Merge branch 'dev' into 'main'

Release Sprint 2

Closes WES-41

See merge request wesign/sign-predictor!11

.drone.yml

@@ -7,7 +7,7 @@ steps:
     pull: if-not-exists
     image: sonarsource/sonar-scanner-cli
     commands:
-      - sonar-scanner -Dsonar.host.url=$SONAR_HOST -Dsonar.login=$SONAR_TOKEN -Dsonar.projectKey=$SONAR_PROJECT_KEY
+      - sonar-scanner -Dsonar.host.url=$SONAR_HOST -Dsonar.login=$SONAR_TOKEN -Dsonar.projectKey=$SONAR_PROJECT_KEY -Dsonar.qualitygate.wait=true
     environment:
       SONAR_HOST:
         from_secret: sonar_host

.gitignore

@@ -6,3 +6,6 @@ cache/
 cache_wlasl/
 
 __pycache__/
+
+checkpoints/
+.ipynb_checkpoints

export.py

@@ -0,0 +1,31 @@
+import torch
+import torchvision
+import onnx
+import numpy as np
+
+from src.model import SPOTER
+from src.identifiers import LANDMARKS
+
+model_name = 'Fingerspelling_AE'
+
+# load PyTorch model from .pth file
+model = SPOTER(num_classes=5, hidden_dim=len(LANDMARKS) *2)
+state_dict = torch.load('models/' + model_name + '.pth')
+model.load_state_dict(state_dict)
+
+# set model to evaluation mode
+model.eval()
+
+# create dummy input tensor
+batch_size = 1
+num_of_frames = 1
+input_shape = (108, num_of_frames)
+dummy_input = torch.randn(batch_size, *input_shape)
+
+# export model to ONNX format
+output_file = 'models/' + model_name + '.onnx'
+torch.onnx.export(model, dummy_input, output_file, input_names=['input'], output_names=['output'])
+
+# load exported ONNX model for verification
+onnx_model = onnx.load(output_file)
+onnx.checker.check_model(onnx_model)

requirements.txt

@@ -2,4 +2,5 @@ torch==1.13.1
 torchvision==0.14.1
 pandas==1.5.3
 mediapipe==0.9.1.0
-tensorboard==2.12.0
+tensorboard==2.12.0
+mediapy==1.1.6

src/augmentations.py

@@ -0,0 +1,11 @@
+import random
+
+
+class MirrorKeypoints:
+    def __call__(self, sample):    
+        if random.random() > 0.5:
+            return sample
+        # flip the keypoints tensor
+        sample = 1 -  sample
+
+        return sample

src/datasets/finger_spelling_dataset.py

@@ -33,7 +33,7 @@ class FingerSpellingDataset(torch.utils.data.Dataset):
         # TODO: make split for train and val and test when enough data is available
 
         # split the data into train and val and test and make them balanced
-        x_train, x_test, y_train, y_test = train_test_split(files, labels, test_size=0.4, random_state=1, stratify=labels)
+        x_train, x_test, y_train, y_test = train_test_split(files, labels, test_size=0.3, random_state=1, stratify=labels)
     
         if subset == "train":
             self.data = x_train
@@ -57,7 +57,7 @@ class FingerSpellingDataset(torch.utils.data.Dataset):
         video_name = self.data[index]
 
         # get the keypoints for the video
-        keypoints_df = self.keypoint_extractor.extract_keypoints_from_video(video_name)
+        keypoints_df = self.keypoint_extractor.extract_keypoints_from_video(video_name, normalize="minxmax")
 
         # filter the keypoints by the identified subset
         if self.keypoints_to_keep:
@@ -73,4 +73,7 @@ class FingerSpellingDataset(torch.utils.data.Dataset):
         # data to tensor
         data = torch.from_numpy(current_row)
 
+        if self.transform:
+            data = self.transform(data)
+
         return data, label

src/datasets/wlasl_dataset.py

@@ -4,8 +4,8 @@ from collections import OrderedDict
 import numpy as np
 import torch
 
