Update MobileNet object detection sample

samples/dnn/mobilenet_ssd_python.py

@@ -23,24 +23,25 @@ classNames = ('background',
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--video", help="path to video file. If empty, camera's stream will be used")
-    parser.add_argument("--prototxt", default="MobileNetSSD_300x300.prototxt",
+    parser.add_argument("--prototxt", default="MobileNetSSD_deploy.prototxt",
                         help="path to caffe prototxt")
-    parser.add_argument("-c", "--caffemodel", help="path to caffemodel file, download it here: "
-                             "https://github.com/chuanqi305/MobileNet-SSD/blob/master/MobileNetSSD_train.caffemodel")
+    parser.add_argument("-c", "--caffemodel", default="MobileNetSSD_deploy.caffemodel",
+                        help="path to caffemodel file, download it here: "
+                        "https://github.com/chuanqi305/MobileNet-SSD/")
     parser.add_argument("--thr", default=0.2, help="confidence threshold to filter out weak detections")
     args = parser.parse_args()
 
-    net = dnn.readNetFromCaffe(args.prototxt, args.caffemodel)
+    net = cv.dnn.readNetFromCaffe(args.prototxt, args.caffemodel)
 
     if len(args.video):
-        cap = cv2.VideoCapture(args.video)
+        cap = cv.VideoCapture(args.video)
     else:
-        cap = cv2.VideoCapture(0)
+        cap = cv.VideoCapture(0)
 
     while True:
         # Capture frame-by-frame
         ret, frame = cap.read()
-        blob = dnn.blobFromImage(frame, inScaleFactor, (inWidth, inHeight), meanVal)
+        blob = cv.dnn.blobFromImage(frame, inScaleFactor, (inWidth, inHeight), meanVal)
         net.setInput(blob)
         detections = net.forward()
 
@@ -71,17 +72,17 @@ if __name__ == "__main__":
                 xRightTop   = int(detections[0, 0, i, 5] * cols)
                 yRightTop   = int(detections[0, 0, i, 6] * rows)
 
-                cv2.rectangle(frame, (xLeftBottom, yLeftBottom), (xRightTop, yRightTop),
+                cv.rectangle(frame, (xLeftBottom, yLeftBottom), (xRightTop, yRightTop),
                               (0, 255, 0))
                 label = classNames[class_id] + ": " + str(confidence)
-                labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+                labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
 
-                cv2.rectangle(frame, (xLeftBottom, yLeftBottom - labelSize[1]),
+                cv.rectangle(frame, (xLeftBottom, yLeftBottom - labelSize[1]),
                                      (xLeftBottom + labelSize[0], yLeftBottom + baseLine),
-                                     (255, 255, 255), cv2.FILLED)
-                cv2.putText(frame, label, (xLeftBottom, yLeftBottom),
-                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
+                                     (255, 255, 255), cv.FILLED)
+                cv.putText(frame, label, (xLeftBottom, yLeftBottom),
+                            cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
 
-        cv2.imshow("detections", frame)
-        if cv2.waitKey(1) >= 0:
+        cv.imshow("detections", frame)
+        if cv.waitKey(1) >= 0:
             break

samples/dnn/ssd_mobilenet_object_detection.cpp

@@ -27,12 +27,12 @@ const char* about = "This sample uses Single-Shot Detector "
                     "(https://arxiv.org/abs/1512.02325)"
                     "to detect objects on image.\n"
                     ".caffemodel model's file is avaliable here: "
-                    "https://github.com/chuanqi305/MobileNet-SSD/blob/master/MobileNetSSD_train.caffemodel\n";
+                    "https://github.com/chuanqi305/MobileNet-SSD\n";
 
 const char* params
     = "{ help           | false | print usage         }"
-      "{ proto          | MobileNetSSD_300x300.prototxt | model configuration }"
-      "{ model          |       | model weights       }"
+      "{ proto          | MobileNetSSD_deploy.prototxt | model configuration }"
+      "{ model          | MobileNetSSD_deploy.caffemodel | model weights }"
       "{ video          |       | video for detection }"
       "{ out            |       | path to output video file}"
       "{ min_confidence | 0.2   | min confidence      }";