Isometric Projection with gluLookAt - glut

I have this house centered in the origin at (0,0,0) with dimensions 10x10x20. I want to make an isometric projection but the house is not displayed.Here is the code for the house:
glBegin(GL_TRIANGLES);
//roof
glColor3f(1,1,0.35);
glVertex3f(-5, 5, 10);
glVertex3f( 5, 5, 10);
glVertex3f( 0, 11.160254, 10);
glColor3f(1,1,0.35);
glVertex3f(-5, 5, -10);
glVertex3f( 5, 5, -10);
glVertex3f( 0, 11.160254, -10);
glEnd();
/* We tell we want to draw quads */
glBegin(GL_QUADS);
/* Every four calls to glVertex, a quad is drawn */
//roof
glColor3f(1,1,0.5);
glVertex3f(-5, 5, 10);
glVertex3f( 0, 11.160254, 10);
glVertex3f( 0, 11.160254, -10);
glVertex3f(-5, 5, -10);
glColor3f(1,1,0.5);
glVertex3f(5, 5, 10);
glVertex3f( 0, 11.160254, 10);
glVertex3f( 0, 11.160254, -10);
glVertex3f(5, 5, -10);
//left face blue
glColor3f(0, 0, 1);
glVertex3f(-5, -5, -10);
glVertex3f(-5, -5, 10);
glVertex3f(-5, 5, 10);
glVertex3f(-5, 5, -10);
//right face cyan
glColor3f(0, 1, 1);
glVertex3f( 5, -5, -10);
glVertex3f( 5, -5, 10);
glVertex3f( 5, 5, 10);
glVertex3f( 5, 5, -10);
//bottom face magenta
glColor3f(1, 0, 1);
glVertex3f(-5, -5, -10);
glVertex3f(-5, -5, 10);
glVertex3f( 5, -5, 10);
glVertex3f( 5, -5, -10);
//top face yellow
glColor3f(1, 1, 0);
glVertex3f(-5, 5, -10);
glVertex3f(-5, 5, 10);
glVertex3f( 5, 5, 10);
glVertex3f( 5, 5, -10);
//red back face
glColor3f(1, 0, 0);
glVertex3f(-5, -5, -10);
glVertex3f(-5, 5, -10);
glVertex3f( 5, 5, -10);
glVertex3f( 5, -5, -10);
//front face green
glColor3f(0, 1, 0);
glVertex3f(-5, -5, 10);
glVertex3f(-5, 5, 10);
glVertex3f( 5, 5, 10);
glVertex3f( 5, -5, 10);
glEnd();
Can someone help me with the parameters that I have to put in gluLookAt in order to get an isometric projection?

Ok, you can try this:
gluLookAt(0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0);
eye at (0.0, 0.0, 10.0), look at the center point (0.0, 0.0, 0.0), up is (0.0, 1.0, 0.0).
Below is very clear image to explain this function:
(from http://profs.sci.univr.it/~colombar/html_openGL_tutorial/en/08viewing_005.html)
Sorry I can't insert the image, because that need 10 reputation. :(

Related

Bar of proportion of two variables

I am having a pandas dataframe as shown below
import numpy as np
data = {
'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50],
'baseline': [1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1],
'endline': [1, 0, np.nan, 1, 0, 0, 1, np.nan, 1, 0, 0, 1, 0, 0, 1, 0, np.nan, np.nan, 1, 0, 1, np.nan, 0, 1, 0, 1, 0, np.nan, 1, 0, np.nan, 0, 0, 0, np.nan, 1, np.nan, 1, np.nan, 0, np.nan, 1, 1, 0, 1, 1, 1, 0, 1, 1],
'gender': ['male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'male', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female', 'female']
}
df = pd.DataFrame(data)
df.head(n = 5)
The challenge is the endline column may have some missing values. My goal is to have 2 bars for each variable side by side as shown below.
Thanks in advance!
Seaborn prefers its data in "long form". Pandas' melt can convert the given dataframe to combine the 'baseline' and 'endline' columns.
By default, sns.barplot shows the mean when there are multiple y-values belonging to the same x-value. You can use a different estimator, e.g. summing the values and dividing by the number of values to get a percentage.