-from identifiers import LANDMARKS
-from keypoint_extractor import KeypointExtractor
+from src.identifiers import LANDMARKS
+from src.keypoint_extractor import KeypointExtractor
 
 
 class WLASLDataset(torch.utils.data.Dataset):

src/keypoint_extractor.py

@@ -27,27 +27,36 @@ class KeypointExtractor:
 
     def extract_keypoints_from_video(self,
                                  video: str,
+                                 normalize: str = None,
+                                 draw: bool = False,
                                 ) -> pd.DataFrame:
         """extract_keypoints_from_video this function extracts keypoints from a video and stores them in a dataframe
 
         :param video: the video to extract keypoints from
         :type video: str
-        :return: dataframe with keypoints
+        :param normalize: the hand normalization algorithm to use, defaults to None
+        :type normalize: str, optional
+        :return: dataframe with keypoints in absolute pixels
         :rtype: pd.DataFrame
         """
-        # check if video exists
-        if not os.path.exists(self.video_folder + video):
-            logging.error("Video does not exist at path: " + self.video_folder + video)
-            return None
 
-        # check if cache exists
-        if not os.path.exists(self.cache_folder):
-            os.makedirs(self.cache_folder)
+        if not draw:
+            # check if video exists
+            if not os.path.exists(self.video_folder + video):
+                logging.error("Video does not exist at path: " + self.video_folder + video)
+                return None
 
-        # check if cache file exists and return
-        if os.path.exists(self.cache_folder + "/" + video + ".npy"):
-            # create dataframe from cache
-            return pd.DataFrame(np.load(self.cache_folder + "/" + video + ".npy", allow_pickle=True), columns=self.columns)
+            # check if cache exists
+            if not os.path.exists(self.cache_folder):
+                os.makedirs(self.cache_folder)
+
+            # check if cache file exists and return
+            if os.path.exists(self.cache_folder + "/" + video + ".npy"):
+                # create dataframe from cache
+                df = pd.DataFrame(np.load(self.cache_folder + "/" + video + ".npy", allow_pickle=True), columns=self.columns)
+                if normalize:
+                    df = self.normalize_hands(df, norm_algorithm=normalize)
+                return df
 
         # open video
         cap = cv2.VideoCapture(self.video_folder + video)
@@ -56,7 +65,9 @@ class KeypointExtractor:
 
         # extract frames from video so we extract 5 frames per second
         frame_rate = int(cap.get(cv2.CAP_PROP_FPS))
-        frame_skip = frame_rate // 5
+        frame_skip = frame_rate // 10
+
+        output_frames = []
 
         while cap.isOpened():
 
@@ -70,7 +81,11 @@ class KeypointExtractor:
             if not success:
                 break
             # extract keypoints of frame
-            results = self.extract_keypoints_from_frame(image)
+            if draw:
+                results, draw_image = self.extract_keypoints_from_frame(image, draw=True)
+                output_frames.append(draw_image)
+            else:
+                results = self.extract_keypoints_from_frame(image)
 
             def extract_keypoints(landmarks):
                 if landmarks:
@@ -80,8 +95,18 @@ class KeypointExtractor:
             k1 = extract_keypoints(results.pose_landmarks)
             k2 = extract_keypoints(results.left_hand_landmarks)
             k3 = extract_keypoints(results.right_hand_landmarks)
-            if k1 and k2 and k3:
-                keypoints_df = pd.concat([keypoints_df, pd.DataFrame([k1+k2+k3], columns=self.columns)])
+            if k1 and (k2 or k3):
+                data = [k1 + (k2 or [0] * 42) + (k3 or [0] * 42)]
+                new_df = pd.DataFrame(data, columns=self.columns)
+                keypoints_df = pd.concat([keypoints_df, new_df], ignore_index=True)
+        
+        # get frame width and height
+        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+        # convert to pixels
+        keypoints_df.iloc[:, ::2] *= frame_width
+        keypoints_df.iloc[:, 1::2] *= frame_height
 
         # close video
         cap.release()
@@ -89,6 +114,12 @@ class KeypointExtractor:
         # save keypoints to cache
         np.save(self.cache_folder + "/" + video + ".npy", keypoints_df.to_numpy())
 
