Rasterio example

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python-powered.json

python_powered.py

# python_powered.py
#
# Turns the 15 features in the Python logo
#
#   http://www.python.org/community/logos/python-powered-h-140x182.png
#
# into a GeoJSON feature collection.

import json
import subprocess

import rasterio
from rasterio import features


with rasterio.drivers():
    
    with rasterio.open('python-powered-h-140x182.png') as src:
        blue = src.read_band(3)
    
    # Set every non-background pixel to 0 and then mask out the
    # white background.
    blue[blue < 255] = 0
    mask = blue != 255

    # transform to world coordinates so that we can map it
    transform = [-35.0, 0.5, 0.0, 40.5, 0.0, -0.5]

    # Get shapes from the positive part of the image.
    shapes = list(features.shapes(blue, mask=mask, transform=transform))

    # Burn those shapes into a new image.
    image = features.rasterize(
                ((g, 255) for g, v in shapes),
                out_shape=blue.shape, fill=0, transform=transform )

    # Write the new image out.
    kwargs = src.meta
    kwargs['count'] = 1
    kwargs['driver'] = 'GTiff'
    with rasterio.open('result.tif', 'w', **kwargs) as dst:
        dst.write_band(1, image)

subprocess.call(['open', 'result.tif'])

# Write the shapes out to GeoJSON.
results = ({
        'type': 'Feature', 
        'properties': {'raster_val': v}, 
        'geometry': s }
    for i, (s, v) 
        in enumerate(shapes) )

collection = {
    'type': 'FeatureCollection', 
    'features': list(results) }

with open('python-powered.json', 'w') as dst:
    json.dump(collection, dst)

rasterio_features_roundtrip.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# python_powered.py\n",
      "#\n",
      "# Turns the 15 features in the Python logo\n",
      "#\n",
      "#   http://www.python.org/community/logos/python-powered-h-140x182.png\n",
      "#\n",
      "# into a GeoJSON feature collection.\n",
      "#\n",
      "# Along the way, demonstrates a round-trip through shapes() and rasterize().\n",
      "\n",
      "import json\n",
      "import subprocess\n",
      "\n",
      "import rasterio\n",
      "from rasterio import features\n",
      "\n",
      "\n",
      "with rasterio.drivers():\n",
      "    with rasterio.open('python-powered-h-140x182.png') as src:\n",
      "        blue = src.read_band(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set every non-background pixel to 0 and then mask out the\n",
      "# white background (positive mask for foreground).\n",
      "blue[blue < 255] = 0\n",
      "mask = blue != 255\n",
      "\n",
      "# Show the source image.\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.cm as cm\n",
      "plt.imshow(blue, cmap=cm.Greys_r)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<matplotlib.image.AxesImage at 0x110dad210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3AQDt2rVDQEAA46suNQ+lWVBaWorg4GDs2LED6enpAABnZ2ckJSWhRYsWjGJK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\nhlDzUCgMoeahUBjyfwxfCqHbdMdWAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110d80d50>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Transform to world coordinates so that we can map it\n",
      "transform = [-35.0, 0.5, 0.0, 40.5, 0.0, -0.5]\n",
      "\n",
      "# Perform a round-trip through shapes() and rasterize().\n",
      "with rasterio.drivers():\n",
      "    \n",
      "    # Extract shapes from the positive (0) source image\n",
      "    shapes = list(features.shapes(blue, mask=mask, transform=transform))\n",
      "    \n",
      "    image = features.rasterize(\n",
      "                ((g, 255) for g, v in shapes), \n",
      "                out_shape=blue.shape, \n",
      "                fill=0, \n",
      "                transform=transform )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.imshow(image, cmap=cm.Greys_r)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.image.AxesImage at 0x11203cb50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mMDAwwMyZMxEZGQkLCwvJIEVCCCwtLWUGLapSPBSKJlAkEcbPPL/++itsbGxQUFCAwMBA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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110961b50>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with rasterio.drivers():\n",
      "    kwargs = src.meta\n",
      "    kwargs['count'] = 1\n",
      "    kwargs['driver'] = 'GTiff'\n",
      "    with rasterio.open('result.tif', 'w', **kwargs) as dst:\n",
      "        dst.write_band(1, image)\n",
      "\n",
      "subprocess.call(['open', 'result.tif'])\n",
      "\n",
      "results = ({\n",
      "        'type': 'Feature', \n",
      "        'properties': {'raster_val': v}, \n",
      "        'geometry': s }\n",
      "    for i, (s, v) \n",
      "        in enumerate(shapes) )\n",
      "\n",
      "collection = {\n",
      "    'type': 'FeatureCollection', \n",
      "    'features': list(results) }\n",
      "\n",
      "with open('python-powered.json', 'w') as dst:\n",
      "    json.dump(collection, dst)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}

Did you use a world file or some other projection source for your source image? For me, GDAL reports ERROR 6: No translation an empty SRS to PROJ.4 format is known. and I get a pretty whacky geojson representation.