fix heatmap bugs

Author: bubbliiiing

yolo.py

@@ -208,6 +208,56 @@ def detect_image(self, image, crop = False, count = False):
             del draw
 
         return image
+    
+    def get_FPS(self, image, test_interval):
+        image_shape = np.array(np.shape(image)[0:2])
+        #---------------------------------------------------------#
+        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
+        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
+        #---------------------------------------------------------#
+        image       = cvtColor(image)
+        #---------------------------------------------------------#
+        #   给图像增加灰条，实现不失真的resize
+        #   也可以直接resize进行识别
+        #---------------------------------------------------------#
+        image_data  = resize_image(image, (self.input_shape[1],self.input_shape[0]), self.letterbox_image)
+        #---------------------------------------------------------#
+        #   添加上batch_size维度
+        #---------------------------------------------------------#
+        image_data  = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
+
+        with torch.no_grad():
+            images = torch.from_numpy(image_data)
+            if self.cuda:
+                images = images.cuda()
+            #---------------------------------------------------------#
+            #   将图像输入网络当中进行预测！
+            #---------------------------------------------------------#
+            outputs = self.net(images)
+            outputs = decode_outputs(outputs, self.input_shape)
+            #---------------------------------------------------------#
+            #   将预测框进行堆叠，然后进行非极大抑制
+            #---------------------------------------------------------#
+            results = non_max_suppression(outputs, self.num_classes, self.input_shape, 
+                        image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
+                                                  
+        t1 = time.time()
+        for _ in range(test_interval):
+            with torch.no_grad():
+                #---------------------------------------------------------#
+                #   将图像输入网络当中进行预测！
+                #---------------------------------------------------------#
+                outputs = self.net(images)
+                outputs = decode_outputs(outputs, self.input_shape)
+                #---------------------------------------------------------#
+                #   将预测框进行堆叠，然后进行非极大抑制
+                #---------------------------------------------------------#
+                results = non_max_suppression(outputs, self.num_classes, self.input_shape, 
+                            image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
+                                
+        t2 = time.time()
+        tact_time = (t2 - t1) / test_interval
+        return tact_time
 
     def detect_heatmap(self, image, heatmap_save_path):
         import cv2
@@ -265,56 +315,6 @@ def sigmoid(x):
         plt.savefig(heatmap_save_path, dpi=200)
         print("Save to the " + heatmap_save_path)
         plt.cla()
-        
-    def get_FPS(self, image, test_interval):
-        image_shape = np.array(np.shape(image)[0:2])
-        #---------------------------------------------------------#
-        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
-        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
-        #---------------------------------------------------------#
-        image       = cvtColor(image)
-        #---------------------------------------------------------#
-        #   给图像增加灰条，实现不失真的resize
-        #   也可以直接resize进行识别
-        #---------------------------------------------------------#
-        image_data  = resize_image(image, (self.input_shape[1],self.input_shape[0]), self.letterbox_image)
-        #---------------------------------------------------------#
-        #   添加上batch_size维度
-        #---------------------------------------------------------#
-        image_data  = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
-
-        with torch.no_grad():
-            images = torch.from_numpy(image_data)
-            if self.cuda:
-                images = images.cuda()
-            #---------------------------------------------------------#
-            #   将图像输入网络当中进行预测！
-            #---------------------------------------------------------#
-            outputs = self.net(images)
-            outputs = decode_outputs(outputs, self.input_shape)
-            #---------------------------------------------------------#
-            #   将预测框进行堆叠，然后进行非极大抑制
-            #---------------------------------------------------------#
-            results = non_max_suppression(outputs, self.num_classes, self.input_shape, 
-                        image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
-                                                  
-        t1 = time.time()
-        for _ in range(test_interval):
-            with torch.no_grad():
-                #---------------------------------------------------------#
-                #   将图像输入网络当中进行预测！
-                #---------------------------------------------------------#
-                outputs = self.net(images)
-                outputs = decode_outputs(outputs, self.input_shape)
-                #---------------------------------------------------------#
-                #   将预测框进行堆叠，然后进行非极大抑制
-                #---------------------------------------------------------#
-                results = non_max_suppression(outputs, self.num_classes, self.input_shape, 
-                            image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
-                                
-        t2 = time.time()
-        tact_time = (t2 - t1) / test_interval
-        return tact_time
 
     def convert_to_onnx(self, simplify, model_path):
         import onnx