+        if normalize:
+            keypoints_df = self.normalize_hands(keypoints_df, norm_algorithm=normalize)
+
+        if draw:
+            return keypoints_df, output_frames
+    
         return keypoints_df
 
 
@@ -108,11 +139,156 @@ class KeypointExtractor:
         if draw:
             # Draw the pose annotations on the image
             draw_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            self.mp_drawing.draw_landmarks(draw_image, results.face_landmarks, self.mp_holistic.FACEMESH_CONTOURS)
+            # self.mp_drawing.draw_landmarks(draw_image, results.face_landmarks, self.mp_holistic.FACEMESH_CONTOURS)
             self.mp_drawing.draw_landmarks(draw_image, results.left_hand_landmarks, self.mp_holistic.HAND_CONNECTIONS)
             self.mp_drawing.draw_landmarks(draw_image, results.right_hand_landmarks, self.mp_holistic.HAND_CONNECTIONS)
+
+            img_width, img_height = image.shape[1], image.shape[0]
+            
+            # create bounding box around hands
+            if results.left_hand_landmarks:
+                x = [landmark.x for landmark in results.left_hand_landmarks.landmark]
+                y = [landmark.y for landmark in results.left_hand_landmarks.landmark]
+                draw_image = cv2.rectangle(draw_image, (int(min(x) * img_width), int(min(y) * img_height)), (int(max(x) * img_width), int(max(y) * img_height)), (0, 255, 0), 2)
+
+            if results.right_hand_landmarks:
+                x = [landmark.x for landmark in results.right_hand_landmarks.landmark]
+                y = [landmark.y for landmark in results.right_hand_landmarks.landmark]
+                draw_image = cv2.rectangle(draw_image, (int(min(x) * img_width), int(min(y) * img_height)), (int(max(x) * img_width), int(max(y) * img_height)), (255, 0, 0), 2)
+            
             self.mp_drawing.draw_landmarks(draw_image, results.pose_landmarks, self.mp_holistic.POSE_CONNECTIONS)
 
