更新obb的map计算

.gitignore

@@ -6,7 +6,6 @@ datasets/
 Medical_Datasets/
 lfw/
 logs/
-model_data/
 .temp_map_out/
 2007_train.txt
 2007_val.txt

get_map.py

 import os
 import xml.etree.ElementTree as ET
-
+import cv2
 from PIL import Image
 from tqdm import tqdm
 import numpy as np
 from utils.utils import get_classes
 from utils.utils_map import get_coco_map, get_map
-from utils.utils_rbox import poly2hbb
 from yolo import YOLO
 
 if __name__ == "__main__":
@@ -126,16 +125,12 @@ if __name__ == "__main__":
                     x4      = bndbox.find('x4').text
                     y4      = bndbox.find('y4').text
                     poly    = np.array([[x1, y1, x2, y2, x3, y3, x4, y4]], dtype=np.int32)
-                    hbb     = poly2hbb(poly)
-                    xc, yc, w, h = hbb[0]
-                    left   = xc - w/2
-                    top    = yc - h/2
-                    right  = xc + w/2
-                    bottom = yc + h/2
+                    poly    = poly.reshape(4, 2)
+                    (x, y), (w, h), angle = cv2.minAreaRect(poly)  # θ ∈ [0， 90]
                     if difficult_flag:
-                        new_f.write("%s %s %s %s %s difficult\n" % (obj_name, left, top, right, bottom))
+                        new_f.write("%s %s %s %s %s %s difficult\n" % (obj_name, int(x), int(y), int(w), int(h),angle))
                     else:
-                        new_f.write("%s %s %s %s %s\n" % (obj_name, left, top, right, bottom))
+                        new_f.write("%s %s %s %s %s %s\n" % (obj_name, int(x), int(y), int(w), int(h),angle))
         print("Get ground truth result done.")
 
     if map_mode == 0 or map_mode == 3:

model_data/ssdd_classes.txt

+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+diningtable
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush

utils/utils_map.py

@@ -5,6 +5,7 @@ import operator
 import os
 import shutil
 import sys
+
 try:
     from pycocotools.coco import COCO
     from pycocotools.cocoeval import COCOeval
@@ -28,6 +29,24 @@ import numpy as np
                 (Right,Bottom)
 '''
 
+def iou_rotate_calculate(boxes1, boxes2):
+    """
+    计算旋转面积
+    boxes1,boxes2格式为x,y,w,h,theta
+    """
+    area1 = boxes1[2] * boxes1[3]
+    area2 = boxes2[2] * boxes2[3]
+    r1 = ((boxes1[0], boxes1[1]), (boxes1[2], boxes1[3]), boxes1[4])
+    r2 = ((boxes2[0], boxes2[1]), (boxes2[2], boxes2[3]), boxes2[4])
+    int_pts = cv2.rotatedRectangleIntersection(r1, r2)[1]
+    if int_pts is not None:
+        order_pts = cv2.convexHull(int_pts, returnPoints=True)
+        int_area = cv2.contourArea(order_pts)
+        ious = int_area * 1.0 / (area1 + area2 - int_area)
+    else:
+        ious = 0
+    return ious
+
 def log_average_miss_rate(precision, fp_cumsum, num_images):
     """
         log-average miss rate:
@@ -209,7 +228,7 @@ def draw_plot_func(dictionary, n_classes, window_title, plot_title, x_label, out
         """
         fig = plt.gcf() # gcf - get current figure
         axes = plt.gca()
-        r = fig.canvas.get_renderer()
+        r = fig.canvas.manager.get_renderer()
         for i, val in enumerate(sorted_values):
             fp_val = fp_sorted[i]
             tp_val = tp_sorted[i]
@@ -238,7 +257,7 @@ def draw_plot_func(dictionary, n_classes, window_title, plot_title, x_label, out
             if i == (len(sorted_values)-1): # largest bar
                 adjust_axes(r, t, fig, axes)
     # set window title
-    fig.canvas.set_window_title(window_title)
+    fig.canvas.manager.set_window_title(window_title)
     # write classes in y axis
     tick_font_size = 12
     plt.yticks(range(n_classes), sorted_keys, fontsize=tick_font_size)
@@ -328,35 +347,37 @@ def get_map(MINOVERLAP, draw_plot, score_threhold=0.5, path = './map_out'):
         for line in lines_list:
             try:
                 if "difficult" in line:
-                    class_name, left, top, right, bottom, _difficult = line.split()
+                    class_name, x, y, w, h,angle, _difficult = line.split()
                     is_difficult = True
                 else:
-                    class_name, left, top, right, bottom = line.split()
+                    class_name, x, y, w, h,angle = line.split()
             except:
                 if "difficult" in line:
                     line_split  = line.split()
                     _difficult  = line_split[-1]
-                    bottom      = line_split[-2]
-                    right       = line_split[-3]
-                    top         = line_split[-4]
-                    left        = line_split[-5]
+                    angle       = line_split[-2]
+                    h           = line_split[-3]
+                    w           = line_split[-4]
+                    y           = line_split[-5]
+                    x           = line_split[-6]
                     class_name  = ""
-                    for name in line_split[:-5]:
+                    for name in line_split[:-6]:
                         class_name += name + " "
-                    class_name  = class_name[:-1]
+                    class_name   = class_name[:-1]
                     is_difficult = True
                 else:
                     line_split  = line.split()
-                    bottom      = line_split[-1]
-                    right       = line_split[-2]
-                    top         = line_split[-3]
-                    left        = line_split[-4]
+                    angle       = line_split[-1]
+                    h           = line_split[-2]
+                    w           = line_split[-3]
+                    y           = line_split[-4]
+                    x           = line_split[-5]
                     class_name  = ""
-                    for name in line_split[:-4]:
+                    for name in line_split[:-5]:
                         class_name += name + " "
                     class_name = class_name[:-1]
 
