File size: 8,913 Bytes
1c5c2a8 e808741 1c5c2a8 e808741 1c5c2a8 e808741 1c5c2a8 e808741 1c5c2a8 e808741 1c5c2a8 e808741 1c5c2a8 e808741 1c5c2a8 e808741 1c5c2a8 e808741 1c5c2a8 e808741 1c5c2a8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | import src.constants as const
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import streamlit as st
import io
def __pre_process_rain_radar_image(image):
# image = ((image * 549.0) / 255.0)
image = np.clip(image, const.RAIN_RADAR_LEFT_CUTOFF, const.RAIN_RADAR_RIGHT_CUTOFF)
image = np.log(np.add(image, 1)) / np.log(const.RAIN_RADAR_RIGHT_CUTOFF + 1)
return image
def __pre_process_sat_image(image):
# Correct the pixel conversions
# image = ((image * 89.0) / 255.0) - 69
image = np.clip(image, -69, 20)
image = -((image - const.SAT_MEAN) / const.SAT_STD)
image = (image + abs(const.SAT_MIN)) / (abs(const.SAT_MIN) + const.SAT_MAX)
return image
def __pre_process_wind_u(image):
# Correct the pixel conversions
# image = ((image * 48.0) / 255.0) - 16
image = np.clip(image, -16, 32)
image = ((image - const.WIND_U_MEAN) / const.WIND_U_STD)
image = (image + abs(const.WIND_U_MIN)) / (abs(const.WIND_U_MIN) + const.WIND_U_MAX)
return image
def __pre_process_wind_v(image):
# Correct the pixel conversions
# image = ((image * 45.0) / 255.0) - 22
image = np.clip(image, -22, 23)
image = ((image - const.WIND_V_MEAN) / const.WIND_V_STD)
image = (image + abs(const.WIND_V_MIN)) / (abs(const.WIND_V_MIN) + const.WIND_V_MAX)
return image
def __process_pixel_correction(images_dic):
file_name_arr = ["rr_0.png", "rr_15.png","rr_30.png","rr_45.png","rr_60.png", "wu_0.png", "wu_60.png", "wv_0.png", "wv_60.png", "sat_0.png", "sat_60.png"]
# Denormalize from 0-255 to original range using saved constants
def denormalize(data, constants):
data = data.astype(float)
log_val = data / 255 * (constants['max_log_val'] - constants['min_log_val']) + constants['min_log_val']
return np.exp(log_val) + constants['offset'] - 1
for i in range(5):
images_dic[file_name_arr[i]] = denormalize(np.array(images_dic[file_name_arr[i]]), const.RAIN_PIXEL_CORR)
for i in range(5,7):
images_dic[file_name_arr[i]] = denormalize(np.array(images_dic[file_name_arr[i]]), const.WU_PIXEL_CORR)
for i in range(7,9):
images_dic[file_name_arr[i]] = denormalize(np.array(images_dic[file_name_arr[i]]), const.WV_PIXEL_CORR)
for i in range(9,11):
images_dic[file_name_arr[i]] = denormalize(np.array(images_dic[file_name_arr[i]]), const.SAT_PIXEL_CORR)
# images_dic['target.png'][images_dic['target.png']==255] = 1
return images_dic
def process_input_seq(images_dic):
images_dic = __process_pixel_correction(images_dic)
images_dic['rr_0.png'] = __pre_process_rain_radar_image(images_dic['rr_0.png'])
images_dic['rr_15.png'] = __pre_process_rain_radar_image(images_dic['rr_15.png'])
images_dic['rr_30.png'] = __pre_process_rain_radar_image(images_dic['rr_30.png'])
images_dic['rr_45.png'] = __pre_process_rain_radar_image(images_dic['rr_45.png'])
images_dic['rr_60.png'] = __pre_process_rain_radar_image(images_dic['rr_60.png'])
images_dic['sat_0.png'] = __pre_process_sat_image(images_dic['sat_0.png'])
images_dic['sat_60.png'] = __pre_process_sat_image(images_dic['sat_60.png'])
images_dic['wu_0.png'] = __pre_process_wind_u(images_dic['wu_0.png'])
images_dic['wu_60.png'] = __pre_process_wind_u(images_dic['wu_60.png'])
images_dic['wv_0.png'] = __pre_process_wind_v(images_dic['wv_0.png'])
images_dic['wv_60.png'] = __pre_process_wind_v(images_dic['wv_60.png'])
return images_dic, 1
def remove_zero_pad(image):
dummy = np.argwhere(image < 245) # assume blackground is zero
max_y = dummy[:, 0].max()
min_y = dummy[:, 0].min()
min_x = dummy[:, 1].min()
max_x = dummy[:, 1].max()
crop_image = image[min_y:max_y, min_x:max_x]
return crop_image
def fig2img(img):
buf = io.BytesIO()
fig, ax = plt.subplots()
ax.set_axis_off() # remove axis ticks and labels
fig.tight_layout(pad=0)
ax.imshow(img, cmap='viridis')
fig.savefig(buf)
buf.seek(0)
img = Image.open(buf)
return img
def plot_seq(seq):
col1, col2, col3, col4, col5, col6 = st.columns(6)
col1.image(fig2img(seq.get("rr_0.png")), use_column_width=True, caption="Rain Radar at t = 0", )
