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FPS_test.py

@@ -17,20 +17,21 @@ video.py里面测试的FPS会低于该FPS，因为摄像头的读取频率有限
 '''
 class FPS_YOLO(YOLO):
     def get_FPS(self, image, test_interval):
-        # 调整图片使其符合输入要求
-        new_image_size = (self.model_image_size[1],self.model_image_size[0])
-        boxed_image = letterbox_image(image, new_image_size)
+        if self.letterbox_image:
+            boxed_image = letterbox_image(image, (self.model_image_size[1],self.model_image_size[0]))
+        else:
+            boxed_image = image.convert('RGB')
+            boxed_image = boxed_image.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC)
         image_data = np.array(boxed_image, dtype='float32')
         image_data /= 255.
-        image_data = np.expand_dims(image_data, 0)  
+        image_data = np.expand_dims(image_data, 0)
 
         out_boxes, out_scores, out_classes = self.sess.run(
             [self.boxes, self.scores, self.classes],
             feed_dict={
                 self.yolo_model.input: image_data,
                 self.input_image_shape: [image.size[1], image.size[0]],
-                K.learning_phase(): 0
-            })
+                K.learning_phase(): 0})
 
         t1 = time.time()
         for _ in range(test_interval):
@@ -39,8 +40,7 @@ class FPS_YOLO(YOLO):
                 feed_dict={
                     self.yolo_model.input: image_data,
                     self.input_image_shape: [image.size[1], image.size[0]],
-                    K.learning_phase(): 0
-                })
+                    K.learning_phase(): 0})
         t2 = time.time()
         tact_time = (t2 - t1) / test_interval
         return tact_time

get_dr_txt.py

@@ -71,7 +71,7 @@ class mAP_YOLO(YOLO):
         #---------------------------------------------------------#
         boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
                 num_classes, self.input_image_shape, max_boxes = self.max_boxes,
-                score_threshold = self.score, iou_threshold = self.iou)
+                score_threshold = self.score, iou_threshold = self.iou, letterbox_image = self.letterbox_image)
         return boxes, scores, classes
 
     #---------------------------------------------------#
@@ -79,11 +79,11 @@ class mAP_YOLO(YOLO):
     #---------------------------------------------------#
     def detect_image(self, image_id, image):
         f = open("./input/detection-results/"+image_id+".txt","w") 
-        #---------------------------------------------------------#
-        #   给图像增加灰条，实现不失真的resize
-        #---------------------------------------------------------#
-        new_image_size = (self.model_image_size[1],self.model_image_size[0])
-        boxed_image = letterbox_image(image, new_image_size)
+        if self.letterbox_image:
+            boxed_image = letterbox_image(image, (self.model_image_size[1],self.model_image_size[0]))
+        else:
+            boxed_image = image.convert('RGB')
+            boxed_image = boxed_image.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC)
         image_data = np.array(boxed_image, dtype='float32')
         image_data /= 255.
         #---------------------------------------------------------#
@@ -100,7 +100,6 @@ class mAP_YOLO(YOLO):
                 self.yolo_model.input: image_data,
                 self.input_image_shape: [image.size[1], image.size[0]],
                 K.learning_phase(): 0})
-
         for i, c in enumerate(out_classes):
             predicted_class = self.class_names[int(c)]
             score = str(out_scores[i])

