Create app.py

app.py

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+'''
+import gradio as gr
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()
+'''
+
+import os
+HOME = os.getcwd()
+print(HOME)
+
+#Upload your own video
+SOURCE_VIDEO_PATH = f"{HOME}/testing.mp4"
+
+# Pip install method (recommended)
+
+!pip install ultralytics
+
+from IPython import display
+display.clear_output()
+
+import ultralytics
+ultralytics.checks()
+
+%cd {HOME}
+!git clone https://github.com/ifzhang/ByteTrack.git
+%cd {HOME}/ByteTrack
+
+# workaround related to https://github.com/roboflow/notebooks/issues/80
+!sed -i 's/onnx==1.8.1/onnx==1.9.0/g' requirements.txt
+
+!pip3 install -q -r requirements.txt
+!python3 setup.py -q develop
+!pip install -q cython_bbox
+!pip install -q onemetric
+# workaround related to https://github.com/roboflow/notebooks/issues/112 and https://github.com/roboflow/notebooks/issues/106
+!pip install -q loguru lap thop
+
+from IPython import display
+display.clear_output()
+
+
+import sys
+sys.path.append(f"{HOME}/ByteTrack")
+
+
+import yolox
+print("yolox.__version__:", yolox.__version__)
+
+from yolox.tracker.byte_tracker import BYTETracker, STrack
+from onemetric.cv.utils.iou import box_iou_batch
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class BYTETrackerArgs:
+    track_thresh: float = 0.25
+    track_buffer: int = 30
+    match_thresh: float = 0.8
+    aspect_ratio_thresh: float = 3.0
+    min_box_area: float = 1.0
+    mot20: bool = False
+
+!pip install supervision==0.1.0
+
+
+from IPython import display
+display.clear_output()
+
+
+import supervision
+print("supervision.__version__:", supervision.__version__)
+
+from supervision.draw.color import ColorPalette
+from supervision.geometry.dataclasses import Point
+from supervision.video.dataclasses import VideoInfo
+from supervision.video.source import get_video_frames_generator
+from supervision.video.sink import VideoSink
+from supervision.notebook.utils import show_frame_in_notebook
+from supervision.tools.detections import Detections, BoxAnnotator
+from supervision.tools.line_counter import LineCounter, LineCounterAnnotator
+
+from typing import List
+
+import numpy as np
+
+
+# converts Detections into format that can be consumed by match_detections_with_tracks function
+def detections2boxes(detections: Detections) -> np.ndarray:
+    return np.hstack((
+        detections.xyxy,
+        detections.confidence[:, np.newaxis]
+    ))
+
+
+# converts List[STrack] into format that can be consumed by match_detections_with_tracks function
+def tracks2boxes(tracks: List[STrack]) -> np.ndarray:
+    return np.array([
+        track.tlbr
+        for track
+        in tracks
+    ], dtype=float)
+
+
+# matches our bounding boxes with predictions
+def match_detections_with_tracks(
+    detections: Detections,
+    tracks: List[STrack]
+) -> Detections:
+    if not np.any(detections.xyxy) or len(tracks) == 0:
+        return np.empty((0,))
+
+    tracks_boxes = tracks2boxes(tracks=tracks)
+    iou = box_iou_batch(tracks_boxes, detections.xyxy)
+    track2detection = np.argmax(iou, axis=1)
+
+    tracker_ids = [None] * len(detections)
+
+    for tracker_index, detection_index in enumerate(track2detection):
+        if iou[tracker_index, detection_index] != 0:
+            tracker_ids[detection_index] = tracks[tracker_index].track_id
+
+    return tracker_ids
+
+# settings
+MODEL = "yolov8x.pt"
+
+from ultralytics import YOLO
+
+model = YOLO(MODEL)
+model.fuse()
+
+# dict maping class_id to class_name
+CLASS_NAMES_DICT = model.model.names
+# class_ids of interest - car, motorcycle, bus and truck