             return results, draw_image
         
-        return results
+        return results
+
+
+    def normalize_hands(self, dataframe: pd.DataFrame, norm_algorithm: str="minmax") -> pd.DataFrame:
+        """normalize_hand this function normalizes the hand keypoints of a dataframe
+
+        :param dataframe: the dataframe to normalize
+        :type dataframe: pd.DataFrame
+        :param norm_algorithm: the normalization algorithm to use, pick from "minmax" and "bohacek"
+        :type norm_algorithm: str
+        :return: the normalized dataframe
+        :rtype: pd.DataFrame
+        """
+
+        if norm_algorithm == "minmax":
+            # normalize left hand
+            dataframe = self.normalize_hand_minmax(dataframe, "left_hand")
+            # normalize right hand
+            dataframe = self.normalize_hand_minmax(dataframe, "right_hand")
+        elif norm_algorithm == "bohacek":
+            # normalize left hand
+            dataframe = self.normalize_hand_bohacek(dataframe, "left_hand")
+            # normalize right hand
+            dataframe = self.normalize_hand_bohacek(dataframe, "right_hand")
+        else:
+            return dataframe
+
+        return dataframe
+    
+    def normalize_hand_minmax(self, dataframe: pd.DataFrame, hand: str) -> pd.DataFrame:
+        """normalize_hand_helper this function normalizes the hand keypoints of a dataframe with respect to the minimum and maximum coordinates
+
+        :param dataframe: the dataframe to normalize
+        :type dataframe: pd.DataFrame
+        :param hand: the hand to normalize
+        :type hand: str
+        :return: the normalized dataframe
+        :rtype: pd.DataFrame
+        """
+        # get all columns that belong to the hand (left hand column 66 - 107, right hand column 108 - 149)
+        hand_columns = np.array([i for i in range(66 + (42 if hand == "right_hand" else 0), 108 + (42 if hand == "right_hand" else 0))])
+        
+        # get the x, y coordinates of the hand keypoints
+        hand_coords = dataframe.iloc[:, hand_columns].values.reshape(-1, 21, 2)
+        
+        # get the min and max x, y coordinates of the hand keypoints
+        min_x, min_y = np.min(hand_coords[:, :, 0], axis=1), np.min(hand_coords[:, :, 1], axis=1)
+        max_x, max_y = np.max(hand_coords[:, :, 0], axis=1), np.max(hand_coords[:, :, 1], axis=1)
+        
+        # calculate the center of the hand keypoints
+        center_x, center_y = (min_x + max_x) / 2, (min_y + max_y) / 2
+        
+        # calculate the width and height of the bounding box around the hand keypoints
+        bbox_width, bbox_height = max_x - min_x, max_y - min_y
+        
+        # repeat the center coordinates and bounding box dimensions to match the shape of hand_coords (numpy magic)
+        center_x, center_y = center_x.reshape(-1, 1, 1), center_y.reshape(-1, 1, 1)
+        center_coords = np.concatenate((np.tile(center_x, (1, 21, 1)), np.tile(center_y, (1, 21, 1))), axis=2)
+        
+        bbox_width, bbox_height = bbox_width.reshape(-1, 1, 1), bbox_height.reshape(-1, 1 ,1)
+        bbox_dims = np.concatenate((np.tile(bbox_width, (1, 21, 1)), np.tile(bbox_height, (1, 21, 1))), axis=2)
+        
+        if np.any(bbox_dims == 0):
+            return dataframe
+        # normalize the hand keypoints based on the bounding box around the hand
+        norm_hand_coords = (hand_coords - center_coords) / bbox_dims
+        
+        # flatten the normalized hand keypoints array and replace the original hand keypoints with the normalized hand keypoints in the dataframe
+        dataframe.iloc[:, hand_columns] = norm_hand_coords.reshape(-1, 42)
+        
+        return dataframe
+    
+    def normalize_hand_bohacek(self, dataframe: pd.DataFrame, hand: str) -> pd.DataFrame:
+        """normalize_hand_helper this function normalizes the hand keypoints of a dataframe using the bohacek normalization algorithm
+
+        :param dataframe: the dataframe to normalize
+        :type dataframe: pd.DataFrame
+        :param hand: the hand to normalize
+        :type hand: str
+        :return: the normalized dataframe
+        :rtype: pd.DataFrame
+        """
+        # get all columns that belong to the hand (left hand column 66 - 107, right hand column 108 - 149)
+        hand_columns = np.array([i for i in range(66 + (42 if hand == "right_hand" else 0), 108 + (42 if hand == "right_hand" else 0))])
+        
+        # get the x, y coordinates of the hand keypoints
+        hand_coords = dataframe.iloc[:, hand_columns].values.reshape(-1, 21, 2)
+        
+        # get the min and max x, y coordinates of the hand keypoints
+        min_x, min_y = np.min(hand_coords[:, :, 0], axis=1), np.min(hand_coords[:, :, 1], axis=1)
+        max_x, max_y = np.max(hand_coords[:, :, 0], axis=1), np.max(hand_coords[:, :, 1], axis=1)
+
+        # calculate the hand keypoint width and height (NOT the bounding box width and height!)
+        width, height = max_x - min_x, max_y - min_y
+
+        # initialize empty arrays for deltas
+        delta_x = np.zeros(width.shape, dtype='float64')
+        delta_y = np.zeros(height.shape, dtype='float64')
+        
+        # calculate the deltas
+        mask = width>height
+        # width > height
+        delta_x[mask] = (0.1 * width)[mask]
+        delta_y[mask] = (delta_x + ((width - height) / 2))[mask]
+        # height >= width
+        delta_y[~mask] = (0.1 * height)[~mask]
+        delta_x[~mask] = (delta_y + ((height - width) / 2))[~mask]
+
+        # Set the starting and ending point of the normalization bounding box
+        starting_x, starting_y = min_x - delta_x, min_y - delta_y
+        ending_x, ending_y = max_x + delta_x, max_y + delta_y
+
+        # calculate the center of the bounding box and the bounding box dimensions
+        bbox_center_x, bbox_center_y = (starting_x + ending_x) / 2, (starting_y + ending_y) / 2
+        bbox_width, bbox_height = ending_x - starting_x, ending_y - starting_y
+
+        # repeat the center coordinates and bounding box dimensions to match the shape of hand_coords
+        bbox_center_x, bbox_center_y = bbox_center_x.reshape(-1, 1, 1), bbox_center_y.reshape(-1, 1, 1)
+        center_coords = np.concatenate((np.tile(bbox_center_x, (1, 21, 1)), np.tile(bbox_center_y, (1, 21, 1))), axis=2)
+        
+        bbox_width, bbox_height = bbox_width.reshape(-1, 1, 1), bbox_height.reshape(-1, 1 ,1)
+        bbox_dims = np.concatenate((np.tile(bbox_width, (1, 21, 1)), np.tile(bbox_height, (1, 21, 1))), axis=2)
+        
+        if np.any(bbox_dims == 0):
+            return dataframe
+        # normalize the hand keypoints based on the bounding box around the hand
+        norm_hand_coords = (hand_coords - center_coords) / bbox_dims
+        
+        # flatten the normalized hand keypoints array and replace the original hand keypoints with the normalized hand keypoints in the dataframe
+        dataframe.iloc[:, hand_columns] = norm_hand_coords.reshape(-1, 42)
+        
+        return dataframe

