diff --git a/OBIA/OBIABase.py b/OBIA/OBIABase.py
index a5e33f726873bf3fb35d1c24d8295a44694b5efe..e45f046f081a6b9cb820ea28077b138591ce055a 100644
--- a/OBIA/OBIABase.py
+++ b/OBIA/OBIABase.py
@@ -324,11 +324,13 @@ class OBIABase:
self.output_map[self.tiles[tilenum][1]:self.tiles[tilenum][1]+self.tiles[tilenum][3],
self.tiles[tilenum][0]:self.tiles[tilenum][0]+self.tiles[tilenum][2]] += tmp[tile_obj]
else:
+ if not os.path.exists(os.path.dirname(output_file)):
+ os.makedirs(os.path.dirname(output_file))
if tilenum == 0 and os.path.exists(output_file):
os.remove(output_file)
mode = 'w+' if not os.path.exists(output_file) else 'r+'
with rio.open(output_file, mode, driver='GTiff', width=self.W, height=self.H, crs=self.crs,
- transform=self.transform, count=1, dtype=np.int16, compress=compress) as dst:
+ transform=self.transform, count=1, dtype=np.uint16, compress=compress) as dst:
win = Window(self.tiles[tilenum][0], self.tiles[tilenum][1],
self.tiles[tilenum][2], self.tiles[tilenum][3])
x = dst.read(1, window=win)
diff --git a/Postprocessing/Report.py b/Postprocessing/Report.py
index f3addd2c95f164d87499ef4a1abe097705fce931..eaab855449076d22d570314e66a04195bc8eb1b4 100644
--- a/Postprocessing/Report.py
+++ b/Postprocessing/Report.py
@@ -1,12 +1,43 @@
import matplotlib
matplotlib.use('Agg')
+from matplotlib.patches import Rectangle
import matplotlib.pyplot as plt
-from sklearn.metrics import ConfusionMatrixDisplay, classification_report
+from sklearn.metrics import ConfusionMatrixDisplay, classification_report, accuracy_score, cohen_kappa_score
+from fpdf import FPDF
import numpy as np
+import rasterio
+from rasterio.enums import Resampling
+from pyproj import Transformer as T
+from datetime import datetime
import os
-def generate_report_figures(yt_yp, out_dir, class_names=None):
+def parse_colormap_file(fn):
+ labels = []
+ colors = []
+ class_names = []
+
+ with open(fn, 'r') as f:
+ for l in f.read().splitlines():
+ sl = l.split(' ')
+ labels.append(int(sl[0]))
+ colors.append((int(sl[1]),int(sl[2]),int(sl[3]),int(sl[4])))
+ class_names.append(' '.join(sl[5:]))
+
+ z = sorted(zip(labels,class_names,colors))
+ labels = [x[0] for x in z]
+ class_names = [x[1] for x in z]
+ colors = [x[2] for x in z]
+
+ return labels, class_names, colors
+
+def generate_report_figures(map, yt_yp, palette_fn, out_dir, map_name=None):
+ labels, class_names, colors = parse_colormap_file(palette_fn)
+ colors_norm = [(x[0]/255,x[1]/255,x[2]/255,x[3]/255) for x in colors]
with plt.ioff():
+ font = {'family': 'Arial',
+ 'weight': 'normal',
+ 'size': 8}
+ plt.rc('font', **font)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if not isinstance(yt_yp, list):
@@ -14,19 +45,128 @@ def generate_report_figures(yt_yp, out_dir, class_names=None):
of = {}
of['conf_matrices'] = []
for i,r in enumerate(yt_yp):
