update cleanup

Author: CR10-dv

geospatial_learn/handyplots.py

@@ -17,7 +17,7 @@
 
 import numpy as np
 
-import matplotlib.pyplot as plt
+
 
 from sklearn.metrics import accuracy_score, classification_report, confusion_matrix,jaccard_score, f1_score
 from sklearn.metrics import precision_recall_fscore_support as prf
@@ -26,9 +26,9 @@
 import seaborn as sns
 
 import pandas as pd
-
+import matplotlib.pyplot as plt
 def plot_classif_report(trueVals, predVals, labels=None, target_names=None,
-                        colmap=plt.cm.Spectral_r):
+                        colmap=plt.cm.Spectral_r, save=None):
 
     """
     Plot a classification report
@@ -52,10 +52,33 @@ def plot_classif_report(trueVals, predVals, labels=None, target_names=None,
 
     clf_report = classification_report(trueVals, predVals, labels=labels,
                                        target_names=target_names, output_dict=True)
-    
+
     dF = pd.DataFrame(clf_report).iloc[:-1, :].T
+    
+    cbVl = dF.values
+    mn = np.round(cbVl.min(), decimals=2)
+    mx= np.round(cbVl.max(), decimals=2)
+    del cbVl
+    
+    
+    
+    f, ax = plt.subplots(figsize=(10, 10))
+    
+    splot = sns.heatmap(dF, annot=True, linewidths=.5, fmt='.2f', cmap=colmap,
+                        ax=ax, vmin=mn, 
+                        vmax=mx, annot_kws={"size": 20})
+    
+    cbar = ax.collections[0].colorbar
+    cbar.set_ticks([mn, mx])
+    cbar.set_ticklabels([str(mn), str(mx)])
+    
+    
 
-    sns.heatmap(dF, annot=True, cmap=colmap)
+    
+    if save != None:
+    
+        fig = splot.get_figure()
+        fig.savefig(save) 
 
     return dF
 

geospatial_learn/setup.py

@@ -1915,10 +1915,10 @@ def _do_phasecong(tempIm,  low_t=0, hi_t=0, norient=6, nscale=6, sigma=2):#, ske
 #    hArray = orientIm[h]>=hT     
 #    return vArray, hArray
 
-def hough2line(inRas, outShp, edge='canny', sigma=2, low_t=None, 
-               hi_t=None, n_orient=6, n_scale=5, hArray=True, vArray=True,
+def hough2line(inRas, outShp, edge='canny', sigma=2, 
+               thresh=None, ratio=2, n_orient=6, n_scale=5, hArray=True, vArray=True,
                prob=False, line_length=100,
-               line_gap=200, valrange=1, interval=50, band=2,
+               line_gap=200, valrange=1, interval=10, band=2,
                min_area=None):
 
         """ 
@@ -2057,7 +2057,8 @@ def hough2line(inRas, outShp, edge='canny', sigma=2, low_t=None,
 
 
 
-            
+        hi_t = thresh
+        low_t = np.round((thresh / ratio), decimals=1)
 
         if edge == 'phase':
             ph = _do_phasecong(tempIm, low_t, hi_t, norient=n_orient, 
@@ -2198,7 +2199,8 @@ def cv_hough2line(inRas, outShp, edge='canny', sigma=2, low_t=0,
 
 
     # This returns an array of r and theta values 
-    lines1 = cv2.HoughLines(np.uint8(edges),1, np.pi/increment, thresh, min_theta=angles.min()-np.deg2rad(bounds), 
+    lines1 = cv2.HoughLines(np.uint8(edges),1, np.pi/increment, thresh,
+                            min_theta=angles.min()-np.deg2rad(bounds), 
                            max_theta=angles.min()+np.deg2rad(bounds)) 
 
 
@@ -2225,23 +2227,24 @@ def cv_hough2line(inRas, outShp, edge='canny', sigma=2, low_t=0,
         y0 = b*r 
 
         # x1 stores the rounded off value of (rcos(theta)-1000sin(theta)) 
-        x1 = int(x0 + 1000*(-b)) 
+        x1 = int(x0 + img.shape[1]*(-b)) 
 
         # y1 stores the rounded off value of (rsin(theta)+1000cos(theta)) 
-        y1 = int(y0 + 1000*(a)) 
+        y1 = int(y0 + img.shape[0]*(a)) 
 
         # x2 stores the rounded off value of (rcos(theta)+1000sin(theta)) 
-        x2 = int(x0 - 1000*(-b)) 
+        x2 = int(x0 - img.shape[0]*(-b)) 
 
         # y2 stores the rounded off value of (rsin(theta)-1000cos(theta)) 
-        y2 = int(y0 - 1000*(a)) 
+        y2 = int(y0 - img.shape[0]*(a)) 
 
         # cv2.line draws a line in img from the point(x1,y1) to (x2,y2). 
         # (0,0,255) denotes the colour of the line to be  
         #drawn. In this case, it is red.  
         cv2.line(img,(x1,y1), (x2,y2), (0,0,255),1)
 
-    lines2 = cv2.HoughLines(np.uint8(edges),1, np.pi/increment, thresh, min_theta=angles.max()-np.deg2rad(bounds), 
+    lines2 = cv2.HoughLines(np.uint8(edges),1, np.pi/increment, thresh,
+                            min_theta=angles.max()-np.deg2rad(bounds), 
                            max_theta=angles.max()+np.deg2rad(bounds)) 
     for l in tqdm(lines2): 
 
@@ -2264,16 +2267,20 @@ def cv_hough2line(inRas, outShp, edge='canny', sigma=2, low_t=0,
         y0 = b*r 
 
         # x1 stores the rounded off value of (rcos(theta)-1000sin(theta)) 
-        x1 = int(x0 + 1000*(-b)) 
+        
+        
+        
+        
+        x1 = int(x0 + img.shape[1]*(-b)) 
 
         # y1 stores the rounded off value of (rsin(theta)+1000cos(theta)) 
-        y1 = int(y0 + 1000*(a)) 
+        y1 = int(y0 + img.shape[0]*(a)) 
 
         # x2 stores the rounded off value of (rcos(theta)+1000sin(theta)) 
-        x2 = int(x0 - 1000*(-b)) 
+        x2 = int(x0 - img.shape[1]*(-b)) 
 
         # y2 stores the rounded off value of (rsin(theta)-1000cos(theta)) 
-        y2 = int(y0 - 1000*(a)) 
+        y2 = int(y0 - img.shape[0]*(a)) 
 
         # cv2.line draws a line in img from the point(x1,y1) to (x2,y2). 
         # (0,0,255) denotes the colour of the line to be