src/train.py

@@ -13,10 +13,12 @@ import torch.optim as optim
 from torch.utils.data import DataLoader
 from torchvision import transforms
 
-from datasets.wlasl_dataset import WLASLDataset
-from identifiers import LANDMARKS
-from keypoint_extractor import KeypointExtractor
-from model import SPOTER
+from src.augmentations import MirrorKeypoints
+from src.datasets.finger_spelling_dataset import FingerSpellingDataset
+from src.datasets.wlasl_dataset import WLASLDataset
+from src.identifiers import LANDMARKS
+from src.keypoint_extractor import KeypointExtractor
+from src.model import SPOTER
 
 
 def train():
@@ -32,30 +34,28 @@ def train():
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    spoter_model = SPOTER(num_classes=100, hidden_dim=len(LANDMARKS) *2)
+    spoter_model = SPOTER(num_classes=12, hidden_dim=len(LANDMARKS) *2)
     spoter_model.train(True)
     spoter_model.to(device)
 
     criterion = nn.CrossEntropyLoss()
-    optimizer = optim.SGD(spoter_model.parameters(), lr=0.001, momentum=0.9)
+    optimizer = optim.SGD(spoter_model.parameters(), lr=0.0001, momentum=0.9)
     scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.1, patience=5)
 
     # TODO: create paths for checkpoints
 
     # TODO: transformations + augmentations
 
-    k = KeypointExtractor("data/videos/")
+    k = KeypointExtractor("data/fingerspelling/data/")
 
-    train_set = WLASLDataset("data/nslt_100.json", "data/missing.txt", k, keypoints_identifier=LANDMARKS, subset="train")
+    transform = transforms.Compose([MirrorKeypoints()])
+
+    train_set = FingerSpellingDataset("data/fingerspelling/data/", k, keypoints_identifier=LANDMARKS, subset="train", transform=transform)
     train_loader = DataLoader(train_set, shuffle=True, generator=g)
     
-    val_set = WLASLDataset("data/nslt_100.json", "data/missing.txt", k, keypoints_identifier=LANDMARKS, subset="val")
+    val_set = FingerSpellingDataset("data/fingerspelling/data/", k, keypoints_identifier=LANDMARKS, subset="val")
     val_loader = DataLoader(val_set, shuffle=True, generator=g)
-
-    test_set = WLASLDataset("data/nslt_100.json", "data/missing.txt", k, keypoints_identifier=LANDMARKS, subset="test")
-    test_loader = DataLoader(test_set, shuffle=True, generator=g)
-
-
+    
     train_acc, val_acc = 0, 0
     lr_progress = []
     top_train_acc, top_val_acc = 0, 0
@@ -81,32 +81,39 @@ def train():
             if int(torch.argmax(torch.nn.functional.softmax(outputs, dim=2))) == int(labels[0]):
                 pred_correct += 1
             pred_all += 1
-
-            if i % 100 == 0:
-                print(f"Epoch: {epoch} | Batch: {i} | Loss: {running_loss.item()} | Train Acc: {(pred_correct / pred_all)}")
-
+            
         if scheduler:
             scheduler.step(running_loss.item() / len(train_loader))
 
