("RAINFALL channels", rainfall_channels, "width", rainfall_width, "height", rainfall_height)
out_water_width, out_water_height = shape_water_out out_water_width, out_water_height = shape_water_out

19
aimodel/src/lib/dataset/dataset_mono.py
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@ -15,12 +15,12 @@ from .shuffle import shuffle
# TO PARSE: # TO PARSE:
def parse_item(metadata, shape_water_desired=[100,100], water_threshold=0.1, water_bins=2): def parse_item(metadata, shape_water_desired=[100,100], water_threshold=0.1, water_bins=2):
water_width_source, water_height_source = metadata["waterdepth"] water_height_source, water_width_source = metadata["waterdepth"]
water_width_target, water_height_target = shape_water_desired water_height_target, water_width_target = shape_water_desired
water_offset_x = math.ceil((water_width_source - water_width_target) / 2) water_offset_x = math.ceil((water_width_source - water_width_target) / 2)
water_offset_y = math.ceil((water_height_source - water_height_target) / 2) water_offset_y = math.ceil((water_height_source - water_height_target) / 2)
rainfall_channels, rainfall_width, rainfall_height = metadata["rainfallradar"]
def parse_item_inner(item): def parse_item_inner(item):
parsed = tf.io.parse_single_example(item, features={ parsed = tf.io.parse_single_example(item, features={
"rainfallradar": tf.io.FixedLenFeature([], tf.string), "rainfallradar": tf.io.FixedLenFeature([], tf.string),
@ -28,12 +28,21 @@ def parse_item(metadata, shape_water_desired=[100,100], water_threshold=0.1, wat
}) })
rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32) rainfall = tf.io.parse_tensor(parsed["rainfallradar"], out_type=tf.float32)
water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32) water = tf.io.parse_tensor(parsed["waterdepth"], out_type=tf.float32)
# [channels, width, height] → [width, height, channels] - ref ConvNeXt does not support data_format=channels_first
rainfall = tf.reshape(rainfall, tf.constant(metadata["rainfallradar"], dtype=tf.int32)) rainfall = tf.reshape(rainfall, tf.constant(metadata["rainfallradar"], dtype=tf.int32))
water = tf.reshape(water, tf.constant(metadata["waterdepth"], dtype=tf.int32)) water = tf.reshape(water, tf.constant(metadata["waterdepth"], dtype=tf.int32))
rainfall = tf.transpose(rainfall, [1, 2, 0]) # channels_first → channels_last # Apparently the water depth data is also in HW instead of WH.... sighs
water = tf.transpose(water, [1, 0])
# [channels, height, weight] → [width, height, channels] - ref ConvNeXt does not support data_format=channels_first
# BUG: For some reasons we have data that's not transposed correctly still!! O.o
# I can't believe in this entire project I have yet to get the rotation of the rainfall radar data correct....!
# %TRANSPOSE%
rainfall = tf.transpose(rainfall, [2, 1, 0])
# rainfall = tf.image.resize(rainfall, tf.cast(tf.constant(metadata["rainfallradar"]) / 2, dtype=tf.int32)) # rainfall = tf.image.resize(rainfall, tf.cast(tf.constant(metadata["rainfallradar"]) / 2, dtype=tf.int32))
water = tf.expand_dims(water, axis=-1) # [width, height] → [width, height, channels=1] water = tf.expand_dims(water, axis=-1) # [width, height] → [width, height, channels=1]