frontend/__init__.py: loop endlessly, show more info, allow model change

frontend/__init__.py

@@ -14,6 +14,8 @@ img_dir = '../../usable_imgs/'
 cv2.namedWindow('Input Image')
 cv2.namedWindow('Predicted Disparity')
 
+# epoch 75 ist weird
+
 
 def normalize_and_colormap(img):
     ret = (img - img.min()) / (img.max() - img.min()) * 255.0
@@ -22,31 +24,42 @@ def normalize_and_colormap(img):
     return ret
 
 
-for img in os.scandir(img_dir):
+while True:
-    if 'ir' not in img.path:
+    for img in os.scandir(img_dir):
-        continue
+        start = datetime.now()
-    input_img = cv2.imread(img.path)
+        if 'ir' not in img.path:
-    if input_img.shape == (1024, 1280, 3):
+            continue
-        diff = (512 - 480) // 2
+        input_img = cv2.imread(img.path)
-        downsampled = cv2.pyrDown(input_img)
+        if input_img.shape == (1024, 1280, 3):
-        input_img = downsampled[diff:downsampled.shape[0]-diff, 0:downsampled.shape[1]]
+            diff = (512 - 480) // 2
-
+            downsampled = cv2.pyrDown(input_img)
-    openBin = {'file': ('file', open(img.path, 'rb'), 'image/png')}
+            input_img = downsampled[diff:downsampled.shape[0]-diff, 0:downsampled.shape[1]]
-
+
-    print('sending image')
+        openBin = {'file': ('file', open(img.path, 'rb'), 'image/png')}
-    start = datetime.now()
+
-    r = requests.put(f'{API_URL}/ir', files=openBin)
+        print('sending image')
-    end = datetime.now()
+        r = requests.put(f'{API_URL}/ir', files=openBin)
-    print('received response')
+        print('received response')
-    print(f'processing took {end - start}')
+        r.raise_for_status()
-    r.raise_for_status()
+        data = json.loads(json.loads(r.text))
-
+
-    # FIXME yuck, don't json the json
+        # FIXME yuck, don't json the json
-    pred_disp = np.asarray(json.loads(json.loads(r.text))['disp'], dtype='uint8')
+        pred_disp = np.asarray(data['disp'], dtype='uint8')
-    ref_pat = np.asarray(json.loads(json.loads(r.text))['reference'], dtype='uint8').transpose((2,0,1)).astype('uint8')
+        in_img = np.asarray(data['input'], dtype='uint8').transpose((2,0,1))
-    pred_disp = cv2.transpose(pred_disp)
+        ref_pat = np.asarray(data['reference'], dtype='uint8').transpose((2,0,1)).astype('uint8')
-
+        duration = data['duration']
-    cv2.imshow('Input Image', input_img)
+        pred_disp = cv2.transpose(pred_disp)
-    # cv2.imshow('Reference Image', ref_pat)
+        print(f'inference took {duration}s')
-    cv2.imshow('Predicted Disparity', normalize_and_colormap(pred_disp))
+        print(f'pipeline and transfer took another {(datetime.now() - start).total_seconds() - float(duration)}s\n')
-    cv2.waitKey()
+
+        cv2.imshow('Input Image', in_img)
+        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()
+        if key == 113:
+            quit()
+        elif key == 101:
+            epoch = input('Enter epoch number or "latest"\n')
+            r = requests.post(f'{API_URL}/model/update/{epoch}')
+            print(r.text)