Update app.py

app.py

@@ -299,42 +299,42 @@ class DetectionPredictor(BasePredictor):
           
         outputs = deepsort.update(xywhs, confss, oids, im0)
         
-        with open('/pulse.csv', 'a') as f:
+        #with open('/pulse.csv', 'a') as f:
             # create the csv writer
-                writer = csv.writer(f)
-                if len(outputs) > 0:
-                    bbox_xyxy = outputs[:, :4]
-                    identities = outputs[:, -2]
-                    object_id = outputs[:, -1]
+                #writer = csv.writer(f)
+        if len(outputs) > 0:
+            bbox_xyxy = outputs[:, :4]
+            identities = outputs[:, -2]
+            object_id = outputs[:, -1]
             
-                    f2 = open('/vehicles_data.csv', "w+")
-                    f2.close()
-                    with open('/vehicles_data.csv', 'a') as f:
-                        writer2 = csv.writer(f)
-                        header = ['id', 'class', 'speed', 'weight']
-                        writer2.writerow(header)
-                        draw_boxes(im0, bbox_xyxy, self.model.names, object_id,writer, writer2, identities)
-        df = pd.read_csv("/pulse.csv")
-        df['time'] = pd.to_datetime(df['time'], format = '%H:%M:%S  %d.%m.%Y')
+                    #f2 = open('/vehicles_data.csv', "w+")
+                    #f2.close()
+                    #with open('/vehicles_data.csv', 'a') as f:
+                        #writer2 = csv.writer(f)
+                        #header = ['id', 'class', 'speed', 'weight']
+                        #writer2.writerow(header)
+        img= draw_boxes(im0, bbox_xyxy, self.model.names, object_id,writer, writer2, identities)
+        #df = pd.read_csv("/pulse.csv")
+        #df['time'] = pd.to_datetime(df['time'], format = '%H:%M:%S  %d.%m.%Y')
         
 
-        df.index = df['time']
-        del df['time']
+        #df.index = df['time']
+        #del df['time']
         
-        try:
-            fig, ax = plt.subplots()
+        #try:
+            #fig, ax = plt.subplots()
             #plt.clf()
-            sns.lineplot(df)
+            #sns.lineplot(df)
             #ax.set_xticklabels([t.get_text().split(".")[0] for t in ax.get_xticklabels()])
-            ax.set_xticklabels([pd.to_datetime(t.get_text()).strftime('%H:%M:%S') for t in ax.get_xticklabels()])
-            plt.ylabel('Pulse')
-            plt.xlabel('time')
-            plt.savefig(f'/time_series/figure_{num:010d}.png')
-            num += 1
-        except:
-          log_string += f'An error occured while saving figure_{num:010d}.png, '
+            #ax.set_xticklabels([pd.to_datetime(t.get_text()).strftime('%H:%M:%S') for t in ax.get_xticklabels()])
+            #plt.ylabel('Pulse')
+            #plt.xlabel('time')
+            #plt.savefig(f'/time_series/figure_{num:010d}.png')
+            #num += 1
+        #except:
+          #log_string += f'An error occured while saving figure_{num:010d}.png, '
       
-        return log_string
+        return log_string, img
 
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
@@ -353,10 +353,10 @@ def gradio_wrapper(img):
     #print(np.shape(img))
     return result
 demo = gr.Interface(
-    gradio_wrapper,
+    fn=gradio_wrapper,
     #gr.Image(source="webcam", streaming=True, flip=True),
-    gr.Image(source="webcam", streaming=True),
-    "image",
+    inputs=gr.Image(source="webcam", streaming=True),
+    outputs=img, log_string
     live=True
 )