update

examples/python/hatdet/darknet.py

@@ -27,7 +27,7 @@ class DarknetYolo:
         self.COLORS=COLORS
 
 
-    def getdetresults(self, frame, hasdraw):
+    def getdetresults(self, frame):
         netMain, metaMain, thresh, COLORS = self.netMain, self.metaMain, self.thresh, self.COLORS
         
         # Create a 4D blob from a frame.
@@ -65,19 +65,25 @@ class DarknetYolo:
         # lower confidences.
         idxs = cv2.dnn.NMSBoxes(boxes, confidences, thresh, 0.3)
         if len(idxs) > 0: 
-            if not hasdraw: 
-                return idxs, boxes, confidences, classIDs, metaMain;            
+            lists = [[0 for i in range(0)] for i in range( len(idxs.flatten()) )]
+            k=0          
             for i in idxs.flatten():
                 # get x,y,w,h
                 (x, y) = (boxes[i][0], boxes[i][1])
                 (w, h) = (boxes[i][2], boxes[i][3])
-                # draw rectangle
+                x2 = x + w
+                y2 = y + h
+                lists[k].append(x)
+                lists[k].append(y)
+                lists[k].append(x2)     
+                lists[k].append(y2)   
+                label = metaMain[classIDs[i]]
+                confidence = confidences[i]
                 color = [int(c) for c in COLORS[classIDs[i]]]
-                cv2.rectangle(frame, (x, y), (x + w, y + h), color, 1, lineType=cv2.LINE_AA)
-                text = "{}: {:.4f}".format(metaMain[classIDs[i]], confidences[i])
-                cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,
-                    0.5, color, 1, lineType=cv2.LINE_AA)
-                cv2.imshow("Tag", frame)
-                #cv2.waitKey(0)
+                lists[k].append(label)   
+                lists[k].append(confidence)
+                lists[k].append(color)
+                k=k+1
+            return lists   
         else:
             print("Cann't detect objects,please check image!")

examples/python/hatdet/test_darknet.py

@@ -53,14 +53,23 @@ while cv.waitKey(1) < 0:
         # Release device
         cap.release()
         break
+    detections_lists = []
+    detections_lists = yolo.getdetresults(frame)
+    print(len(detections_lists ))
    
-    idxs, boxes, confidences, classIDs, metaMain = yolo.getdetresults(frame,False)
-    for i in idxs.flatten():
-        (x, y) = (boxes[i][0], boxes[i][1])
-        (w, h) = (boxes[i][2], boxes[i][3])
-        cv.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 255), 1, lineType=cv.LINE_AA)
-        text = "{}: {:.4f}".format(metaMain[classIDs[i]], confidences[i])
-        cv.putText(frame, text, (x, y - 5), cv.FONT_HERSHEY_SIMPLEX,
-                    0.5, (255, 255, 255), 1, lineType=cv.LINE_AA)
-        cv.imshow("Tag", frame)
+    for i in range( len(detections_lists) ):
+        # get x,y,w,h
+        x1 = detections_lists[i][0]
+        y1 = detections_lists[i][1]
+        x2 = detections_lists[i][2]
+        y2 = detections_lists[i][3]
+        label = detections_lists[i][4]
+        confidence = detections_lists[i][5]        
+        color = detections_lists[i][6]
 
+        cv.rectangle(frame, (x1, y1), (x2, y2), color, 1, lineType=cv.LINE_AA)
+        text = "{}: {:.4f}".format(label, confidence )
+        cv.putText(frame, text, (x1, y1 - 5), cv.FONT_HERSHEY_SIMPLEX,
+                    0.5, color, 1, lineType=cv.LINE_AA)
+        cv.imshow("Tag", frame)
+    #print(detections_lists)