Here is some code to get you started:
import matplotlib.pyplot as plt
from matplotlib.ticker import PercentFormatter
import seaborn as sns
import pandas as pd
import numpy as np
data = {
'id': range(1, 51),
'baseline': [1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1],
'endline': [1, 0, np.nan, 1, 0, 0, 1, np.nan, 1, 0, 0, 1, 0, 0, 1, 0, np.nan, np.nan, 1, 0, 1, np.nan, 0, 1, 0, 1, 0, np.nan, 1, 0, np.nan, 0, 0, 0, np.nan, 1, np.nan, 1, np.nan, 0, np.nan, 1, 1, 0, 1, 1, 1, 0, 1, 1]
}
df = pd.DataFrame(data)
sns.set_style('white')
ax = sns.barplot(data=df.melt(value_vars=['baseline', 'endline']),
x='variable', y='value',
estimator=lambda x: np.sum(x) / np.size(x) * 100, ci=None,
color='cornflowerblue')
ax.bar_label(ax.containers[0], fmt='%.1f %%', fontsize=20)
sns.despine(ax=ax, left=True)
ax.grid(True, axis='y')
ax.yaxis.set_major_formatter(PercentFormatter(100))
ax.set_xlabel('')
ax.set_ylabel('')
plt.tight_layout()
plt.show()

Selecting the value at a given date for each lat/lon point in xarray

I have a xr.DataArray object that has a day of 2015 (as a cftime.DateTimeNoLeap object) for each lat-lon point on the grid.
date_matrix2015
<xarray.DataArray (lat: 160, lon: 320)>
array([[cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0), ...,
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0)],
[cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0), ...,
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0)],
[cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0), ...,
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 12, 11, 12, 0, 0, 0)],
...,
[cftime.DatetimeNoLeap(2015, 3, 14, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 3, 14, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 3, 14, 12, 0, 0, 0), ...,
cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0)],
[cftime.DatetimeNoLeap(2015, 9, 15, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 9, 15, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 9, 15, 12, 0, 0, 0), ...,
cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 9, 15, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 9, 15, 12, 0, 0, 0)],
[cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0), ...,
cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0),
cftime.DatetimeNoLeap(2015, 9, 16, 12, 0, 0, 0)]], dtype=object)
Coordinates:
year int64 2015
* lat (lat) float64 -89.14 -88.03 -86.91 -85.79 ... 86.91 88.03 89.14
* lon (lon) float64 0.0 1.125 2.25 3.375 4.5 ... 355.5 356.6 357.8 358.9
I have another xr.DataArray on the same lat-lon grid for vertical velocity (omega) that has data for every day in 2015. At each lat-lon point I would like to select the velocity value on the corresponding day given in date_matrix2015. Ideally I would like to do something like this:
omega.sel(time=date_matrix2015)
I have tried constructing the new dataarray manually with iteration, but I haven't had much luck.
Does anyone have any ideas? Thank you in advance!
------------EDIT---------------
Here is a minimal reproducible example for the problem. To clarify what I am looking for: I have two DataArrays, one for daily precipitation values, and one for daily omega values. I want to determine for each lat/lon point the day that saw the maximum precipitation (I think I have done this part correctly). From there I want to select at each lat/lon point the omega value that occurred on the day of maximum precipitation. So ultimately I would like to end up with a DataArray of omega values that has two dimensions, lat and lon, where the value at each lat/lon point is the omega value on the day of maximum rainfall at that location.
import numpy as np
import xarray as xr
import pandas as pd
precip = np.abs(8*np.random.randn(10,10,10))
omega = 15*np.random.randn(10,10,10)
lat = np.arange(0,10)
lon = np.arange(0, 10)
##Note: actual data resolution is 160x360
dates = pd.date_range('01-01-2015', '01-10-2015')
precip_da = xr.DataArray(precip).rename({'dim_0':'time', 'dim_1':'lat', 'dim_2':'lon'}).assign_coords({'time':dates, 'lat':lat, 'lon':lon})
omega_da = xr.DataArray(omega).rename({'dim_0':'time', 'dim_1':'lat', 'dim_2':'lon'}).assign_coords({'time':dates, 'lat':lat, 'lon':lon})
#Find Date of maximum precip for each lat lon point and store in an array
maxDateMatrix = precip_da.idxmax(dim='time')
#For each lat lon point, select the value from omega_da on the day of maximum precip (i.e. the date given at that location in the maxDateMatrix)
You can pair da.sel with da.idxmax to select the index of the maxima along any number of dimensions:
In [10]: omega_da.sel(time=precip_da.idxmax(dim='time'))
Out[10]:
<xarray.DataArray (lat: 10, lon: 10)>
array([[ 17.72211193, -16.20781517, 9.65493368, -28.16691093,
18.8756182 , 16.81924325, -20.55251804, -18.36625778,
-19.57938236, -10.77385357],
[ 3.95402784, -5.28478105, -8.6632994 , 2.46787932,
20.53981254, -4.74908659, 9.5274101 , -1.08191372,
9.4637305 , -10.91884369],
[-31.30033085, 6.6284144 , 8.15945444, 5.74849304,
12.49505739, 2.11797825, -18.12861347, 7.27497695,
5.16197504, -32.99882591],
...
