🎥 Add example for video frames (#136)

* leave some of the notebook output

* Update README with steps for video frames

README.md

@@ -80,6 +80,19 @@ Further examples and use cases are found in the [accompanying Jupyter Notebook](
 
 If no output folder is added, asks for confirmation and destructively crops images in-place.
 
+#### Detecting faces from video files
+You can use autocrop to detect faces in frames extracted from a video. A great way to [perform the frame extraction step is with `ffmpeg`](https://ffmpeg.org/download.html):
+
+```sh
+mkdir frames faces
+
+# Extract one frame per second
+ffmpeg -i input.mp4 -filter:v fps=fps=1/60 frames/ffmpeg_%0d.bmp
+
+# Crop faces as jpg
+autocrop -i frames -o faces -e jpg
+```
+
 
 ## Supported file types
 
@@ -119,7 +132,7 @@ pip install .
 ~~~
 
 ### conda
-Development of a `conda-forge` package for the [Anaconda Python distribution](https://www.anaconda.com/download/) is also currently slated for development. Please leave feedback on [issue #7](https://github.com/leblancfg/autocrop/issues/7) if you are insterested in helping out.
+Development of a `conda-forge` package for the [Anaconda Python distribution](https://www.anaconda.com/download/) is currently stalled due to the complexity of setting up the workflow with OpenCV. Please leave feedback on [issue #7](https://github.com/leblancfg/autocrop/issues/7) to see past attempts if you are insterested in helping out!
 
 ## Requirements
 Best practice for your projects is of course to [use virtual environments](http://docs.python-guide.org/en/latest/dev/virtualenvs/). At the very least, you will need to [have pip installed](https://pip.pypa.io/en/stable/installing/).

tests/visual_tests.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-23T19:44:29.134965Z",
@@ -49,25 +49,15 @@
      "metadata": {},
      "output_type": "execute_result"
     }
-   },
-   "outputs": [],
+   ],
    "source": [
     "img_array = c.crop(loc)\n",
     "PIL.Image.fromarray(img_array)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PIL.Image.fromarray(c.crop(img_array))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-23T19:44:37.012712Z",
@@ -97,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-23T19:44:32.387838Z",
@@ -122,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-23T19:44:32.604788Z",
@@ -160,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-23T19:44:32.734430Z",
@@ -198,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-23T19:44:35.895830Z",
@@ -342,15 +332,6 @@
     "## `_crop_postions`"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## `_crop_postions`"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 13,
@@ -458,7 +439,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.0"
   }
  },
  "nbformat": 4,