-        # validate
+        # validate and print val acc
+        val_pred_correct, val_pred_all = 0, 0
         with torch.no_grad():
             for i, (inputs, labels) in enumerate(val_loader):
                 inputs = inputs.squeeze(0).to(device)
-                labels = labels.to(device)
+                labels = labels.to(device, dtype=torch.long)
 
-                outputs = spoter_model(inputs)
-                _, predicted = torch.max(outputs.data, 1)
-                val_acc = (predicted == labels).sum().item() / labels.size(0)
+                outputs = spoter_model(inputs).expand(1, -1, -1)
+
+                if int(torch.argmax(torch.nn.functional.softmax(outputs, dim=2))) == int(labels[0]):
+                    val_pred_correct += 1
+                val_pred_all += 1
+        
+        val_acc = (val_pred_correct / val_pred_all)
+
+        print(f"Epoch: {epoch} | Train Acc: {(pred_correct / pred_all)} | Val Acc: {val_acc}")
 
 
         # save checkpoint
-        # if val_acc > top_val_acc:
-        #     top_val_acc = val_acc
-        #     top_train_acc = train_acc
-        #     checkpoint_index = epoch
-        #     torch.save(spoter_model.state_dict(), f"checkpoints/spoter_{epoch}.pth")
+        if val_acc > top_val_acc and epoch > 55:
+            top_val_acc = val_acc
+            top_train_acc = train_acc
+            checkpoint_index = epoch
+            torch.save(spoter_model.state_dict(), f"checkpoints/spoter_{epoch}.pth")
 
-        print(f"Epoch: {epoch} | Train Acc: {train_acc} | Val Acc: {val_acc}")
         lr_progress.append(optimizer.param_groups[0]['lr'])
+    
+    print(f"Best val acc: {top_val_acc} | Best train acc: {top_train_acc} | Epoch: {checkpoint_index}")
 
-train()
+# Path: src/train.py
+if __name__ == "__main__":
+    train()