[-34.73945635, 24.40515233, 14.56982584, 12.16550083,
-8.3558104 , -20.16328749, -33.89051472, -0.09599935,
2.65689584, 29.54056082],
[-18.8660847 , -7.58120994, 15.57632568, 4.19142695,
8.71046261, 9.05684805, 8.48128361, 0.34166869,
8.41090015, -2.31386572],
[ -4.38999926, 17.00411671, 16.66619606, 24.99390669,
-14.01424591, 19.85606151, -16.87897 , 12.84205521,
-16.78824975, -6.33920671]])
Coordinates:
time (lat, lon) datetime64[ns] 2015-01-01 2015-01-01 ... 2015-01-10
* lat (lat) int64 0 1 2 3 4 5 6 7 8 9
* lon (lon) int64 0 1 2 3 4 5 6 7 8 9
See the great section of the xarray docs on Indexing and Selecting Data for more info, especially the section on Advanced Indexing, which goes into using DataArrays as indexers for powerful reshaping operations.

Python - Convert from Response Variable to Pandas Dataframe

I ran a LIWC analysis and it gives me the following results (below). I would like to turn the result into a pandas dataframe. If anyone can chip in, that would be wonderful.
Thanks in advance :)
Best,
David
resp = requests.post(url, auth=(api_key, api_secret), data=data)
resp1 = resp
print(resp.json())
{'plan_usage': {'call_limit': 1000, 'calls_made': 6, 'calls_remaining': 994, 'percent_used': 0.6, 'start_date': '2020-12-09T03:05:57.779556Z', 'end_date': '2020-12-23T03:05:57.779556Z'}, 'results': [{'response_id': 'd1382f42-5c28-4528-ab2e-81b80ba185e2', 'request_id': 'req-1', 'language': 'en', 'version': 'v1.0.0', 'summary': {'word_count': 57, 'words_per_sentence': 11.4, 'sentence_count': 5, 'six_plus_words': 0.2982456140350877, 'emojis': 0, 'emoticons': 0, 'hashtags': 0, 'urls': 0}, 'liwc': {'scores': {'analytical_thinking': 80.77394876079086, 'authentic': 38.8220872694557, 'clout': 50, 'emotional_tone': 97.58138119866139, 'dictionary_words': 0.8771929824561403, 'categories': {'achievement': 0, 'adjectives': 0.017543859649122806, 'adverbs': 0.03508771929824561, 'affect': 0.05263157894736842, 'affiliation': 0.017543859649122806, 'all_punctuation': 0.10526315789473684, 'anger_words': 0, 'anxiety_words': 0, 'apostrophes': 0, 'articles': 0.12280701754385964, 'assent': 0, 'auxiliary_verbs': 0.14035087719298245, 'biological_processes': 0, 'body': 0, 'causation': 0, 'certainty': 0, 'cognitive_processes': 0.05263157894736842, 'colons': 0, 'commas': 0.017543859649122806, 'comparisons': 0, 'conjunctions': 0.07017543859649122, 'dashes': 0, 'death': 0, 'differentiation': 0, 'discrepancies': 0.017543859649122806, 'drives': 0.03508771929824561, 'exclamations': 0, 'family': 0, 'feel': 0, 'female': 0, 'filler_words': 0, 'focus_future': 0, 'focus_past': 0, 'focus_present': 0.14035087719298245, 'friends': 0.017543859649122806, 'function_words': 0.543859649122807, 'health': 0, 'hear': 0, 'home': 0, 'i': 0.03508771929824561, 'impersonal_pronouns': 0.03508771929824561, 'informal_language': 0, 'ingestion': 0, 'insight': 0, 'interrogatives': 0.017543859649122806, 'leisure': 0.14035087719298245, 'male': 0, 'money': 0, 'motion': 0.05263157894736842, 'negations': 0, 'negative_emotion_words': 0, 'netspeak': 0, 'nonfluencies': 0, 