- cm = ConfusionMatrixDisplay.from_predictions(r[0], r[1], display_labels=class_names, normalize='true',
- include_values=True, xticks_rotation=45)
- of['conf_matrices'].append('{}/conf_matrix_{}.eps'.format(out_dir, str(i).zfill(2)))
- cm.figure_.savefig(of['conf_matrices'][-1], bbox_inches='tight')
+ cm = ConfusionMatrixDisplay.from_predictions(r[0], r[1], normalize='true', include_values=True)
+ of['conf_matrices'].append('{}/conf_matrix_{}.png'.format(out_dir, str(i).zfill(2)))
+ cm.ax_.set_xticklabels(class_names, rotation=45, ha='right')
+ cm.ax_.set_yticklabels(class_names)
+ cm.ax_.set_xlabel("Predicted label", labelpad=10)
+ cm.ax_.set_ylabel("True label", labelpad=15)
+ cm.figure_.tight_layout()
+ cm.figure_.savefig(of['conf_matrices'][-1], dpi=300)
of['cl_rep'] = []
summary = []
for r in yt_yp:
of['cl_rep'].append(classification_report(r[0], r[1], output_dict=True, target_names=class_names))
- of['summary']
fsc = [np.array([x[c]['f1-score'] for x in of['cl_rep']]) for c in class_names]
fsc_m = [np.mean(x) for x in fsc]
fsc_s = [np.std(x) for x in fsc]
+ fig, ax = plt.subplots()
+ ax.bar(range(len(class_names)), fsc_m, yerr=fsc_s, align="center", ecolor='black', capsize=10, color=colors_norm)
+ ax.set_xticks(range(len(class_names)))
+ ax.set_xticklabels(class_names, rotation=45, ha='right')
+ ax.set_title('Per-class F1-scores', fontsize=12, fontweight='bold', pad=10)
+ ax.yaxis.grid(True)
+ plt.tight_layout()
+ of['summary'] = '{}/f1scores.png'.format(out_dir)
+ plt.savefig(of['summary'], dpi=300)
+
+ if map_name is None:
+ map_name = 'output'
+ of['quicklook'] = create_map_quicklook_and_legend(map, labels, colors, class_names, yt_yp, out_dir, map_name)
+
+ return of
+
+def create_map_quicklook_and_legend(map, labels, colors, class_names, yt_yp, out_dir, name='', qkl_height=1024):
+ if not os.path.exists(out_dir):
+ os.makedirs(out_dir)
+ oa,k = [],[]
+ for yt,yp in yt_yp:
+ oa.append(accuracy_score(yt, yp))
+ k.append(cohen_kappa_score(yt, yp))
+ oam, oas = np.mean(np.array(oa)), np.std(np.array(oa))
+ km, ks = np.mean(np.array(k)), np.std(np.array(k))
+ with rasterio.open(map) as ds:
+ tr = ds.transform
+ crs = ds.crs.to_epsg()
+ smap = ds.read(1,
+ out_shape=(1, qkl_height, int(ds.width * (qkl_height/ds.height))),
+ resampling=Resampling.nearest
+ )
+ cmap = np.zeros((np.max(labels)+1,4))
+ cmap[labels] = np.array([np.array(list(c)) for c in colors])
+ img = cmap[smap].astype(int)
+
+ font = {'family': 'Arial',
+ 'weight': 'normal',
+ 'size': 6}
+ plt.rc('font', **font)
+ fig, ax = plt.subplots()
+ ax.imshow(img)
+ custom_leg = [Rectangle([0,0], 0, 0, fill=True, edgecolor=(0,0,0),
+ facecolor=([x[0]/255,x[1]/255,x[2]/255,x[3]/255])) for x in colors]
+ ax.legend(custom_leg, class_names, loc='center left', bbox_to_anchor=(1, 0.5), fontsize=6)
+ conv = T.from_crs(crs, 4326, always_xy=True)
+ xt, yt = ax.get_xticks(), ax.get_yticks()