visualize_data.ipynb

@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.keypoint_extractor import KeypointExtractor\n",
+    "\n",
+    "# reload modules\n",
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "video_name = '69547.mp4' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract keypoints\n",
+    "keypoint_extractor = KeypointExtractor('data/videos/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from IPython.display import HTML\n",
+    "from base64 import b64encode\n",
+    "import mediapy as media\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Define the frames per second (fps) and duration of the video\n",
+    "fps = 25\n",
+    "duration = 10\n",
+    "\n",
+    "# Create a dummy video of random noise\n",
+    "_, video_frames = keypoint_extractor.extract_keypoints_from_video(video_name, normalize=\"minmax\", draw=True)\n",
+    "\n",
+    "# Convert the video to a numpy array\n",
+    "video = np.array(video_frames)\n",
+    "media.show_video(video, height=400, codec='gif', fps=4)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.model import SPOTER\n",
+    "from src.identifiers import LANDMARKS\n",
+    "import torch\n",
+    "\n",
+    "spoter_model = SPOTER(num_classes=5, hidden_dim=len(LANDMARKS) *2)\n",
+    "spoter_model.load_state_dict(torch.load('models/spoter_40.pth'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get average number of frames in test set\n",
+    "from src.keypoint_extractor import KeypointExtractor\n",
+    "from src.datasets.finger_spelling_dataset import FingerSpellingDataset\n",
+    "from src.identifiers import LANDMARKS\n",
+    "import numpy as np\n",
+    "\n",
+    "keypoints_extractor = KeypointExtractor(\"data/fingerspelling/data/\")\n",
+    "test_set = FingerSpellingDataset(\"data/fingerspelling/data/\", keypoints_extractor, keypoints_identifier=LANDMARKS, subset=\"val\")\n",
+    "\n",
+    "frames = []\n",
+    "labels = []\n",
+    "for sample, label in test_set:\n",
+    "    frames.append(sample.shape[0])\n",
+    "    labels.append(label)\n",
+    "\n",
+    "print(np.mean(frames))\n",
+    "# get label frequency in the labels list\n",
+    "from collections import Counter\n",
+    "\n",
+    "counter = Counter(labels)\n",
+    "print(counter)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hand keypoint visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_hand_keypoints(dataframe, hand, frame):\n",
+    "    hand_columns = np.array([i for i in range(66 + (42 if hand == \"right\" else 0), 108 + (42 if hand == \"right\" else 0))])\n",
+    "    \n",
+    "    # get the x, y coordinates of the hand keypoints\n",
+    "    frame_df = dataframe.iloc[frame:frame+1, hand_columns]\n",
+    "    hand_coords = frame_df.values.reshape(21, 2)\n",
+    "    \n",
+    "    x_coords = hand_coords[:, ::2] #Even indices\n",
+    "    y_coords = hand_coords[:, 1::2] #Uneven indices\n",
+    "    \n",
+    "    #Plot the keypoints\n",
+    "    plt.scatter(x_coords, y_coords)\n",
+    "    return frame_df.style"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Set video, hand and frame to display\n",
+    "video_name = '69547.mp4'\n",
+    "hand = \"right\"\n",
+    "frame = 3\n",
+    "%reload_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.keypoint_extractor import KeypointExtractor\n",
+    "import numpy as np\n",
+    "\n",
+    "#Extract keypoints from requested video\n",
+    "keypoints_extractor = KeypointExtractor(\"data/videos/\")\n",
+    "\n",
+    "#Plot the hand keypoints\n",
+    "df = keypoints_extractor.extract_keypoints_from_video(video_name)\n",
+    "df.head()\n",
+    "plot_hand_keypoints(df, hand, frame)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Plot the NORMALIZED hand keypoints (using minxmax)\n",
+    "df = keypoints_extractor.extract_keypoints_from_video(video_name, normalize=\"minmax\")\n",
+    "plot_hand_keypoints(df, hand, frame)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Plot the NORMALIZED hand keypoints (using bohacek)\n",
+    "df = keypoints_extractor.extract_keypoints_from_video(video_name, normalize=\"bohacek\")\n",
+    "plot_hand_keypoints(df, hand, frame)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