'numbers': 0, 'other_grammar': 0.2807017543859649, 'other_punctuation': 0, 'parentheses': 0, 'perceptual_processes': 0.017543859649122806, 'periods': 0.08771929824561403, 'personal_concerns': 0.14035087719298245, 'personal_pronouns': 0.03508771929824561, 'positive_emotion_words': 0.05263157894736842, 'power': 0, 'prepositions': 0.10526315789473684, 'pronouns': 0.07017543859649122, 'quantifiers': 0.05263157894736842, 'question_marks': 0, 'quotes': 0, 'relativity': 0.17543859649122806, 'religion': 0, 'reward': 0.017543859649122806, 'risk': 0, 'sad_words': 0, 'see': 0.017543859649122806, 'semicolons': 0, 'sexual': 0, 'she_he': 0, 'social': 0.03508771929824561, 'space': 0.10526315789473684, 'swear_words': 0, 'tentative': 0.03508771929824561, 'they': 0, 'time': 0.017543859649122806, 'time_orientation': 0.14035087719298245, 'verbs': 0.19298245614035087, 'we': 0, 'work': 0, 'you': 0}}}, 'sallee': {'counts': {'emotions': {'admiration': 5, 'amusement': 0, 'anger': 0, 'boredom': 0, 'calmness': 0, 'curiosity': 0, 'desire': 0, 'disgust': 0, 'excitement': 0.375, 'fear': 0, 'gratitude': 2, 'joy': 6.375, 'love': 5, 'pain': 0, 'sadness': 0, 'surprise': 0}, 'goodfeel': 13.375, 'ambifeel': 0, 'badfeel': 0, 'emotionality': 13.375, 'sentiment': 13.375, 'non_emotion': None}, 'scores': {'emotions': {'admiration': 0.3333333333333333, 'amusement': 0, 'anger': 0, 'boredom': 0, 'calmness': 0, 'curiosity': 0, 'desire': 0, 'disgust': 0, 'excitement': 0.03614457831325301, 'fear': 0, 'gratitude': 0.16666666666666666, 'joy': 0.3893129770992366, 'love': 0.3333333333333333, 'pain': 0, 'sadness': 0, 'surprise': 0}, 'goodfeel': 0.2015065913370998, 'ambifeel': 0, 'badfeel': 0, 'emotionality': 0.2015065913370998, 'sentiment': 0.6541600137038615, 'non_emotion': 0.7984934086629002}, 'emotion_word_count': 4}}]}
js = resp.json()
df = pd.json_normalize(js['results'][0])
df.columns
Index(['response_id', 'request_id', 'language', 'version',
'summary.word_count', 'summary.words_per_sentence',
'summary.sentence_count', 'summary.six_plus_words', 'summary.emojis',
'summary.emoticons',
...
'sallee.scores.emotions.pain', 'sallee.scores.emotions.sadness',
'sallee.scores.emotions.surprise', 'sallee.scores.goodfeel',
'sallee.scores.ambifeel', 'sallee.scores.badfeel',
'sallee.scores.emotionality', 'sallee.scores.sentiment',
'sallee.scores.non_emotion', 'sallee.emotion_word_count'],
dtype='object', length=150)
df.iloc[0]
response_id d1382f42-5c28-4528-ab2e-81b80ba185e2
request_id req-1
language en
version v1.0.0
summary.word_count 57
...
sallee.scores.badfeel 0
sallee.scores.emotionality 0.202
sallee.scores.sentiment 0.654
sallee.scores.non_emotion 0.798
sallee.emotion_word_count 4
Name: 0, Length: 150, dtype: object

Image center in the calibration matrix

I have a camera and a 3D object, I have calculated the camera matrix as given in this. The image captured using the camera is having size 1600x1200. But in the camera matrix I am not getting the value of image centers properly. Instead of 800, 600 I am getting some other values. What will be the possible reasons for this.
