Merge pull request #40 from tryolabs/remove_dependencies

Make OpenCV and motmetrics optional dependencies

.github/workflows/ci.yml

@@ -19,7 +19,7 @@ jobs:
     strategy:
       max-parallel: 3
       matrix:
-        python-version: [3.6, 3.7, 3.8, 3.9]
+        python-version: [3.7, 3.8, 3.9]
     steps:
       - uses: actions/checkout@v2
 

.isort.cfg

@@ -0,0 +1,3 @@
+[tool.isort]
+profile = "black"
+multi_line_output = 3

README.md

@@ -20,12 +20,24 @@ Norfair is built, used and maintained by [Tryolabs](https://tryolabs.com).
 
 ## Installation
 
-Norfair currently supports Python 3.6+.
+Norfair currently supports Python 3.7+.
+
+For the pure Python version, install as:
 
 ```bash
 pip install norfair
 ```
 
+To make Norfair install the dependencies to support more features, install as:
+
+```bash
+pip install norfair[video]  # Adds several video helper features running on OpenCV
+pip install norfair[metrics]  # Supports running MOT metrics evaluation
+pip install norfair[metrics,video]  # Everything included
+```
+
+If the needed dependencies are already present in the system, installing the pure Python version of Norfair is enough for enabling the extra features. This is particuarly useful for embedded devices, where installing compiled dependencies can be difficult.
+
 ## How it works
 
 Norfair works by estimating the future position of each point based on its past positions. It then tries to match these estimated positions with newly detected points provided by the detector. For this matching to occur, Norfair can rely on any distance function specified by the user of the library. Therefore, each object tracker can be made as simple or as complex as needed.
@@ -114,4 +126,4 @@ If you are interested, please [contact us](https://tryolabs.com/#contact).
 
 ## License
 
-Copyright © 2020, [Tryolabs](https://tryolabs.com). Released under the [BSD 3-Clause](LICENSE).
+Copyright © 2021, [Tryolabs](https://tryolabs.com). Released under the [BSD 3-Clause](LICENSE).

demos/alphapose/README.md

@@ -4,8 +4,9 @@ An example of how to integrate Norfair into the video inference loop of a pre ex
 
 ## Instructions
 
-1. [Follow the instructions](https://github.com/MVIG-SJTU/AlphaPose/tree/pytorch#installation) to install the Pytorch version of AlphaPose.
-2. Apply this diff to this [commit](https://github.com/MVIG-SJTU/AlphaPose/commit/ded84d450faf56227680f0527ff7e24ab7268754) on AlphaPose and use their [video_demo.py](https://github.com/MVIG-SJTU/AlphaPose/blob/ded84d450faf56227680f0527ff7e24ab7268754/video_demo.py) to process your video.
+1. Install Norfair with `pip install norfair[video]`.
+2. [Follow the instructions](https://github.com/MVIG-SJTU/AlphaPose/tree/pytorch#installation) to install the Pytorch version of AlphaPose.
+3. Apply this diff to this [commit](https://github.com/MVIG-SJTU/AlphaPose/commit/ded84d450faf56227680f0527ff7e24ab7268754) on AlphaPose and use their [video_demo.py](https://github.com/MVIG-SJTU/AlphaPose/blob/ded84d450faf56227680f0527ff7e24ab7268754/video_demo.py) to process your video.
 
     ```diff
     diff --git a/dataloader.py b/dataloader.py

demos/detectron2/README.md

@@ -6,8 +6,9 @@ Simplest possible example of tracking. Based on [Detectron2](https://github.com/
 
 Assuming Norfair is installed:
 
-1. [Follow the instructions](https://detectron2.readthedocs.io/tutorials/install.html) to install Detectron2.
-2. Run `python detectron2_cars.py`. For the demo, we are using [this traffic footage](https://www.youtube.com/watch?v=aio9g9_xVio).
+1. Install Norfair with `pip install norfair[video]`.
+2. [Follow the instructions](https://detectron2.readthedocs.io/tutorials/install.html) to install Detectron2.
+3. Run `python detectron2_cars.py`. For the demo, we are using [this traffic footage](https://www.youtube.com/watch?v=aio9g9_xVio).
 