nets/yolo4.py

@@ -225,7 +225,7 @@ def yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape):
 #---------------------------------------------------#
 #   获取每个box和它的得分
 #---------------------------------------------------#
-def yolo_boxes_and_scores(feats, anchors, num_classes, input_shape, image_shape):
+def yolo_boxes_and_scores(feats, anchors, num_classes, input_shape, image_shape, letterbox_image):
     #-----------------------------------------------------------------#
     #   将预测值调成真实值
     #   box_xy : -1,13,13,3,2; 
@@ -240,7 +240,23 @@ def yolo_boxes_and_scores(feats, anchors, num_classes, input_shape, image_shape)
     #   我们需要对齐进行修改，去除灰条的部分。
     #   将box_xy、和box_wh调节成y_min,y_max,xmin,xmax
     #-----------------------------------------------------------------#
-    boxes = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape)
+    if letterbox_image:
+        boxes = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape)
+    else:
+        box_yx = box_xy[..., ::-1]
+        box_hw = box_wh[..., ::-1]
+        box_mins = box_yx - (box_hw / 2.)
+        box_maxes = box_yx + (box_hw / 2.)
+
+        input_shape = K.cast(input_shape, K.dtype(box_yx))
+        image_shape = K.cast(image_shape, K.dtype(box_yx))
+
+        boxes =  K.concatenate([
+            box_mins[..., 0:1] * image_shape[0],  # y_min
+            box_mins[..., 1:2] * image_shape[1],  # x_min
+            box_maxes[..., 0:1] * image_shape[0],  # y_max
+            box_maxes[..., 1:2] * image_shape[1]  # x_max
+        ])
     #-----------------------------------------------------------------#
     #   获得最终得分和框的位置
     #-----------------------------------------------------------------#
@@ -258,7 +274,8 @@ def yolo_eval(yolo_outputs,
               image_shape,
               max_boxes=20,
               score_threshold=.6,
-              iou_threshold=.5):
+              iou_threshold=.5,
+              letterbox_image=True):
     #---------------------------------------------------#
     #   获得特征层的数量，有效特征层的数量为3
     #---------------------------------------------------#
@@ -280,7 +297,7 @@ def yolo_eval(yolo_outputs,
     #   对每个特征层进行处理
     #-----------------------------------------------------------#
     for l in range(num_layers):
-        _boxes, _box_scores = yolo_boxes_and_scores(yolo_outputs[l], anchors[anchor_mask[l]], num_classes, input_shape, image_shape)
+        _boxes, _box_scores = yolo_boxes_and_scores(yolo_outputs[l], anchors[anchor_mask[l]], num_classes, input_shape, image_shape, letterbox_image)
         boxes.append(_boxes)
         box_scores.append(_box_scores)
     #-----------------------------------------------------------#

yolo.py

@@ -21,15 +21,20 @@ from utils.utils import letterbox_image
 #--------------------------------------------#
 class YOLO(object):
     _defaults = {
-        "model_path"        : 'model_data/yolo4_weight.h5',
+        "model_path"        : 'model_data/yolo4_voc_weights.h5',
         "anchors_path"      : 'model_data/yolo_anchors.txt',
-        "classes_path"      : 'model_data/coco_classes.txt',
+        "classes_path"      : 'model_data/voc_classes.txt',
         "score"             : 0.5,
         "iou"               : 0.3,
         "max_boxes"         : 100,
         # 显存比较小可以使用416x416
         # 显存比较大可以使用608x608
-        "model_image_size"  : (416, 416)
+        "model_image_size"  : (416, 416),
+        #---------------------------------------------------------------------#
+        #   该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
+        #   在多次测试后，发现关闭letterbox_image直接resize的效果更好
+        #---------------------------------------------------------------------#
+        "letterbox_image"   : False,
     }
 
     @classmethod
@@ -119,19 +124,22 @@ class YOLO(object):
         #---------------------------------------------------------#
         boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
                 num_classes, self.input_image_shape, max_boxes = self.max_boxes,
-                score_threshold = self.score, iou_threshold = self.iou)
+                score_threshold = self.score, iou_threshold = self.iou, letterbox_image = self.letterbox_image)
         return boxes, scores, classes
 
     #---------------------------------------------------#
     #   检测图片
     #---------------------------------------------------#
     def detect_image(self, image):
-        start = timer()
         #---------------------------------------------------------#
         #   给图像增加灰条，实现不失真的resize
+        #   也可以直接resize进行识别
         #---------------------------------------------------------#
-        new_image_size = (self.model_image_size[1],self.model_image_size[0])
-        boxed_image = letterbox_image(image, new_image_size)
+        if self.letterbox_image:
+            boxed_image = letterbox_image(image, (self.model_image_size[1],self.model_image_size[0]))
+        else:
+            boxed_image = image.convert('RGB')
+            boxed_image = boxed_image.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC)
         image_data = np.array(boxed_image, dtype='float32')
         image_data /= 255.
         #---------------------------------------------------------#
@@ -197,8 +205,6 @@ class YOLO(object):
             draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
             del draw
 
-        end = timer()
-        print(end - start)
         return image
 
     def close_session(self):