sidharthrajaram

#Sidharth Rajaram, 2017, part of NBA-Predict
from sklearn import svm
import numpy as np

def combineData(values1, values2):
    combined_stats = []
    for a in range(values1.size):
        result_component = [values1[a],values2[a]]
        combined_stats.append(result_component)
    combined_stats = np.array(combined_stats)

sidharthrajaram

# 3D Non-Maximum Suppression Implemented in Python - Sidharth Rajaram
import numpy as np
import time

# This approach assumes there are prediction scores (one class only) in the incoming bounding boxes as well.
# Selects best score and then suppresses.
# class score + bounding box = (p, x, y, z, w, h, l)
# p: classification score / probability
# x,y,z: location
# w,h,l: dimensions

sidharthrajaram

# sid rajaram
import numpy as np
import time

# This approach assumes there are class scores in the incoming lists as well.
# Selects best score and then suppresses.
# class score + bounding box = (p, x, y, w, h)
# p: classification score / probability
# x,y: location
# w,h: dimensions