+ xpos = ['{:.3f}°'.format(conv.transform(tr[2] + tr[0]*x,0)[0]) for x in xt[1:-1]]
+ ypos = ['{:.3f}°'.format(conv.transform(0, tr[5] + tr[4] * x)[1]) for x in yt[1:-1]]
+ ax.set_xticks(xt[1:-1])
+ ax.set_yticks(yt[1:-1])
+ ax.set_xticklabels(xpos, rotation=45, ha='right')
+ ax.set_yticklabels(ypos)
+ ax.set_title("Final Classification Map Quicklook", fontsize=12, fontweight='bold', pad=15)
+ ax.set_xlabel("Overall Map Accuracy : {:.2f} +/- {:.2f}\nCohen's Kappa : {:.2f} +/- {:.2f}".format(oam,oas,km,ks),
+ fontsize=10, fontweight='bold', labelpad=15)
+ plt.tight_layout()
+ out_fn = '{}/{}_QKL.png'.format(out_dir,name)
+ plt.savefig(out_fn, dpi=300)
+ return out_fn
+
+def generate_pdf(of, out_pdf, name='output'):
+ pdf = FPDF('P', 'mm', 'A4')
+ pdf.set_font("helvetica", 'B', 16)
+ # Page 1 - Summary (OA/Kappa/Quickview)
+ pdf.add_page()
+ pdf.set_xy(0,16)
+ txt = 'Moringa Final Report for Chain {}, {}'.format(name, datetime.now().strftime('%Y-%m-%d %Hh%M'))
+ pdf.cell(0, txt=txt, align='C')
+ pdf.image(of['quicklook'], 14, 24, h=140)
+ pdf.image(of['summary'], 22, 170, h=120)
+ # Pages 2-end, Per-fold assessment
+ for i,(cm,rep) in enumerate(zip(of['conf_matrices'], of['cl_rep'])):
+ pdf.add_page()
+ pdf.set_xy(0, 16)
+ pdf.set_font("helvetica", '', 14)
+ pdf.cell(0, txt="Per-fold assessment - Fold #{}".format(str(i+1).zfill(2)), align='C')
+ pdf.image(cm, 14,24,h=120)
+ table_data = [["Class", "Precision", "Recall", "F1-score", "Support"]]
+ for k in list(rep.keys())[:-3] + list(rep.keys())[-2:]:
+ table_data.append([k, rep[k]['precision'], rep[k]['recall'], rep[k]['f1-score'], rep[k]['support']])
+ pdf.set_xy(0, 150)
+ pdf.set_font("helvetica", '', 10)
+ with pdf.table(width=140, col_widths=(60,20,20,20,20)) as table:
+ row = table.row()
+ for datum in table_data[0]:
+ row.cell(datum, align='C')
+ for data_row in table_data[1:-2]:
+ row = table.row()
+ row.cell(data_row[0])
+ for datum in data_row[1:4]:
+ row.cell('{:.4f}'.format(datum), align='R')
+ row.cell('{}'.format(data_row[-1]), align='R')
+ row = table.row()
+ row.cell('Summary')
+ for data_row in table_data[-2:]:
+ row = table.row()
+ row.cell(data_row[0])
+ for datum in data_row[1:4]:
+ row.cell('{:.4f}'.format(datum), align='R')
+ row.cell('{}'.format(data_row[-1]), align='R')
+ row = table.row()
+ row.cell('Accuracy')
+ row.cell('{:.2f}%'.format(rep['accuracy']*100), align='R')
+
+
+ pdf.output(out_pdf)
- return fsc
diff --git a/docker/dockerfile b/docker/dockerfile
index 2804f617735641e33c8e2ccd50045d7556b4b937..4361f75c2a57f7ec538778f42fb4782f70935892 100644
--- a/docker/dockerfile
+++ b/docker/dockerfile
@@ -113,7 +113,8 @@ RUN pip install gdal==3.4.2 \
scikit-image \
psutil \
planetary_computer \
- theia_picker
+ theia_picker \
+ fpdf2
RUN mkdir moringav2
COPY --chown=ubuntu . /home/ubuntu/moringav2
\ No newline at end of file