webcam_view.py

@@ -0,0 +1,167 @@
+import cv2
+import mediapipe as mp
+import numpy as np
+import torch
+
+from src.identifiers import LANDMARKS
+from src.model import SPOTER
+
+# Initialize MediaPipe Hands model
+holistic = mp.solutions.holistic.Holistic(
+            min_detection_confidence=0.5,
+            min_tracking_confidence=0.5,
+            model_complexity=2
+        )
+mp_holistic = mp.solutions.holistic
+mp_drawing = mp.solutions.drawing_utils
+# Initialize video capture object
+cap = cv2.VideoCapture(0)
+
+
+keypoints = []
+
+spoter_model = SPOTER(num_classes=12, hidden_dim=len(LANDMARKS) *2)
+spoter_model.load_state_dict(torch.load('models/spoter_57.pth'))
+
+m = {
+    0: "A",
+    1: "B",
+    2: "C",
+    3: "D",
+    4: "E",
+    5: "F",
+    6: "G",
+    7: "H",
+    8: "I",
+    9: "J",
+    10: "K",
+    11: "L",
+} 
+
+while True:
+    # Read a frame from the webcam
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    # Convert the frame to RGB
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+    # Detect hand landmarks in the frame
+    results = holistic.process(frame)
+
+    def extract_keypoints(landmarks):
+        if landmarks:
+            return [i for landmark in landmarks.landmark for i in [landmark.x, landmark.y]]
+
+    k1 = extract_keypoints(results.pose_landmarks)
+    k2 = extract_keypoints(results.left_hand_landmarks)
+    k3 = extract_keypoints(results.right_hand_landmarks)
+
+    if k1 and (k2 or k3):
+        data = np.array([k1 + (k2 or [0] * 42) + (k3 or [0] * 42)])
+        
+        def normalize_hand(frame, data, hand, algorithm="minmax"):
+            hand_columns = np.array([i for i in range(66 + (42 if hand == "right_hand" else 0), 108 + (42 if hand == "right_hand" else 0))])
+            hand_data = np.array(data[0])[hand_columns]
+
+            # convert to absolute pixels
+            hand_data = hand_data.reshape(21, 2)
+            hand_data[:, 0] *= frame.shape[1]
+            hand_data[:, 1] *= frame.shape[0]
+
+            min_x, min_y =  np.min(hand_data[:, 0]), np.min(hand_data[:, 1])
+            max_x, max_y = np.max(hand_data[:, 0]), np.max(hand_data[:, 1])
+
+            width, height = max_x - min_x, max_y - min_y
+
+            if algorithm == "minmax":
+                bbox_height, bbox_width = height, width
+                center_x, center_y = (min_x + max_x) / 2, (min_y + max_y) / 2
+
+                starting_x, starting_y = min_x, min_y
+                ending_x, ending_y = max_x, max_y
+                
+            elif algorithm == "bohacek":
+                if width > height:
+                    delta_x = 0.1 * width
+                    delta_y = delta_x + ((width - height) / 2)
+                else:
+                    delta_y = 0.1 * height
+                    delta_x = delta_y + ((height - width) / 2)
+
+                starting_x, starting_y = min_x - delta_x, min_y - delta_y
+                ending_x, ending_y = max_x + delta_x, max_y + delta_y
+                
+                center_x, center_y = (starting_x + ending_x) / 2, (starting_y + ending_y) / 2
+                bbox_height, bbox_width = ending_y - starting_y, ending_x - starting_x
+
+            else: 
+                print("Not a valid normalization algorithm")
+                return data, frame
+
+            if bbox_height == 0 or bbox_width == 0:
+                return data, frame
+            
+            center_coords = np.tile(np.array([center_x, center_y]), (21, 1)).reshape(21, 2)
+            bbox_dims = np.tile(np.array([bbox_width, bbox_height]), (21, 1)).reshape(21, 2)
+        
+            hand_data = (hand_data - center_coords) / bbox_dims
+
+            # add bouding box to frame
+            frame = cv2.rectangle(frame, (int(starting_x), int(starting_y)), (int(ending_x), int(ending_y)), (0, 255, 0), 2)
+
+            data[:, hand_columns] = hand_data.reshape(-1, 42)
+            return data, frame
+
+        norm_alg = "minmax"
+
+        data, frame = normalize_hand(frame, data, "left_hand", norm_alg)
+        data, frame = normalize_hand(frame, data, "right_hand", norm_alg)
+
+        # get values of the landmarks as a list of integers
+        values = []
+        for i in LANDMARKS.values():
+            values.append(i*2)
+            values.append(i*2+1)
+        filtered = np.array(data[0])[np.array(values)]
+
+        while len(keypoints) >= 8:
+            keypoints.pop(0)
+        keypoints.append(filtered)
+        
+        if len(keypoints) == 8:
+            # keypoints to tensor
+            keypoints_tensor = torch.tensor(keypoints).float()
+
+            # predict
+            outputs = spoter_model(keypoints_tensor).expand(1, -1, -1)
+
+            # softmax
+            outputs = torch.nn.functional.softmax(outputs, dim=2)
+
+            # get topk predictions
+            topk = torch.topk(outputs, k=3, dim=2)
+            
+            # show overlay on frame at top right with confidence scores of topk predictions
+            for i, (label, score) in enumerate(zip(topk.indices[0][0], topk.values[0][0])):
+                cv2.putText(frame, f"{m[label.item()]} {score.item():.2f}", (frame.shape[1] - 200, 50 + i * 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+            
+        
+    mp_drawing.draw_landmarks(frame, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
+    mp_drawing.draw_landmarks(frame, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
+    mp_drawing.draw_landmarks(frame, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
+
+    # frame to rgb
+    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+
+    # Show the frame
+    cv2.imshow('MediaPipe Hands', frame)
+
+    # Wait for key press to exit
+    if cv2.waitKey(5) & 0xFF == 27:
+        break
+
+# Release the video capture object and destroy the windows
+cap.release()
+cv2.destroyAllWindows()