diff --git a/tracker.py b/tracker.py
index 11a7ef3..2bd4a01 100644
--- a/tracker.py
+++ b/tracker.py
@@ -1,74 +1,74 @@
-from deep_sort.utils.parser import get_config
-from deep_sort.deep_sort import DeepSort
-import torch
-import cv2
-
-palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
-cfg = get_config()
-cfg.merge_from_file("deep_sort/configs/deep_sort.yaml")
-deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
-                    max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
-                    nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
-                    max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
-                    use_cuda=True)
-
-
-def plot_bboxes(image, bboxes, line_thickness=None):
-    # Plots one bounding box on image img
-    tl = line_thickness or round(
-        0.002 * (image.shape[0] + image.shape[1]) / 2) + 1  # line/font thickness
-    for (x1, y1, x2, y2, cls_id, pos_id) in bboxes:
-        if cls_id in ['smoke', 'phone', 'eat']:
-            color = (0, 0, 255)
-        else:
-            color = (0, 255, 0)
-        if cls_id == 'eat':
-            cls_id = 'eat-drink'
-        c1, c2 = (x1, y1), (x2, y2)
-        cv2.rectangle(image, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
-        tf = max(tl - 1, 1)  # font thickness
-        t_size = cv2.getTextSize(cls_id, 0, fontScale=tl / 3, thickness=tf)[0]
-        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
-        cv2.rectangle(image, c1, c2, color, -1, cv2.LINE_AA)  # filled
-        cv2.putText(image, '{} ID-{}'.format(cls_id, pos_id), (c1[0], c1[1] - 2), 0, tl / 3,
-                    [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
-
-    return image
-
-
-def update_tracker(target_detector, image):
-
-        new_faces = []
-        _, bboxes = target_detector.detect(image)
-
-        bbox_xywh = []
-        confs = []
-
-        # Adapt detections to deep sort input format
-        for x1, y1, x2, y2, _, conf in bboxes:
-            
-            obj = [
-                int((x1+x2)/2), int((y1+y2)/2),
-                x2-x1, y2-y1
-            ]
-            bbox_xywh.append(obj)
-            confs.append(conf)
-
-        xywhs = torch.Tensor(bbox_xywh)
-        confss = torch.Tensor(confs)
-
-        # Pass detections to deepsort
-        outputs = deepsort.update(xywhs, confss, image)
-
-
-        bboxes2draw = []
-        face_bboxes = []
-        for value in list(outputs):
-            x1,y1,x2,y2,track_id = value
-            bboxes2draw.append(
-                (x1, y1, x2, y2, '', track_id)
-            )
-
-        image = plot_bboxes(image, bboxes2draw)
-
-        return image, new_faces, face_bboxes
+from deep_sort.utils.parser import get_config
+from deep_sort.deep_sort import DeepSort
+import torch
+import cv2
+
+palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
+cfg = get_config()
+cfg.merge_from_file("deep_sort/configs/deep_sort.yaml")
+deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
+                    max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
+                    nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
+                    max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
+                    use_cuda=True)
+
+
+def plot_bboxes(image, bboxes, line_thickness=None):
+    # Plots one bounding box on image img
+    tl = line_thickness or round(
+        0.002 * (image.shape[0] + image.shape[1]) / 2) + 1  # line/font thickness
+    for (x1, y1, x2, y2, cls_id, pos_id) in bboxes:
+        if cls_id in ['smoke', 'phone', 'eat']:
+            color = (0, 0, 255)
+        else:
+            color = (0, 255, 0)
+        if cls_id == 'eat':
+            cls_id = 'eat-drink'
+        c1, c2 = (x1, y1), (x2, y2)
+        cv2.rectangle(image, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
+        tf = max(tl - 1, 1)  # font thickness
+        t_size = cv2.getTextSize(cls_id, 0, fontScale=tl / 3, thickness=tf)[0]
+        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
+        cv2.rectangle(image, c1, c2, color, -1, cv2.LINE_AA)  # filled
+        cv2.putText(image, '{} ID-{}'.format(cls_id, pos_id), (c1[0], c1[1] - 2), 0, tl / 3,
+                    [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+
+    return image
+
+
+def update_tracker(target_detector, image):
+
+        new_faces = []
+        _, bboxes = target_detector.detect(image)
+
+        bbox_xywh = []
+        confs = []
+
+        # Adapt detections to deep sort input format
+        for x1, y1, x2, y2, _, conf in bboxes:
+            
+            obj = [
+                int((x1+x2)/2), int((y1+y2)/2),
+                x2-x1, y2-y1
+            ]
+            bbox_xywh.append(obj)
+            confs.append(conf)
+
+        xywhs = torch.Tensor(bbox_xywh)
+        confss = torch.Tensor(confs)
+
+        # Pass detections to deepsort
+        outputs = deepsort.update(xywhs, confss, image)
+
+
+        bboxes2draw = []
+        face_bboxes = []
+        for value in list(outputs):
+            x1,y1,x2,y2,track_id = value
+            bboxes2draw.append(
+                (x1, y1, x2, y2, '', track_id)
+            )
+
+        image = plot_bboxes(image, bboxes2draw)
+
+        return image, new_faces, face_bboxes