#include <iostream>
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <math.h>
using namespace std;
using namespace cv;
int main()
{
int numberofpoints=30;
float p2d[30][2] =
{{72, 169}, {72, 184}, {71, 264}, {96, 168}, {94, 261}, {94, 276}, {257, 133},
{254, 322}, {254, 337}, {278, 132}, {278, 146}, {275, 321}, {439, 228},
{439, 243}, {437, 328}, {435, 431}, {461, 226}, {459, 326}, {459, 342},
{457, 427}, {457, 444}, {612, 196}, {609, 291}, {605, 392}, {605, 406},
{634, 191}, {633, 206}, {630, 288}, {630, 303}, {627, 390}};
float p3d[30][3] =
{{0, 0, 98}, {0, 0, 94}, {0, 0, 75}, {4, 4, 98}, {4, 4, 75}, {4, 4, 71},
{0, 96, 75}, {0, 96, 25}, {0, 96, 21}, {4, 100, 75},
{4, 100, 71}, {4, 100, 25}, {96, 0, 98}, {96, 0, 94},
{96, 0, 75}, {96, 0, 50}, {100, 4, 98}, {100, 4, 75},
{100, 4, 71}, {100, 4, 50}, {100, 4, 46}, {96, 96, 75},
{96, 96, 50}, {96, 96, 25}, {96, 96, 21}, {100, 100, 75},
{100, 100, 71}, {100, 100, 50}, {100, 100, 46}, {100, 100, 25}};
Mat Matrix2D= Mat(30, 2, CV_64FC1, &p2d);
Mat Matrix3D= Mat(30, 3, CV_64FC1, &p3d);
Mat X_3D=Matrix3D.col(0);
Mat Y_3D=Matrix3D.col(1);
Mat Z_3D=Matrix3D.col(2);
Mat X_2D=Matrix2D.col(0);
Mat Y_2D=Matrix2D.col(1);
Mat z = Mat::zeros(numberofpoints,4, CV_64FC1);
Mat o = Mat::ones(numberofpoints,1, CV_64FC1);
Mat temp,temp1,C,D,AoddRows;
hconcat( X_3D, Y_3D,temp);
hconcat(Z_3D ,o,temp1);
hconcat(z, -X_2D.mul(X_3D),C);
hconcat(-X_2D.mul(Y_3D),-X_2D.mul(Z_3D),D);
hconcat(temp,temp1,AoddRows);
hconcat(AoddRows,C,AoddRows);
hconcat(AoddRows,D,AoddRows);
hconcat(AoddRows,-X_2D,AoddRows);
Mat A1,B1,C1,AevenRows;
hconcat(z,Matrix3D,A1);
hconcat(o,-Y_2D.mul(X_3D) ,B1);
hconcat(-Y_2D.mul(Y_3D),-Y_2D.mul(Z_3D),C1);
hconcat(A1,B1,AevenRows);
hconcat(AevenRows,C1,AevenRows);
hconcat(AevenRows,-Y_2D,AevenRows);
Mat A;
vconcat(AoddRows,AevenRows,A);
Mat w, u, EigenVectors;
SVD::compute(A, w, u, EigenVectors);
Mat EigenVector_SmallestEigenvalue=EigenVectors.row(EigenVectors.rows-1);
Mat P=Mat::zeros(3,4, CV_64FC1);
int k=0;
for(int i=0;i<P.rows;i++)
{
for(int j=0;j<P.cols;j++)
{
P.at<double>(i, j) = EigenVector_SmallestEigenvalue.at<double>(0,k);
k++;
}
}
double abs_lambda = sqrt(P.at<double>(2,0) * P.at<double>(2,0) + P.at<double>(2,1) * P.at<double>(2,1) + P.at<double>(2,2) * P.at<double>(2,2));
P = P / abs_lambda;
Mat ProjectionMatrix = P;
Mat T= Mat::zeros(3,1, CV_64FC1);
Mat R = Mat::zeros(3,3, CV_64FC1);
Mat B = Mat::zeros(3,3, CV_64FC1);
Mat b = Mat::zeros(3,1,CV_64FC1);
P.row(0).colRange(0,3).copyTo(B.row(0));
P.row(1).colRange(0,3).copyTo(B.row(1));
P.row(2).colRange(0,3).copyTo(B.row(2));
P.col(3).copyTo(b.col(0));
Mat K=B*B.t();
double Center_x = K.at<double>(0,2);
double Center_y = K.at<double>(1,2);
double beta = sqrt(K.at<double>(1,1)-Center_y*Center_y);
double gemma = (K.at<double>(0,1)-Center_x*Center_y)/beta;
double alpha = sqrt(K.at<double>(0,0)-Center_x*Center_x-gemma*gemma);