 ## Explanation
 

demos/motmetrics4norfair/README.md

@@ -1,24 +1,28 @@
 # Compute MOTChallenge metrics
+
 Demo on how to evaluate a Norfair tracker on the [MOTChallenge](https://motchallenge.net).
 
 ## Instructions
 
-1. Download the [MOT17](https://motchallenge.net/data/MOT17/) dataset running
+1. Install Norfair with `pip install norfair[metrics,video]`.
+2. Download the [MOT17](https://motchallenge.net/data/MOT17/) dataset running:
+
+    ```bash
+    curl -O https://motchallenge.net/data/MOT17.zip  # To download Detections + Ground Truth + Images (5.5GB)
+    unzip MOT17.zip
+    ```
+
+    or
 
-```bash
-curl -O https://motchallenge.net/data/MOT17.zip  # To download Detections + Ground Truth + Images (5.5GB)  
-unzip MOT17.zip
- ``` 
-or
+    ```bash
+    curl -O https://motchallenge.net/data/MOT17Labels.zip  # To download Detections + Ground Truth (9.7 MB)
+    unzip MOT17Labels.zip
+    ```
 
-```bash
-curl -O https://motchallenge.net/data/MOT17Labels.zip  # To download Detections + Ground Truth (9.7 MB)  
-unzip MOT17Labels.zip
-``` 
+    Given that the ground truth files for the testing set are not publicly available, you will only be able to use motmetrics4norfair with the training set.
 
-Given that the ground truth files for the testing set are not publicly available, you will only be able to use motmetrics4norfair with the training set.
+3. Display the instructions:
 
-2. Display the motmetrics4norfair instructions: 
-```bash
-    python motmetrics4norfair.py --help 
-``` 
+    ```bash
+    python motmetrics4norfair.py --help
+    ```

demos/motmetrics4norfair/motmetrics4norfair.py

@@ -1,8 +1,9 @@
+import argparse
 import os.path
+
 import numpy as np
-import norfair
-from norfair import Detection, Tracker, metrics, video, drawing
-import argparse
+
+from norfair import Tracker, drawing, metrics, video
 
 frame_skip_period = 1
 detection_threshold = 0.01

demos/openpose/README.md

@@ -4,8 +4,9 @@ Demo for extrapolating detections through skipped frames. Based on [OpenPose](ht
 
 ## Instructions
 
-1. Install [OpenPose version 1.4](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.4.0).
-2. Run `python openpose_extrapolation.py`.
+1. Install Norfair with `pip install norfair[video]`.
+2. Install [OpenPose version 1.4](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases/tag/v1.4.0).
+3. Run `python openpose_extrapolation.py`.
 
 ## Explanation
 

demos/yolov4/README.md

@@ -4,12 +4,12 @@ Simplest possible example of tracking. Based on [pytorch YOLOv4](https://github.
 
 ## Instructions
 
-1. Clone [pytorch YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4/tree/master) and download the [weights](https://drive.google.com/open?id=1wv_LiFeCRYwtpkqREPeI13-gPELBDwuJ) published on the repo into your local clone of the repo.
-2. Copy `yolov4demo.py` into your local clone of the repo and run `python yolov4demo.py <video file>`.
+1. Install Norfair with `pip install norfair[video]`.
+2. Clone [pytorch YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4/tree/master) and download the [weights](https://drive.google.com/open?id=1wv_LiFeCRYwtpkqREPeI13-gPELBDwuJ) published on the repo into your local clone of the repo.
+3. Copy `yolov4demo.py` into your local clone of the repo and run `python yolov4demo.py <video file>`.
 
 ## Explanation
 
 This example tracks objects using a single point per detection: the centroid of the bounding boxes around cars returned by YOLOv4.
 
 ![Norfair YOLOv4 demo](../../docs/yolo_cars.gif)
-

demos/yolov4/yolov4demo.py

@@ -3,13 +3,13 @@
 import cv2
 import numpy as np
 import torch
-
-import norfair
 from models import Yolov4
-from norfair import Detection, Tracker, Video
 from tool.torch_utils import do_detect
 from tool.utils import load_class_names, plot_boxes_cv2
 
+import norfair
+from norfair import Detection, Tracker, Video