col2.image(fig2img(seq.get("rr_15.png")), use_column_width=True, caption="Rain Radar at t = 15")
col3.image(fig2img(seq.get("rr_30.png")), use_column_width=True, caption="Rain Radar at t = 30")
col4.image(fig2img(seq.get("rr_45.png")), use_column_width=True, caption="Rain Radar at t = 45")
col5.image(fig2img(seq.get("rr_60.png")), use_column_width=True, caption="Rain Radar at t = 60")
col6.image(fig2img(seq.get("wu_0.png")), use_column_width=True, caption="Wind U Component at t = 0")
col1.image(fig2img(seq.get("wu_60.png")), use_column_width=True, caption="Wind U Component at t = 60")
col2.image(fig2img(seq.get("wv_0.png")), use_column_width=True, caption="Wind V Component at t = 0")
col3.image(fig2img(seq.get("wv_60.png")), use_column_width=True, caption="Wind V Component at t = 60")
col4.image(fig2img(seq.get("sat_0.png")), use_column_width=True, caption="Satellite at t = 0")
col5.image(fig2img(seq.get("sat_60.png")), use_column_width=True, caption="Satellite at t = 60")
def fig2img_overlap(img, overlap):
base_cmap = plt.cm.Reds
# Create a new colormap from the base, modifying the alpha values
colors = base_cmap(np.arange(base_cmap.N))
half_index = base_cmap.N // 8
colors[:half_index, -1] = 0 # np.exp(np.linspace(0, 1, base_cmap.N)) / np.exp(1) # Modify alpha channel
hot_alpha = LinearSegmentedColormap.from_list('hot_alpha', colors, base_cmap.N)
buf = io.BytesIO()
fig, ax = plt.subplots()
ax.set_axis_off() # remove axis ticks and labels
fig.tight_layout(pad=0)
ax.imshow(img, cmap="gray")
ax.imshow(overlap, cmap=hot_alpha, alpha=0.7)
fig.savefig(buf)
buf.seek(0)
img = Image.open(buf)
return img
def plot_seq_with_overlap(seq, overlap_seq):
col1, col2, col3, col4, col5, col6 = st.columns(6)
col1.image(fig2img_overlap(seq.get("rr_0.png"), overlap_seq.get("rr_0.png")), use_column_width=True,
caption="Rain Radar at t = 0", )
col2.image(fig2img_overlap(seq.get("rr_15.png"), overlap_seq.get("rr_15.png")), use_column_width=True,
caption="Rain Radar at t = 15")
col3.image(fig2img_overlap(seq.get("rr_30.png"), overlap_seq.get("rr_30.png")), use_column_width=True,
caption="Rain Radar at t = 30")
col4.image(fig2img_overlap(seq.get("rr_45.png"), overlap_seq.get("rr_45.png")), use_column_width=True,
caption="Rain Radar at t = 45")
col5.image(fig2img_overlap(seq.get("rr_60.png"), overlap_seq.get("rr_60.png")), use_column_width=True,
caption="Rain Radar at t = 60")
col6.image(fig2img_overlap(seq.get("wu_0.png"), overlap_seq.get("wu_0.png")), use_column_width=True,
caption="Wind U Component at t = 0")
col1.image(fig2img_overlap(seq.get("wu_60.png"), overlap_seq.get("wu_60.png")), use_column_width=True,
caption="Wind U Component at t = 60")
col2.image(fig2img_overlap(seq.get("wv_0.png"), overlap_seq.get("wv_0.png")), use_column_width=True,
caption="Wind V Component at t = 0")
col3.image(fig2img_overlap(seq.get("wv_60.png"), overlap_seq.get("wv_60.png")), use_column_width=True,
caption="Wind V Component at t = 60")
col4.image(fig2img_overlap(seq.get("sat_0.png"), overlap_seq.get("sat_0.png")), use_column_width=True,
caption="Satellite at t = 0")
col5.image(fig2img_overlap(seq.get("sat_60.png"), overlap_seq.get("sat_60.png")), use_column_width=True,
caption="Satellite at t = 60")
def __calculate_tp_fp_fn_tn(pred, target):
tp_fp_fn_tn = [0, 0, 0, 0]
diff = 2 * pred - target
diff = np.array(diff)
print("diff:", diff)
tp_fp_fn_tn[0] = (diff == 1).sum()
tp_fp_fn_tn[1] = (diff == 2).sum()
tp_fp_fn_tn[2] = (diff == -1).sum()
tp_fp_fn_tn[3] = (diff == 0).sum()
return tp_fp_fn_tn
def get_precision(pred, target):
tp_fp_fn_tn = __calculate_tp_fp_fn_tn(pred, target)
precision = tp_fp_fn_tn[0] / (tp_fp_fn_tn[0] + tp_fp_fn_tn[1])
return precision
def get_recall(pred, target):
tp_fp_fn_tn = __calculate_tp_fp_fn_tn(pred, target)
recall = tp_fp_fn_tn[0] / (tp_fp_fn_tn[0] + tp_fp_fn_tn[2])
return recall
def get_f1(pred, target):
precision = get_precision(pred, target)
recall = get_recall(pred, target)
f1 = 2 * precision * recall / (precision + recall)
return f1
def get_csi(pred, target):
tp_fp_fn_tn = __calculate_tp_fp_fn_tn(pred, target)
csi = tp_fp_fn_tn[0] / (tp_fp_fn_tn[0] + tp_fp_fn_tn[1] + tp_fp_fn_tn[2])
return csi
|