Mat CameraMatrix=Mat::zeros(3,3, CV_64FC1);
CameraMatrix.at<double>(0,0)=alpha;
CameraMatrix.at<double>(0,1)=gemma;
CameraMatrix.at<double>(0,2)=Center_x;
CameraMatrix.at<double>(1,1)=beta;
CameraMatrix.at<double>(1,2)=Center_y;
CameraMatrix.at<double>(2,2)=1;
R = CameraMatrix.inv()*B;
T = CameraMatrix.inv()*b;
Mat newMatrix2D=Mat::zeros(numberofpoints,2, CV_64FC1);
for(int i=0;i<numberofpoints;i++)
{
double num_u = P.at<double>(0,0) * Matrix3D.at<double>(i,0)
+ P.at<double>(0,1) * Matrix3D.at<double>(i,1)
+ P.at<double>(0,2) * Matrix3D.at<double>(i,2)
+ P.at<double>(0,3);
double num_v = P.at<double>(1,0) * Matrix3D.at<double>(i,0)
+ P.at<double>(1,1) * Matrix3D.at<double>(i,1)
+ P.at<double>(1,2) * Matrix3D.at<double>(i,2)
+ P.at<double>(1,3);
double den = P.at<double>(2,0) * Matrix3D.at<double>(i,0)
+ P.at<double>(2,1) * Matrix3D.at<double>(i,1)
+ P.at<double>(2,2) * Matrix3D.at<double>(i,2)
+ P.at<double>(2,3);
newMatrix2D.at<double>(i,0)=num_u/den;
newMatrix2D.at<double>(i,1)=num_v/den;
}
Mat reprojMatrix=newMatrix2D;
Mat errorDiff=reprojMatrix-Matrix2D;
int size=errorDiff.rows;
double sum=0;
for(int i=0;i<errorDiff.rows;i++)
{
sum=sum + sqrt(errorDiff.at<double>(i,0) * errorDiff.at<double>(i,0)
+ errorDiff.at<double>(i,1) * errorDiff.at<double>(i,1));
}
sum=sum/size;
cout<<"Average Error="<<sum<<endl;
cout<<"Translation Matrix="<<T<<endl<<endl;
cout<<"Rotational Matrix="<<R<<endl<<endl;
cout<<"Camera Matrix="<<CameraMatrix<<endl;
return 0;
}
SAMPLE 1:
const int numberofpoints = 30;
float p2d[numberofpoints][2] =
{{72, 169}, {72, 184}, {71, 264}, {96, 168}, {94, 261}, {94, 276}, {257, 133},
{254, 322}, {254, 337}, {278, 132}, {278, 146}, {275, 321}, {439, 228},
{439, 243}, {437, 328}, {435, 431}, {461, 226}, {459, 326}, {459, 342},
{457, 427}, {457, 444}, {612, 196}, {609, 291}, {605, 392}, {605, 406},
{634, 191}, {633, 206}, {630, 288}, {630, 303}, {627, 390}};
float p3d[numberofpoints][3] =
{{0, 0, 98}, {0, 0, 94}, {0, 0, 75}, {4, 4, 98}, {4, 4, 75}, {4, 4, 71},
{0, 96, 75}, {0, 96, 25}, {0, 96, 21}, {4, 100, 75},
{4, 100, 71}, {4, 100, 25}, {96, 0, 98}, {96, 0, 94},
{96, 0, 75}, {96, 0, 50}, {100, 4, 98}, {100, 4, 75},
{100, 4, 71}, {100, 4, 50}, {100, 4, 46}, {96, 96, 75},
{96, 96, 50}, {96, 96, 25}, {96, 96, 21}, {100, 100, 75},
{100, 100, 71}, {100, 100, 50}, {100, 100, 46}, {100, 100, 25}};
SAMPLE 2:
const int numberofpoints = 24;
float p2d[24][2] =
{{41, 291}, {41, 494}, {41, 509}, {64, 285}, {64, 303},
{64, 491}, {195, 146}, {216, 144}, {216, 160}, {431, 337},
{431, 441}, {431, 543}, {431, 558}, {452, 336}, {452, 349},
{452, 438}, {452, 457}, {452, 539}, {568, 195}, {566, 291},
{588, 190}, {587, 209}, {586, 289}, {586, 302}};
float p3d[24][3] =
{{0, 0, 75}, {0, 0, 25}, {0, 0, 21}, {4, 4, 75}, {4, 4, 71}, {4, 4, 25},
{0, 96, 75 }, {4, 100, 75}, {4, 100, 71}, {96, 0, 75}, {96, 0, 50}, {96, 0, 25},