+
 max_distance_between_points = 30
 
 

norfair/__init__.py

@@ -1,4 +1,4 @@
 from .drawing import *
 from .tracker import Detection, Tracker
-from .utils import print_objects_as_table, get_cutout
+from .utils import get_cutout, print_objects_as_table
 from .video import Video

norfair/drawing.py

@@ -1,10 +1,15 @@
-from typing import Optional, Tuple, Sequence
+from typing import Optional, Sequence, Tuple
 
-import cv2
-import numpy as np
+try:
+    import cv2
+except ImportError:
+    from .utils import DummyOpenCVImport
+
+    cv2 = DummyOpenCVImport()
 import random
 
-from .tracker import Detection, TrackedObject
+import numpy as np
+
 from .utils import validate_points
 
 

norfair/metrics.py

@@ -1,11 +1,21 @@
 import os
+
 import numpy as np
 from rich import print
 from rich.progress import track
+
 from norfair import Detection
-import motmetrics as mm
+
+try:
+    import motmetrics as mm
+    import pandas as pd
+except ImportError:
+    from .utils import DummyMOTMetricsImport
+
+    mm = DummyMOTMetricsImport()
+    pandas = DummyMOTMetricsImport()
 from collections import OrderedDict
-import pandas as pd
+
 
 
 class InformationFile:
@@ -157,6 +167,9 @@ def __init__(self):
         self.paths = []
 
     def create_accumulator(self, input_path, information_file=None):
+        # Check that motmetrics is installed here, so we don't have to process
+        # the whole dataset before failing out if we don't.
+        mm.metrics
 
         file_name = os.path.split(input_path)[1]
 
@@ -220,9 +233,9 @@ def compute_metrics(
         return self.summary
 
     def save_metrics(self, save_path=".", file_name="metrics.txt"):
-
         if not os.path.exists(save_path):
-            os.makedirs(save_folder)
+            os.makedirs(save_path)
+
         metrics_path = os.path.join(save_path, file_name)
         metrics_file = open(metrics_path, "w+")
         metrics_file.write(self.summary)

norfair/tracker.py

@@ -1,10 +1,10 @@
 import math
-from typing import Callable, Optional, Sequence, List
+from typing import Callable, List, Optional, Sequence
 
 import numpy as np
 from filterpy.kalman import KalmanFilter
-
 from rich import print
+
 from .utils import validate_points
 
 

norfair/utils.py

@@ -1,5 +1,5 @@
 import os
-from typing import Tuple, Sequence
+from typing import Sequence, Tuple
 
 import numpy as np
 from rich import print
@@ -68,3 +68,23 @@ def get_cutout(points, image):
     max_y = int(max(points[:, 1]))
     min_y = int(min(points[:, 1]))
     return image[min_y:max_y, min_x:max_x]
+
+
+class DummyOpenCVImport:
+    def __getattribute__(self, name):
+        print(
+            """[bold red]Missing dependency:[/bold red] You are trying to use Norfair's video features. However, OpenCV is not installed.
+
+Please, make sure there is an existing installation of OpenCV or install Norfair with `pip install norfair\[video]`."""
+        )
+        exit()
+
+
+class DummyMOTMetricsImport:
+    def __getattribute__(self, name):
+        print(
+            """[bold red]Missing dependency:[/bold red] You are trying to use Norfair's metrics features without the required dependencies.
+
+Please, install Norfair with `pip install norfair\[metrics]`, or `pip install norfair\[metrics,video]` if you also want video features."""
+        )
+        exit()

norfair/video.py

@@ -1,11 +1,16 @@
 import os
 import time
-from typing import Optional, List, Union
+from typing import List, Optional, Union
 
-import cv2
+try:
+    import cv2
+except ImportError:
+    from .utils import DummyOpenCVImport
+
+    cv2 = DummyOpenCVImport()
 import numpy as np
 from rich import print
-from rich.progress import BarColumn, Progress, TimeRemainingColumn, ProgressColumn
+from rich.progress import BarColumn, Progress, ProgressColumn, TimeRemainingColumn
 
 from .utils import get_terminal_size