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vectorProcessing.py
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vectorProcessing.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Jan 18 11:39:23 2020
@author: yasha
"""
import time
import requests
import json
import os
import numpy as np
import math
# helper methods:
def json_r(filename):
with open(filename) as f:
return (json.load(f))
def unit(np_array):
return np_array / np.linalg.norm(np_array)
def cos_sim(v, u):
return np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(u))
def split_joints_tuple(joints_array):
return list(zip(joints_array[::2], joints_array[1::2]))
def squish(x):
return 1 - math.e ** (-x)
## 0.8 threshhold value?
# comparison algorithm
def compare_dance(init_pose, sample_pos):
zip_init_pose = split_joints_tuple(init_pose)
zip_sample_pos = split_joints_tuple(sample_pos)
accum = 0;
num_vecs = 0
for j in range(len(init_pose) // 2):
for k in range(len(init_pose) // 2):
if j >= k:
continue
elif zip_init_pose[j][0] < 0 or zip_sample_pos[j][0] < 0:
continue
else:
v_init_j = (np.array(zip_init_pose[j]))
v_init_k = (np.array(zip_init_pose[k]))
v_sam_j = (np.array(zip_sample_pos[j]))
v_sam_k = (np.array(zip_sample_pos[k]))
v_init_diff = unit(v_init_j - v_init_k)
v_sam_diff = unit(v_sam_j - v_sam_k)
cos_dist_val = 1 - (cos_sim(v_init_diff, v_sam_diff))
accum += cos_dist_val; num_vecs += 1
# accum += np.linalg.norm(v_init_diff - v_sam_diff)**2
return (accum / num_vecs)
"""
Returns a parsed list of joint data from cloud API
"""
def request_joint_data(filepath):
LOGIN_URL = 'https://api.wrnch.ai/v1/login'
JOBS_URL = 'https://api.wrnch.ai/v1/jobs'
API_KEY = "f2b6d23f-b864-4c59-bbb3-f5aae914fb3b"
resp_auth = requests.post(LOGIN_URL, data={'api_key': API_KEY})
JWTTOKEN = json.loads(resp_auth.text)['access_token']
# load
with open(filepath, 'rb') as f:
resp_sub_job = requests.post(JOBS_URL,
headers={'Authorization': f'Bearer {JWTTOKEN}'},
files={'media': f},
data={'work_type': 'json'})
job_id = json.loads(resp_sub_job.text)['job_id']
print('Status code:', resp_sub_job.status_code)
print('Response:', resp_sub_job.text)
time.sleep(0.5)
GET_JOB_STATUS_URL = 'https://api.wrnch.ai/v1/status' + '/' + job_id
resp_get_job = requests.get(GET_JOB_STATUS_URL, headers={'Authorization': f'Bearer {JWTTOKEN}'})
print('Status code:', resp_get_job.status_code)
print('\nResponse:', resp_get_job.text)
time.sleep(5)
GET_JOB_URL = JOBS_URL + '/' + job_id
print(GET_JOB_URL)
resp_get_job = requests.get(GET_JOB_URL, headers={'Authorization': f'Bearer {JWTTOKEN}'})
cloud_pose_estimation = json.loads(resp_get_job.text)
return cloud_pose_estimation['frames'][0]['persons'][0]['pose2d']['joints']
def make_base_model(pathname, posename):
imgs = os.listdir(pathname)
i = 0
while(i < len(imgs)):
data = request_joint_data(imgs[i])
if i == 0:
output = np.array(data)
else:
vec = np.array(data)
output = np.vstack((output, vec))
i+=1
# writing to binary npy final
np.save(posename, output) # posename = <posename>.npy
if __name__ == "__main__":
# importing .json/jpeg file
os.chdir(r"C:\Users\yasha\Desktop\McWics\james-test\base")
imgs = os.listdir(r"C:\Users\yasha\Desktop\McWics\james-test\base")
i = 0
while (i <= 9):
data1 = request_joint_data(imgs[i])
data2 = request_joint_data(imgs[i + 1])
print("result of comparison: {}".format(compare_dance(data1, data2)))
i += 2
data = request_joint_data(imgs[0]);
data_ = request_joint_data(imgs[6])
print(compare_dance(data, data_))
# print(compare_dance((0.5,0.43,0.42,0.64,0.23,0.88,0.76, 0.43, 0.6, 0.32),(0.5335,0.4253,0.235,0.273,0.769,0.67,0.47, 0.457, 0.543, 0.56)))
# print(compare_dance(joints, joints))