{96, 0, 21}, {100, 4, 75}, {100, 4, 71}, {100, 4, 50}, {100, 4, 46}, {100, 4, 25}, {96, 96, 75 }, {96, 96, 50 }, {100, 100, 75}, {100, 100, 71}, {100, 100, 50}, {100, 100, 46}};
SAMPLE 3:
const int numberofpoints = 33;
float p2d[numberofpoints][2] =
{{45, 310}, {43, 412}, {41, 513}, {41, 530}, {68, 305},
{68, 321}, {66, 405}, {66, 423}, {64, 509}, {201, 70}, {201, 84},
{200, 155}, {198, 257}, {218, 259}, {218, 271}, {441, 364},
{439, 464}, {437, 569}, {437, 586}, {462, 361}, {462, 376},
{460, 462}, {460, 479}, {459, 565}, {583, 117}, {583, 131},
{580, 215}, {576, 313}, {603, 113}, {600, 214}, {599, 229},
{596, 311}, {596, 323}};
float p3d[numberofpoints][3] =
{{0, 0, 75}, {0, 0, 50}, {0, 0, 25}, {0, 0, 21}, {4, 4, 75}, {4, 4, 71},
{4, 4, 50}, {4, 4, 46}, {4, 4, 25}, {0, 96, 98}, {0, 96, 94}, {0, 96, 75},
{0, 96, 50}, {4, 100,75}, {4, 100,71}, {96, 0, 75}, {96, 0, 50}, {96, 0, 25},
{96, 0, 21}, {100, 4,75}, {100, 4,71}, {100, 4,50}, {100, 4,46}, {100, 4,25},{96, 96,98}, {96, 96,94}, {96, 96,75}, {96, 96,50}, {100, 100, 98}, {100, 100, 75},{100, 100, 71}, {100, 100, 50}, {100, 100, 46}};
EDIT:
I've taken one of the 3D coordinate point sets you've provided, and then tried to project them on a virtual camera (to prevent any errors with 3d-2d point correspondences). The alpha, beta and gamma are estimated correctly in this case, but u and v (that you are looking for) were for some reason negative in all of my experiments. Perhaps there's a bug in my code, but maybe this is due to inaccuracy of OpenCV solveZ implementation (I've had problems with OpenCV's eigenvalue/vector computation before and try to use OpenBLAS when high accuracy is needed).
Another possible reason - as authors state, this approach performs algebraic minimization, which may produce physically meaningless results. Perhaps you should check remaining part of paper or try a different approach.
Here's the updated code:
#include <opencv2/opencv.hpp>
#include <iostream>
int main()
{
// generate random 3d points
const int numberofpoints = 33;
float p3d_[numberofpoints][3] =
{{0, 0, 75}, {0, 0, 50}, {0, 0, 25}, {0, 0, 21}, {4, 4, 75}, {4, 4, 71},
{4, 4, 50}, {4, 4, 46}, {4, 4, 25}, {0, 96, 98}, {0, 96, 94}, {0, 96, 75},
{0, 96, 50}, {4, 100,75}, {4, 100,71}, {96, 0, 75}, {96, 0, 50}, {96, 0, 25},
{96, 0, 21}, {100, 4,75}, {100, 4,71}, {100, 4,50}, {100, 4,46}, {100, 4,25},{96, 96,98}, {96, 96,94}, {96, 96,75}, {96, 96,50}, {100, 100, 98}, {100, 100, 75},{100, 100, 71}, {100, 100, 50}, {100, 100, 46}};
std::vector<cv::Point3d> p3d;
for (int i = 0; i < numberofpoints; i++) {
cv::Point3d X(p3d_[i][0], p3d_[i][1], p3d_[i][2]);
p3d.push_back(X);
}
// set up virtual camera
const cv::Size imgSize(1600, 1200);
const double fx = 100;
const double fy = 120;
cv::Mat1d K = (cv::Mat1d(3, 3) << fx, 0, imgSize.width/2,
0, fy, imgSize.height/2,
0, 0, 1);
std::cout << "K = " << std::endl;
