"cell_type": "code",
"collapsed": false,
"input": [
+ "import numpy as np\n",
"import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
"\n",
"df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
"print(df.shape)\n",
"cell_type": "code",
"collapsed": false,
"input": [
- "gray()\n",
- "matshow(predictions_df.values)\n",
- "xlabel('Classes')\n",
- "ylabel('Windows')"
+ "plt.gray()\n",
+ "plt.matshow(predictions_df.values)\n",
+ "plt.xlabel('Classes')\n",
+ "plt.ylabel('Windows')"
],
"language": "python",
"metadata": {},
"print(f.order(ascending=False)[:5])\n",
"\n",
"# Show top detection in red, second-best top detection in blue.\n",
- "im = imread('images/fish-bike.jpg')\n",
- "imshow(im)\n",
+ "im = plt.imread('images/fish-bike.jpg')\n",
+ "plt.imshow(im)\n",
"currentAxis = plt.gca()\n",
"\n",
"det = df.iloc[i]\n",
"coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
- "currentAxis.add_patch(Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
+ "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
"\n",
"det = df.iloc[j]\n",
"coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
- "currentAxis.add_patch(Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
+ "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
],
"language": "python",
"metadata": {},
"cell_type": "code",
"collapsed": false,
"input": [
- "imshow(im)\n",
+ "plt.imshow(im)\n",
"currentAxis = plt.gca()\n",
"colors = ['r', 'b', 'y']\n",
"for c, det in zip(colors, nms_dets[:3]):\n",
" currentAxis.add_patch(\n",
- " Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
+ " plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
" fill=False, edgecolor=c, linewidth=5)\n",
" )\n",
"print 'scores:', nms_dets[:3, 4]"