Remove motmetrics (which adds pandas) and opencv as dependencies

Add them as extra dependencies, adjust code to return pretty error
messages when imports fail, and update documentation.

README.md

@@ -20,12 +20,20 @@ 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 pure python version that works anywhere install as:
 ```bash
 pip install norfair
 ```
 
+For more features install as:
+```bash
+pip install norfair[metrics]  # Supports running MOT metrics evaluation
+pip install norfair[video]  # Adds several video helper features running on OpenCV
+pip install norfair[metrics,video]  # Everything included
+```
+
 ## 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.

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

@@ -3,7 +3,8 @@ Demo on how to evaluate a Norfair tracker on the [MOTChallenge](https://motchall
 
 ## 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)  
@@ -18,7 +19,7 @@ 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.
 
-2. Display the motmetrics4norfair instructions: 
+3. Display the motmetrics4norfair instructions: 
 ```bash
     python motmetrics4norfair.py --help 
-``` 
+``` 

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

@@ -4,8 +4,9 @@ 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
 

norfair/drawing.py

@@ -1,6 +1,9 @@
 from typing import Optional, Tuple, Sequence
-
-import cv2
+try:
+    import cv2
+except:
+    from utils import DummyOpenCVImport
+    cv2 = DummyOpenCVImport()
 import numpy as np
 import random
 

norfair/metrics.py

@@ -3,7 +3,11 @@
 from rich import print
 from rich.progress import track
 from norfair import Detection
-import motmetrics as mm
+try:
+    import motmetrics as mm
+except:
+    from utils import DummyMOTMetricsImport
+    mm = DummyMOTMetricsImport()
 from collections import OrderedDict
 import pandas as pd
 

norfair/utils.py

@@ -68,3 +68,14 @@ 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("You are trying to use Norfair's video features. Install with `pip install norfair\[video]`, or install opencv-python manually.")
+        exit()
+
+class DummyMOTMetricsImport:
+    def __getattribute__(self, name):
+        print("You are trying to use Norfair's metrics features. Install with `pip install norfair\[metrics]`, or install motmetrics manually.")
+        exit()

norfair/video.py

@@ -1,8 +1,11 @@
 import os
 import time
 from typing import Optional, List, 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

pyproject.toml

@@ -1,18 +1,22 @@
 [tool.poetry]
 name = "norfair"
-version = "0.1.8"
+version = "0.2.0"
 description = "Lightweight Python library for real-time 2D object tracking."
 license = "BSD-3-Clause"
 authors = ["Tryolabs <[email protected]>"]
 repository = "https://github.com/tryolabs/norfair"
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = "^3.6"
+python = "^3.7"
 filterpy = "^1.4.5"
 rich = "^6.1.1"
-opencv-python = ">= 3.2.0, < 5.0.0"
-motmetrics = "1.2.0"
+opencv-python = {version = ">= 3.2.0, < 5.0.0", optional = true}
+motmetrics = {version = "1.2.0", optional = true}
+
+[tool.poetry.extras]
+metrics = ["motmetrics"]
+video = ["opencv-python"]
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.0.1"