write numbers on top of bars

main.py

@@ -149,29 +149,55 @@ def draw_text_in_image(img, text, pos, color, line_width):
 """
  Draw plot using Matplotlib
 """
-def draw_plot_func(dictionary, n_classes, window_title, plot_title, y_label, output_path, to_show, plot_color, stack_bar):
+def draw_plot_func(dictionary, n_classes, window_title, plot_title, y_label, output_path, to_show, plot_color, true_p_bar):
+  # set window title
+  fig = plt.gcf() # gcf - get current figure
+  fig.canvas.set_window_title(window_title)
   # sort the dictionary by decreasing value (reverse=True), into a list of tuples
   sorted_dic_by_value = sorted(dictionary.items(), key=operator.itemgetter(1), reverse=True)
   # unpacking the list of tuples into two lists
   sorted_keys, sorted_values = zip(*sorted_dic_by_value)
-  plt.bar(range(n_classes), sorted_values, align='center', color=plot_color, label='True Predictions')
-  # special case to draw in red false detections
-  if stack_bar != "":
-    stack_list = []
+  # special case to draw in (green, true predictions) & (red, false predictions)
+  if true_p_bar != "":
+    fp_sorted = []
+    tp_sorted = []
     for key in sorted_keys:
-      stack_list.append(dictionary[key] - stack_bar[key])
-    plt.bar(range(n_classes), stack_list, align='center', color='crimson', label='False Predictions')
+      fp_sorted.append(dictionary[key] - true_p_bar[key])
+      tp_sorted.append(true_p_bar[key])
+    plt.bar(range(n_classes), fp_sorted, align='center', color='crimson', label='False Predictions')
+    plt.bar(range(n_classes), tp_sorted, align='center', color='forestgreen', label='True Predictions', bottom=fp_sorted)
+    # add legend
     plt.legend(loc='best')
-  # write classes in x axis "vertically"
-  if n_classes <= 30:
-    plt.xticks(range(n_classes), sorted_keys, rotation='vertical', fontsize=12)
+    # write number on top of bar
+    for i, val in enumerate(sorted_values):
+      if i == 0:
+        # re-scale plot to write numbers on top
+        axes = plt.gca() # get current axes
+        axes.set_ylim([0,sorted_values[0]*1.25]) # this only works since the first value is the largest
+      fp_val = fp_sorted[i]
+      y_fp_offset = len(str(fp_val) * 7) # 'hack' to find top offset
+      tp_val = tp_sorted[i]
+      y_tp_offset = y_fp_offset + len(str(tp_val)) * 7 + 2
+      # (- 0.4) because the bar's default width=0.8
+      plt.annotate(str(fp_val), xy=(i - 0.4,val), color='crimson', fontweight='bold', rotation='vertical', xytext=(0,y_fp_offset), textcoords='offset points')
+      plt.annotate(str(tp_val), xy=(i - 0.4,val), color='forestgreen', fontweight='bold', rotation='vertical', xytext=(0,y_tp_offset), textcoords='offset points')
   else:
-    # if there are more than 30 classes we need to use the default font size
-    #   otherwise the labels start to overlap each other
-    plt.xticks(range(n_classes), sorted_keys, rotation='vertical')
-  # set window title
-  fig = plt.gcf() # gcf - get current figure
-  fig.canvas.set_window_title(window_title)
+    plt.bar(range(n_classes), sorted_values, align='center', color=plot_color)
+    # write number on top of bar
+    for i, val in enumerate(sorted_values):
+      if i == 0:
+        # re-scale plot to write numbers on top
+        axes = plt.gca() # get current axes
+        axes.set_ylim([0,sorted_values[0]*1.25]) # this only works since the first value is the largest
+      str_val = str(val)
+      if val <= 1.0:
+        str_val = "{0:.2f}".format(val)
+      y_offset = len(str_val) * 7 # 'hack' to find top offset
+      # (- 0.4) because the bar's default width=0.8
+      plt.annotate(str_val, xy=(i - 0.4,val), color=plot_color, fontweight='bold', rotation='vertical', xytext=(0,y_offset), textcoords='offset points')
+
+  # write classes in x axis "vertically"
+  plt.xticks(range(n_classes), sorted_keys, rotation='vertical')
   # set plot title
   plt.title(plot_title, fontsize=14)
   # set axis titles
@@ -612,7 +638,7 @@ def draw_plot_func(dictionary, n_classes, window_title, plot_title, y_label, out
   output_path = results_files_path + "/Predicted Objects Info.png"
   to_show = False
   plot_color = 'forestgreen'
-  stack_bar = count_true_positives
+  true_p_bar = count_true_positives
   draw_plot_func(
     pred_counter_per_class,
     len(pred_counter_per_class),
@@ -622,7 +648,7 @@ def draw_plot_func(dictionary, n_classes, window_title, plot_title, y_label, out
     output_path,
     to_show,
     plot_color,
-    stack_bar
+    true_p_bar
     )
 
 """