init

src/python/main.py

@@ -0,0 +1,53 @@
+from collections import defaultdict
+import csv
+import sys
+
+import cv2
+from shapely.geometry import MultiPolygon, Polygon
+import shapely.wkt
+import shapely.affinity
+import numpy as np
+import tifffile as tiff
+
+#%matplotlib inline
+import matplotlib.pyplot as plt
+from matplotlib import cm
+
+FILE_2015 = '/media/sf_share/linux/workspaces/guangdong/input/quickbird2015.tif'
+FILE_2017 = '/media/sf_share/linux/workspaces/guangdong/input/quickbird2017.tif'
+FILE_cadastral2015 = '/media/sf_share/linux/workspaces/guangdong/input/cadastral2015.tif'
+FILE_tinysample = '/media/sf_share/linux/workspaces/guangdong/input/tinysample.tif'
+
+#read images
+im_2015 = tiff.imread(FILE_2015).transpose([1, 2, 0])
+im_2017 = tiff.imread(FILE_2017).transpose([1, 2, 0])
+im_tiny = tiff.imread(FILE_tinysample)
+im_cada = tiff.imread(FILE_cadastral2015)
+
+im_2015.shape
+im_tiny.shape
+im_cada.shape
+
+
+def scale_percentile(matrix):
+    w, h, d = matrix.shape
+    matrix = np.reshape(matrix, [w * h, d]).astype(np.float64)
+    # Get 2nd and 98th percentile
+    mins = np.percentile(matrix, 1, axis=0)
+    maxs = np.percentile(matrix, 99, axis=0) - mins
+    matrix = (matrix - mins[None, :]) / maxs[None, :]
+    matrix = np.reshape(matrix, [w, h, d])
+    matrix = matrix.clip(0, 1)
+    return matrix
+
+fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(16, 6))
+
+p1 = plt.subplot(121)
+i1 = p1.imshow(scale_percentile(im_2015[100:1000, 100:1000, :3]))
+plt.colorbar(i1)
+
+p2 = plt.subplot(122)
+i2 = p2.imshow(im_2015[100:1000, 100:1000, 3])
+plt.colorbar(i2)
+
+plt.show()

src/python/plot2.py

@@ -0,0 +1,19 @@
+fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(14, 10))
+
+p1 = plt.subplot(221)
+i1 = p1.imshow(im_2015[100:1000, 100:1000, 0])
+plt.colorbar(i1)
+
+p2 = plt.subplot(222)
+i2 = p2.imshow(im_2015[100:1000, 100:1000, 1])
+plt.colorbar(i2)
+
+p3 = plt.subplot(223)
+i3 = p3.imshow(im_2015[100:1000, 100:1000, 2])
+plt.colorbar(i3)
+
+p4 = plt.subplot(224)
+i4 = p4.imshow(im_2015[100:1000, 100:1000, 3])
+plt.colorbar(i4)
+
+plt.show()

src/python/result.py

@@ -0,0 +1,37 @@
+x_start = 500
+x_end = 1000
+y_start = 4800
+y_end = 5300
+
+plt.subplots(ncols=3, nrows=2, figsize=(16, 8))
+
+mt = np.ma.array(np.ones((x_end-x_start, y_end-y_start)), 
+                 mask=((im_tiny[x_start:x_end, y_start:y_end, 0]/np.max(im_tiny)+im_cada[x_start:x_end, y_start:y_end])==0))
+
+p151 = plt.subplot(231)
+i151 = p151.imshow(im_2015[x_start:x_end, y_start:y_end, 3])
+plt.colorbar(i151)
+
+p152 = plt.subplot(233)
+i152 = p152.imshow(im_tiny[x_start:x_end, y_start:y_end, 0])
+plt.colorbar(i152)
+
+p153 = plt.subplot(232)
+i153 = p153.imshow(im_2015[x_start:x_end, y_start:y_end, 3])
+p153.imshow(mt, cmap=cm.bwr, alpha=0.3, vmin=0, vmax=1)
+plt.colorbar(i153)
+
+p171 = plt.subplot(234)
+i171 = p171.imshow(im_2017[x_start:x_end, y_start:y_end, 3])
+plt.colorbar(i171)
+
+p172 = plt.subplot(236)
+i172 = p172.imshow(im_cada[x_start:x_end, y_start:y_end])
+plt.colorbar(i172)
+
+p173 = plt.subplot(235)
+i173 = p173.imshow(im_2017[x_start:x_end, y_start:y_end, 3])
+p173.imshow(mt, cmap=cm.bwr, alpha=0.3, vmin=0, vmax=1)
+plt.colorbar(i173)
+
+plt.show()