+CLASS_ID = [2, 3, 5, 7]
+
+# create frame generator
+generator = get_video_frames_generator(SOURCE_VIDEO_PATH)
+# create instance of BoxAnnotator
+box_annotator = BoxAnnotator(color=ColorPalette(), thickness=4, text_thickness=4, text_scale=2)
+# acquire first video frame
+iterator = iter(generator)
+frame = next(iterator)
+# model prediction on single frame and conversion to supervision Detections
+results = model(frame)
+detections = Detections(
+    xyxy=results[0].boxes.xyxy.cpu().numpy(),
+    confidence=results[0].boxes.conf.cpu().numpy(),
+    class_id=results[0].boxes.cls.cpu().numpy().astype(int)
+)
+# format custom labels
+labels = [
+    f"{CLASS_NAMES_DICT[class_id]} {confidence:0.2f}"
+    for _, confidence, class_id, tracker_id
+    in detections
+]
+# annotate and display frame
+frame = box_annotator.annotate(frame=frame, detections=detections, labels=labels)
+
+%matplotlib inline
+show_frame_in_notebook(frame, (16, 16))
+
+# settings
+# Please settings the line for the counting
+LINE_START = Point(50, 430)
+LINE_END = Point(1280-50, 430)
+
+TARGET_VIDEO_PATH = f"{HOME}/vehicle-counting-result.mp4"
+
+VideoInfo.from_video_path(SOURCE_VIDEO_PATH)
+
+from tqdm.notebook import tqdm
+
+
+# create BYTETracker instance
+byte_tracker = BYTETracker(BYTETrackerArgs())
+# create VideoInfo instance
+video_info = VideoInfo.from_video_path(SOURCE_VIDEO_PATH)
+# create frame generator
+generator = get_video_frames_generator(SOURCE_VIDEO_PATH)
+# create LineCounter instance
+line_counter = LineCounter(start=LINE_START, end=LINE_END)
+# create instance of BoxAnnotator and LineCounterAnnotator
+box_annotator = BoxAnnotator(color=ColorPalette(), thickness=4, text_thickness=4, text_scale=2)
+line_annotator = LineCounterAnnotator(thickness=4, text_thickness=4, text_scale=2)
+
+# open target video file
+with VideoSink(TARGET_VIDEO_PATH, video_info) as sink:
+    # loop over video frames
+    for frame in tqdm(generator, total=video_info.total_frames):
+        # model prediction on single frame and conversion to supervision Detections
+        results = model(frame)
+        detections = Detections(
+            xyxy=results[0].boxes.xyxy.cpu().numpy(),
+            confidence=results[0].boxes.conf.cpu().numpy(),
+            class_id=results[0].boxes.cls.cpu().numpy().astype(int)
+        )
+        # filtering out detections with unwanted classes
+        mask = np.array([class_id in CLASS_ID for class_id in detections.class_id], dtype=bool)
+        detections.filter(mask=mask, inplace=True)
+        # tracking detections
+        tracks = byte_tracker.update(
+            output_results=detections2boxes(detections=detections),
+            img_info=frame.shape,
+            img_size=frame.shape
+        )
+        tracker_id = match_detections_with_tracks(detections=detections, tracks=tracks)
+        detections.tracker_id = np.array(tracker_id)
+        # filtering out detections without trackers
+        mask = np.array([tracker_id is not None for tracker_id in detections.tracker_id], dtype=bool)
+        detections.filter(mask=mask, inplace=True)
+        # format custom labels
+        labels = [
+            f"#{tracker_id} {CLASS_NAMES_DICT[class_id]} {confidence:0.2f}"
+            for _, confidence, class_id, tracker_id
+            in detections
+        ]
+        # updating line counter
+        line_counter.update(detections=detections)
+        # annotate and display frame
+        frame = box_annotator.annotate(frame=frame, detections=detections, labels=labels)
+        line_annotator.annotate(frame=frame, line_counter=line_counter)
+        sink.write_frame(frame)
+