maixhub client now send result with maix protocol(uart by default)

Author: Neutree

examples/protocol/comm_protocol_yolov5.py

@@ -17,11 +17,11 @@
 def encode_objs(objs):
     '''
         encode objs info to bytes body for protocol
-        2B x(LE) + 2B y(LE) + 2B w(LE) + 2B h(LE) + 2B idx ...
+        2B x(LE) + 2B y(LE) + 2B w(LE) + 2B h(LE) + 2B idx + 4B score(float) ...
     '''
     body = b''
     for obj in objs:
-        body += struct.pack("<hhHHH", obj.x, obj.y, obj.w, obj.h, obj.class_id)
+        body += struct.pack("<hhHHHf", obj.x, obj.y, obj.w, obj.h, obj.class_id, obj.score)
     return body
 
 def decode_objs(body):

projects/app_maixhub_client/app.yaml

@@ -1,7 +1,7 @@
 id: maixhub
 name: MaixHub Client
 name[zh]: MaixHub 客户端
-version: 1.0.2
+version: 1.1.0
 author: Sipeed
 icon: app.png
 desc: MaixHub client to collect image or deploy AI model etc.

projects/app_maixhub_client/maixhub/demos/classifier.py

@@ -1,22 +1,39 @@
-from maix import nn, image
-
+from maix import nn, image, comm
+import struct
 
 class Demo:
     def __init__(self, model_path):
         self.model_path = model_path
         self.classifier = None
+        self.p = None
+        self.APP_CMD_CLASSIFY_RES = 0x02
+
+    def encode_objs(self, res):
+        '''
+            encode objs info to bytes body for protocol
+            2B max idx, 4B prob, 2B second idx, 4B prob, 2B third idx, 4B prob
+        '''
+        body = b''
+        for obj in res:
+            body += struct.pack("<Hf", obj[0], obj[1])
+        return body
 
     def init(self):
         # camera.config(size=self.input_size)
         print("-- load model:", self.model_path)
         self.classifier = nn.Classifier(model=self.model_path, dual_buff = True)
         print("-- load ok")
+        self.p = comm.CommProtocol(buff_size = 1024)
 
     def input_size(self):
         return self.classifier.input_size()
 
     def loop(self, img, dis_img, key):
         res = self.classifier.classify(img)
+
+        body = self.encode_objs(res)
+        self.p.report(self.APP_CMD_CLASSIFY_RES, body)
+
         max_idx, max_prob = res[0]
         msg = "{:.2f}: {}".format(max_prob, self.classifier.labels[max_idx])
         h = image.string_size("A", scale = 2, thickness = 2)[1]

projects/app_maixhub_client/maixhub/demos/yolov5.py

@@ -1,18 +1,38 @@
-from maix import nn, image
+from maix import nn, image, comm
+import struct
 
 class Demo:
     def __init__(self, model_path):
         self.model_path = model_path
         self.detector = None
+        self.p = None
+        self.APP_CMD_DETECT_RES = 0x02
+        self.report_on = True
+
+    def encode_objs(self, objs):
+        '''
+            encode objs info to bytes body for protocol
+            2B x(LE) + 2B y(LE) + 2B w(LE) + 2B h(LE) + 2B idx + 4B score(float) ...
+        '''
+        body = b''
+        for obj in objs:
+            body += struct.pack("<hhHHHf", obj.x, obj.y, obj.w, obj.h, obj.class_id, obj.score)
+        return body
 
     def init(self):
         self.detector = nn.YOLOv5(model=self.model_path, dual_buff = True)
+        self.p = comm.CommProtocol(buff_size = 1024)
 
     def input_size(self):
         return self.detector.input_size()
 
     def loop(self, img, dis_img, key):
         objs = self.detector.detect(img, conf_th = 0.5, iou_th = 0.45)
+
+        if len(objs) > 0 and self.report_on:
+            body = self.encode_objs(objs)
+            self.p.report(self.APP_CMD_DETECT_RES, body)
+
         for obj in objs:
             rect = image.resize_map_pos(img.width(), img.height(), dis_img.width(), dis_img.height(), image.Fit.FIT_CONTAIN, obj.x, obj.y, obj.w, obj.h)
             str_h = image.string_size(self.detector.labels[obj.class_id])[1]