-            bbox = left + " " + top + " " + right + " " + bottom
+            bbox = x + " " + y + " " + w + " " + h + " " + angle
             if is_difficult:
                 bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False, "difficult":True})
                 is_difficult = False
@@ -396,21 +417,22 @@ def get_map(MINOVERLAP, draw_plot, score_threhold=0.5, path = './map_out'):
             lines = file_lines_to_list(txt_file)
             for line in lines:
                 try:
-                    tmp_class_name, confidence, left, top, right, bottom = line.split()
+                    tmp_class_name, confidence, x, y, w, h,angle = line.split()
                 except:
                     line_split      = line.split()
-                    bottom          = line_split[-1]
-                    right           = line_split[-2]
-                    top             = line_split[-3]
-                    left            = line_split[-4]
-                    confidence      = line_split[-5]
+                    angle           = line_split[-1]
+                    h               = line_split[-2]
+                    w               = line_split[-3]
+                    y               = line_split[-4]
+                    x               = line_split[-5]
+                    confidence      = line_split[-6]
                     tmp_class_name  = ""
-                    for name in line_split[:-5]:
+                    for name in line_split[:-6]:
                         tmp_class_name += name + " "
                     tmp_class_name  = tmp_class_name[:-1]
 
                 if tmp_class_name == class_name:
-                    bbox = left + " " + top + " " + right + " " +bottom
+                    bbox = x + " " + y + " " + w + " " + h + " " + angle
                     bounding_boxes.append({"confidence":confidence, "file_id":file_id, "bbox":bbox})
 
         bounding_boxes.sort(key=lambda x:float(x['confidence']), reverse=True)
@@ -464,17 +486,13 @@ def get_map(MINOVERLAP, draw_plot, score_threhold=0.5, path = './map_out'):
                 bb          = [float(x) for x in detection["bbox"].split()]
                 for obj in ground_truth_data:
                     if obj["class_name"] == class_name:
-                        bbgt    = [ float(x) for x in obj["bbox"].split() ]
-                        bi      = [max(bb[0],bbgt[0]), max(bb[1],bbgt[1]), min(bb[2],bbgt[2]), min(bb[3],bbgt[3])]
-                        iw      = bi[2] - bi[0] + 1
-                        ih      = bi[3] - bi[1] + 1
-                        if iw > 0 and ih > 0:
-                            ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + (bbgt[2] - bbgt[0]
-                                            + 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih
-                            ov = iw * ih / ua
-                            if ov > ovmax:
-                                ovmax = ov
-                                gt_match = obj
+                        bbgt    = [float(x) for x in obj["bbox"].split() ]
+                        box1    = np.array([bb[0], bb[1], bb[2], bb[3], bb[4]], np.float32)
+                        box2    = np.array([bbgt[0], bbgt[1], bbgt[2], bbgt[3], bbgt[4]], np.float32)
+                        ov      = iou_rotate_calculate(box1, box2)
+                        if ov > ovmax:
+                            ovmax = ov
+                            gt_match = obj
 
                 if show_animation:
                     status = "NO MATCH FOUND!" 
@@ -606,7 +624,7 @@ def get_map(MINOVERLAP, draw_plot, score_threhold=0.5, path = './map_out'):
                 plt.fill_between(area_under_curve_x, 0, area_under_curve_y, alpha=0.2, edgecolor='r')
 
                 fig = plt.gcf()
-                fig.canvas.set_window_title('AP ' + class_name)
+                fig.canvas.manager.set_window_title('AP ' + class_name)
 
                 plt.title('class: ' + text)
                 plt.xlabel('Recall')
@@ -782,7 +800,7 @@ def get_map(MINOVERLAP, draw_plot, score_threhold=0.5, path = './map_out'):
         plot_title = "mAP = {0:.2f}%".format(mAP*100)
         x_label = "Average Precision"
         output_path = RESULTS_FILES_PATH + "/mAP.png"
-        to_show = True
+        to_show = False
         plot_color = 'royalblue'
         draw_plot_func(
             ap_dictionary,

yolo.py

@@ -376,22 +376,17 @@ class YOLO(object):
             top_label   = np.array(results[0][:, 7], dtype = 'int32')
             top_conf    = results[0][:, 5] * results[0][:, 6]
             top_rboxes  = results[0][:, :5]
-            top_polys   = rbox2poly(top_rboxes)
-            top_hbbs    = poly2hbb(top_polys)
         for i, c in list(enumerate(top_label)):
             predicted_class = self.class_names[int(c)]
-            hbb             = top_hbbs[i]
+            obb             = top_rboxes[i]
             score           = str(top_conf[i])
 
-            xc, yc, w, h = hbb
-            left   = xc - w/2
-            top    = yc - h/2
-            right  = xc + w/2
-            bottom = yc + h/2
+            xc, yc, w, h, angle = obb
+
             if predicted_class not in class_names:
                 continue
 
-            f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom))))
+            f.write("%s %s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(xc)), str(int(yc)), str(int(w)), str(int(h)), str(math.degrees(angle))))
 
         f.close()
         return