fix: designs fixed

.ipynb_checkpoints/app-checkpoint.ipynb

@@ -149,7 +149,7 @@
     "no_sequences = 1200\n",
     "\n",
     "# no of frames in each video\n",
-    "#sequence_length = 30\n",
+    "sequence_length = 30\n",
     "\n",
     "label_map = {label:num for num, label in enumerate(actions)}\n",
     "#label_map\n",
@@ -171,7 +171,7 @@
    "outputs": [],
    "source": [
     "# change the .h5 file with the one you saved\n",
-    "model = load_model('models/weights_custom.h5')"
+    "model = load_model('weights_custom.h5')"
    ]
   },
   {
@@ -280,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "b1950407",
    "metadata": {
     "scrolled": true
@@ -294,8 +294,6 @@
     "predictions = []\n",
     "lst = []\n",
     "threshold = 0.5\n",
-    "num_of_frames = 30\n",
-    "batch_size = 16\n",
     "\n",
     "cap = cv2.VideoCapture(0)\n",
     "# Set mediapipe model \n",
@@ -318,10 +316,10 @@
     "        kp = extract_keypoints(results)\n",
     "        sequence.append(kp)\n",
     "        seq.append(keypoints)\n",
-    "        sequence = sequence[-1*num_of_frames:]\n",
-    "        seq = seq[-1*batch_size:]\n",
+    "        sequence = sequence[-1*sequence_length:]\n",
+    "        seq = seq[-1*sequence_length:]\n",
     "        \n",
-    "        if len(sequence) == num_of_frames:\n",
+    "        if len(sequence) == sequence_length:\n",
     "            res = model.predict(np.expand_dims(seq, axis=0))[0]\n",
     "            #print(actions[np.argmax(res)])\n",
     "\n",