Some Updates

analysisAPI/backgroundFalseNegErrors.py

@@ -1,9 +1,9 @@
 ## imports
 import os, time
+import cv2
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.path as mplPath
-from scipy.misc import imresize
 import skimage.io as io
 
 # package imports 
@@ -146,7 +146,7 @@ def backgroundFalseNegErrors( coco_analyze, imgs_info, saveDir ):
             grid = grid.reshape((ny,nx))
 
             the_mask += np.array(grid, dtype=int)
-        segm_heatmap += imresize(the_mask,(128,128))
+        segm_heatmap += cv2.resize(the_mask,(128,128))
 
     fig = plt.figure(figsize=(10,10))
     ax = plt.subplot(111)

analysisAPI/backgroundFalsePosErrors.py

@@ -3,7 +3,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.path as mplPath
-from scipy.misc import imresize
 import skimage.io as io
 
 # package imports 

analysisAPI/localizationErrors.py

@@ -81,7 +81,8 @@ def localizationErrors( coco_analyze, imgs_info, saveDir ):
     plt.close()
 
     fig = plt.figure(figsize=(15,15)); plt.axis('off')
-    I = io.imread('./latex/manikin.jpg')
+    filepath = os.path.dirname(os.path.realpath(__file__)) + "/"
+    I = io.imread(filepath + '../latex/manikin.jpg')
     plt.imshow(I); ax = plt.gca(); ax.set_autoscale_on(False)
 
     rects_d = {}

analysisAPI/utilities.py

@@ -6,7 +6,6 @@
 import matplotlib.path as mplPath
 from matplotlib.collections import PatchCollection
 from matplotlib.patches import Polygon
-from scipy.misc import imresize
 import skimage.io as io
 
 """

latex/report_template.tex

@@ -42,7 +42,7 @@
 % HUMAN SKELETON COLOR CODING
 \section{Human Pose and Skeleton Color Coding}
 \begin{wrapfigure}{l}{0.5\textwidth}
-\includegraphics[width=\linewidth]{./latex/color_coding.pdf}
+\includegraphics[width=\linewidth]{color_coding.pdf}
 \caption{ {\small \textbf{Detection's Skeleton Color Coding.}}}
 \end{wrapfigure}
 We adopt the following color coding when visualizing an algorithm's keypoint detections:

pycocotools/cocoeval.py

@@ -605,8 +605,8 @@ def setDetParams(self):
         self.imgIds = []
         self.catIds = []
         # np.arange causes trouble.  the data point on arange is slightly larger than the true value
-        self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True)
-        self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True)
+        self.iouThrs = np.linspace(.5, 0.95, int(np.round((0.95 - .5) / .05) + 1), endpoint=True)
+        self.recThrs = np.linspace(.0, 1.00, int(np.round((1.00 - .0) / .01) + 1), endpoint=True)
         self.maxDets = [1, 10, 100]
         self.areaRng = [[0 ** 2, 1e5 ** 2], [0 ** 2, 32 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]]
         self.areaRngLbl = ['all', 'small', 'medium', 'large']
@@ -616,8 +616,8 @@ def setKpParams(self):
         self.imgIds = []
         self.catIds = []
         # np.arange causes trouble.  the data point on arange is slightly larger than the true value
-        self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True)
-        self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True)
+        self.iouThrs = np.linspace(.5, 0.95, int(np.round((0.95 - .5) / .05) + 1), endpoint=True)
+        self.recThrs = np.linspace(.0, 1.00, int(np.round((1.00 - .0) / .01) + 1), endpoint=True)
         self.maxDets = [20]
         self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]]
         self.areaRngLbl = ['all', 'medium', 'large']

requirements.txt

@@ -1,3 +1,4 @@
 colour
 numpy
-matplotlib
+matplotlib
+opencv-python

run_analysis.py

@@ -1,5 +1,5 @@
 ## general imports
-import os, sys, json, datetime, jinja2
+import os, shutil, sys, json, datetime, jinja2
 from jinja2 import Template
 
 ## COCO imports
@@ -19,6 +19,7 @@ def main():
     if len(sys.argv) != 6:
         raise ValueError("Please specify args: $> python run_analysis.py [annotations_path] [detections_path] [save_dir] [team_name] [version_name]")
 
+    filepath = os.path.dirname(os.path.realpath(__file__)) + "/"
     latex_jinja_env = jinja2.Environment(
         block_start_string    = '\BLOCK{',
         block_end_string      = '}',
@@ -30,7 +31,7 @@ def main():
         line_comment_prefix   = '%#',
         trim_blocks           = True,
         autoescape            = False,
-        loader                = jinja2.FileSystemLoader(os.path.abspath('./latex/'))
+        loader                = jinja2.FileSystemLoader(filepath + 'latex/')
     )
     template = latex_jinja_env.get_template('report_template.tex')
     template_vars  = {}
@@ -78,6 +79,7 @@ def main():
 
     ## initialize COCO analyze api
     coco_analyze = COCOanalyze(coco_gt, coco_dt, 'keypoints')
+    coco_analyze.params.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]]
     if teamName == 'fakekeypoints100':
         imgIds  = sorted(coco_gt.getImgIds())[0:100]
         coco_analyze.cocoEval.params.imgIds = imgIds
@@ -119,9 +121,10 @@ def main():
     paths = sizeSensitivity( coco_analyze, .75, saveDir )
     template_vars.update(paths)
 
-    output_report = open('./%s_performance_report.tex'%teamName, 'w')
+    output_report = open("{}/{}_performance_report.tex".format(saveDir, teamName), 'w')
     output_report.write( template.render(template_vars) )
     output_report.close()
+    shutil.copyfile(filepath + 'latex/color_coding.pdf', os.path.join(saveDir, 'color_coding.pdf'))
 
 if __name__ == '__main__':
     main()