tensorflow2

keras-yolo3-master/utils/bbox.py

@@ -9,7 +9,7 @@ class BoundBox:
         self.ymin = ymin
         self.xmax = xmax
         self.ymax = ymax
-        
+
         self.c       = c
         self.classes = classes
 
@@ -19,14 +19,14 @@ class BoundBox:
     def get_label(self):
         if self.label == -1:
             self.label = np.argmax(self.classes)
-        
+
         return self.label
-    
+
     def get_score(self):
         if self.score == -1:
             self.score = self.classes[self.get_label()]
-            
-        return self.score      
+
+        return self.score
 
 def _interval_overlap(interval_a, interval_b):
     x1, x2 = interval_a
@@ -41,49 +41,51 @@ def _interval_overlap(interval_a, interval_b):
         if x2 < x3:
              return 0
         else:
-            return min(x2,x4) - x3    
+            return min(x2,x4) - x3
 
 def bbox_iou(box1, box2):
     intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])
-    intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])  
-    
+    intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])
+
     intersect = intersect_w * intersect_h
 
     w1, h1 = box1.xmax-box1.xmin, box1.ymax-box1.ymin
     w2, h2 = box2.xmax-box2.xmin, box2.ymax-box2.ymin
-    
+
     union = w1*h1 + w2*h2 - intersect
-    
+
+    if union == 0: return 0
+
     return float(intersect) / union
 
 def draw_boxes(image, boxes, labels, obj_thresh, quiet=True):
     for box in boxes:
         label_str = ''
         label = -1
-        
+
         for i in range(len(labels)):
             if box.classes[i] > obj_thresh:
                 if label_str != '': label_str += ', '
                 label_str += (labels[i] + ' ' + str(round(box.get_score()*100,0)) + '%')
                 label = i
             if not quiet: print(label_str)
-                
+
         if label >= 0:
             text_size = cv2.getTextSize(label_str, cv2.FONT_HERSHEY_SIMPLEX, 1.1e-4 * image.shape[0], 2)
             width, height = text_size[0][0], text_size[0][1]
-            region = np.array([[box.xmin-3,        box.ymin], 
-                               [box.xmin-3,        box.ymin-height-16], 
-                               [box.xmin+width+6, box.ymin-height-16], 
-                               [box.xmin+width+6, box.ymin]], dtype='int32')  
+            region = np.array([[box.xmin-3,        box.ymin],
+                               [box.xmin-3,        box.ymin-height-16],
+                               [box.xmin+width+6, box.ymin-height-16],
+                               [box.xmin+width+6, box.ymin]], dtype='int32')
 
             cv2.rectangle(img=image, pt1=(box.xmin,box.ymin), pt2=(box.xmax,box.ymax), color=get_color(label), thickness=1)
             cv2.fillPoly(img=image, pts=[region], color=get_color(label))
-            cv2.putText(img=image, 
-                        text=label_str, 
-                        org=(box.xmin+6, box.ymin - 6), 
-                        fontFace=cv2.FONT_HERSHEY_SIMPLEX, 
-                        fontScale=0.7e-3 * image.shape[0], 
-                        color=(0,0,0), 
+            cv2.putText(img=image,
+                        text=label_str,
+                        org=(box.xmin+6, box.ymin - 6),
+                        fontFace=cv2.FONT_HERSHEY_SIMPLEX,
+                        fontScale=0.7e-3 * image.shape[0],
+                        color=(0,0,0),
                         thickness=2)
-        
-    return image          
+
+    return image

keras-yolo3-master/utils/multi_gpu_model.py

@@ -1,5 +1,5 @@
-from keras.layers import Lambda, concatenate
-from keras.models import Model
+from tensorflow.keras.layers import Lambda, concatenate
+from tensorflow.keras.models import Model
 import tensorflow as tf
 
 def multi_gpu_model(model, gpus):
@@ -59,4 +59,4 @@ def multi_gpu_model(model, gpus):
         for name, outputs in zip(model.output_names, all_outputs):
             merged.append(concatenate(outputs,
                                                                 axis=0, name=name))
-        return Model(model.inputs, merged)
+        return Model(model.inputs, merged)

keras-yolo3-master/utils/utils.py

@@ -3,6 +3,7 @@ import numpy as np
 import os
 from .bbox import BoundBox, bbox_iou
 from scipy.special import expit
+import tensorflow as tf
 
 def _sigmoid(x):
     return expit(x)
@@ -166,18 +167,30 @@ def do_nms(boxes, nms_thresh):
                 if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh:
                     boxes[index_j].classes[c] = 0
 
-def decode_netout(netout, anchors, obj_thresh, net_h, net_w):
-    grid_h, grid_w = netout.shape[:2]
+def decode_netout(netout_old, anchors, obj_thresh, net_h, net_w):
+    grid_h, grid_w = netout_old.shape[:2]
     nb_box = 3
-    netout = netout.reshape((grid_h, grid_w, nb_box, -1))
-    nb_class = netout.shape[-1] - 5
+    #netout = netout.reshape((grid_h, grid_w, nb_box, -1))
+    netout_old = tf.reshape(netout_old, (grid_h, grid_w, nb_box, -1))
+    nb_class = netout_old.shape[-1] - 5
 
     boxes = []
+    ## Tensorflow v.2
+    #print(tf.shape(netout))
+    aux_1 = _sigmoid(netout_old[..., :2])
+    #print(tf.shape(aux_1))
+    aux_2 = _sigmoid(netout_old[..., 4])
+    #print(tf.shape(aux_2[..., np.newaxis]))
+    aux_3 = aux_2[..., np.newaxis] * _softmax(netout_old[..., 5:])
+    aux_4 = aux_3 * (aux_3 > obj_thresh)
+    #print(tf.shape(aux_4))
+    netout = tf.concat([aux_1,netout_old[..., 2:4] ,aux_2[..., np.newaxis], aux_4], 3)
+    #print(tf.shape(new_netout))
 
-    netout[..., :2]  = _sigmoid(netout[..., :2])
-    netout[..., 4]   = _sigmoid(netout[..., 4])
-    netout[..., 5:]  = netout[..., 4][..., np.newaxis] * _softmax(netout[..., 5:])
-    netout[..., 5:] *= netout[..., 5:] > obj_thresh
+    #netout[..., :2] = _sigmoid(netout[..., :2])
+    #netout[..., 4]   = _sigmoid(netout[..., 4])
+    #netout[..., 5:]  = netout[..., 4][..., np.newaxis] * _softmax(netout[..., 5:])
+    #netout[..., 5:] *= netout[..., 5:] > obj_thresh
 
     for i in range(grid_h*grid_w):
         row = i // grid_w
@@ -198,7 +211,7 @@ def decode_netout(netout, anchors, obj_thresh, net_h, net_w):
             h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height
 
             # last elements are class probabilities
-            classes = netout[row,col,b,5:]
+            classes = np.array(netout[row,col,b,5:])
 
             box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)