refactor python ROC script and add axis ticks

apps/sft/misc/sft.py

@@ -1,10 +1,10 @@
 #!/usr/bin/env python
 
 import cv2, re, glob
-import numpy as np
+import numpy             as np
 import matplotlib.pyplot as plt
 
-""" Convert numpy matrices with rectangles and confidences to sorted list of detections."""
+""" Convert numPy matrices with rectangles and confidences to sorted list of detections."""
 def convert2detections(rects, confs, crop_factor = 0.125):
     if rects is None:
         return []
@@ -14,11 +14,13 @@ def convert2detections(rects, confs, crop_factor = 0.125):
     dts = [Detection(r,c) for r, c in dts]
 
     dts.sort(lambda x, y : -1  if (x.conf - y.conf) > 0 else 1)
+
     for dt in dts:
         dt.crop(crop_factor)
 
     return dts
 
+""" Create new instance of soft cascade."""
 def cascade(min_scale, max_scale, nscales, f):
     # where we use nms cv::SCascade::DOLLAR == 2
     c = cv2.SCascade(min_scale, max_scale, nscales, 2)
@@ -27,6 +29,7 @@ def cascade(min_scale, max_scale, nscales, f):
     assert c.load(dom)
     return c
 
+""" Compute prefix sum for en array"""
 def cumsum(n):
     cum = []
     y = 0
@@ -35,6 +38,7 @@ def cumsum(n):
         cum.append(y)
     return cum
 
+""" Compute x and y arrays for ROC plot"""
 def computeROC(confidenses, tp, nannotated, nframes):
     confidenses, tp = zip(*sorted(zip(confidenses, tp), reverse = True))
 
@@ -46,34 +50,52 @@ def computeROC(confidenses, tp, nannotated, nframes):
 
     return fppi, miss_rate
 
-
+""" Crop rectangle by factor"""
 def crop_rect(rect, factor):
     val_x = factor * float(rect[2])
     val_y = factor * float(rect[3])
     x = [int(rect[0] + val_x), int(rect[1] + val_y), int(rect[2] - 2.0 * val_x), int(rect[3] - 2.0 * val_y)]
     return x
 
-#
+"""Initialize plot axises"""
-
+def initPlot(name = "ROC curve Bahnhof"):
-def initPlot():
 
     fig, ax = plt.subplots()
     fig.canvas.draw()
 
     plt.xlabel("fppi")
     plt.ylabel("miss rate")
-    plt.title("ROC curve Bahnhof")
+    plt.title(name)
     plt.grid(True)
     plt.xscale('log')
     plt.yscale('log')
 
-def showPlot(name):
+"""Show resulted plot"""
-    plt.savefig(name)
+def showPlot(file_name):
+    # plt.savefig(file_name)
+    plt.axis((pow(10, -3), pow(10, 1), 0.0, 1))
+    plt.yticks( [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.64, 0.8, 1], ['.05', '.10', '.20', '.30', '.40', '.50', '.64', '.80', '1'] )
     plt.show()
 
+def match(gts, dts):
+    # Cartesian product for each detection BB_dt with each BB_gt
+    overlaps = [[dt.overlap(gt) for gt in gts]for dt in dts]
+
+    matches_gt = [0]*len(gts)
+    matches_dt = [0]*len(dts)
+
+    for idx, row in enumerate(overlaps):
+        imax = row.index(max(row))
+
+        if (matches_gt[imax] == 0 and row[imax] > 0.5):
+            matches_gt[imax] = 1
+            matches_dt[idx]  = 1
+    return matches_dt
+
 
 def plotLogLog(fppi, miss_rate, c):
-    plt.semilogy(fppi, miss_rate, color = c, linewidth = 2)
+    print
+    plt.loglog(fppi, miss_rate, color = c, linewidth = 2)
 
 
 def draw_rects(img, rects, color, l = lambda x, y : x + y):
@@ -102,7 +124,7 @@ class Detection:
     def crop(self, factor):
         self.bb = crop_rect(self.bb, factor)
 
-    # we use rect-stype for dt and box style for gt. ToDo: fix it
+    # we use rect-style for dt and box style for gt. ToDo: fix it
     def overlap(self, b):
 
         a = self.bb
@@ -155,19 +177,3 @@ def norm_box(box, ratio):
 
 def norm_acpect_ratio(boxes, ratio):
     return [ norm_box(box, ratio)  for box in boxes]
-
-
-def match(gts, dts):
-    # Cartesian product for each detection BB_dt with each BB_gt
-    overlaps = [[dt.overlap(gt) for gt in gts]for dt in dts]
-
-    matches_gt = [0]*len(gts)
-    matches_dt = [0]*len(dts)
-
-    for idx, row in enumerate(overlaps):
-        imax = row.index(max(row))
-
-        if (matches_gt[imax] == 0 and row[imax] > 0.5):
-            matches_gt[imax] = 1
-            matches_dt[idx]  = 1
-    return matches_dt