frontend/__init__.py: allow for data transfer minimization

frontend/__init__.py

@@ -44,7 +44,7 @@ def extract_data(data):
     # get result and rotate 90 deg
     pred_disp = cv2.transpose(np.asarray(data['disp'], dtype='uint8'))
 
-    if input not in data:
+    if 'input' not in data:
         return pred_disp, duration
 
     in_img = np.asarray(data['input'], dtype='uint8').transpose((2, 0, 1))
@@ -72,6 +72,12 @@ def put_image(img_path):
     return data
 
 
+def change_minimal_data(enabled):
+    r = requests.post(f'{API_URL}/params/minimal_data/{not enabled}')
+    cv2.destroyWindow('Input Image')
+    cv2.destroyWindow('Reference Image')
+
+
 if __name__ == '__main__':
     while True:
         for img in os.scandir(img_dir):
@@ -83,14 +89,18 @@ if __name__ == '__main__':
             downsize_input_img()
 
             data = put_image('buffer.png')
+            if 'input' in data:
                 pred_disp, in_img, ref_pat, duration = extract_data(data)
+            else:
+                pred_disp, duration = extract_data(data)
 
             print(f'inference took {duration:1.4f}s')
             print(f'pipeline and transfer took another {(datetime.now() - start).total_seconds() - float(duration):1.4f}s')
             print(f"Pred. Disparity: \n\t{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}\n")
 
+            if 'input' in data:
                 cv2.imshow('Input Image', in_img)
-            # cv2.imshow('Reference Image', ref_pat)
+                cv2.imshow('Reference Image', ref_pat)
             cv2.imshow('Normalized Predicted Disparity', normalize_and_colormap(pred_disp))
             cv2.imshow('Predicted Disparity', pred_disp)
             key = cv2.waitKey()
@@ -99,3 +109,5 @@ if __name__ == '__main__':
                 quit()
             elif key == 101:
                 change_epoch()
+            elif key == 109:
+                change_minimal_data('input' not in data)