std::cout << K << std::endl << std::endl;
cv::Mat1d t = (cv::Mat1d(3, 1) << 10, 20, 20);
cv::Mat1d rvecDeg = (cv::Mat1d(3, 1) << 10, 20, 30);
// project point on camera
std::vector<cv::Point2d> p2d;
cv::projectPoints(p3d,
rvecDeg*CV_PI/180,
t,
K,
cv::Mat(),
p2d);
// estimate projection
cv::Mat1d G = cv::Mat1d::zeros(numberofpoints*2, 12);
for (int i = 0; i < numberofpoints; i++) {
const double X = p3d[i].x;
const double Y = p3d[i].y;
const double Z = p3d[i].z;
G(i*2 + 0, 0) = X;
G(i*2 + 0, 1) = Y;
G(i*2 + 0, 2) = Z;
G(i*2 + 0, 3) = 1;
G(i*2 + 1, 4) = X;
G(i*2 + 1, 5) = Y;
G(i*2 + 1, 6) = Z;
G(i*2 + 1, 7) = 1;
const double u = p2d[i].x;
const double v = p2d[i].y;
G(i*2 + 0, 8) = u*X;
G(i*2 + 0, 9) = u*Y;
G(i*2 + 0, 10) = u*Z;
G(i*2 + 0, 11) = u;
G(i*2 + 1, 8) = v*X;
G(i*2 + 1, 9) = v*Y;
G(i*2 + 1, 10) = v*Z;
G(i*2 + 1, 11) = v;
}
std::cout << "G = " << std::endl;
std::cout << G << std::endl << std::endl;
cv::Mat1d p;
cv::SVD::solveZ(G, p);
cv::Mat1d P = p.reshape(0, 3).clone();
P /= P(2, 3);
std::cout << "p = " << std::endl;
std::cout << p << std::endl << std::endl;
std::cout << "P = " << std::endl;
std::cout << P << std::endl << std::endl;
cv::Mat1d B(3, 3);
cv::Mat1d b(3, 1);
P.colRange(0, 3).copyTo(B);
P.col(3).copyTo(b);
std::cout << "B = " << std::endl;
std::cout << B << std::endl << std::endl;
std::cout << "b = " << std::endl;
std::cout << b << std::endl << std::endl;
cv::Mat1d K_ = B*B.t();
K_ /= K_(2, 2);
std::cout << "K_ = " << std::endl;
std::cout << K_ << std::endl << std::endl;
const double u = K_(0, 2);
const double v = K_(1, 2);
const double ku = K_(0, 0);
const double kv = K_(1, 1);
const double kc = K_(0, 1);
const double beta = sqrt(kv - v*v);
const double gamma = (kc - u*v)/beta;
const double alpha = sqrt(ku - u*u - gamma*gamma);
cv::Mat1d A = (cv::Mat1d(3, 3) << alpha, gamma, u,
0, beta, v,
0, 0, 1);
std::cout << "A = " << std::endl;
std::cout << A << std::endl << std::endl;
return 0;
}
And output:
K =
[100, 0, 800;
0, 120, 600;
0, 0, 1]
G =
[0, 0, 75, 1, 0, 0, 0, 0, 0, 0, 63156.71331987197, 842.0895109316264;
0, 0, 0, 0, 0, 0, 75, 1, 0, 0, 46451.70410142483, 619.3560546856644;
0, 0, 50, 1, 0, 0, 0, 0, 0, 0, 42144.23132613153, 842.8846265226306;
0, 0, 0, 0, 0, 0, 50, 1, 0, 0, 31473.60945095979, 629.4721890191959;
0, 0, 25, 1, 0, 0, 0, 0, 0, 0, 21113.32803626328, 844.5331214505311;
0, 0, 0, 0, 0, 0, 25, 1, 0, 0, 16261.14346086196, 650.4457384344785;
0, 0, 21, 1, 0, 0, 0, 0, 0, 0, 17744.50634900177, 844.9764928096083;
0, 0, 0, 0, 0, 0, 21, 1, 0, 0, 13777.82037617329, 656.0866845796805;
4, 4, 75, 1, 0, 0, 0, 0, 3374.871998456306, 3374.871998456306, 63278.84997105574, 843.7179996140766;
0, 0, 0, 0, 4, 4, 75, 1, 2506.953106676701, 2506.953106676701, 47005.37075018814, 626.7382766691752;
4, 4, 71, 1, 0, 0, 0, 0, 3375.547822963469, 3375.547822963469, 59915.97385760157, 843.8869557408673;
0, 0, 0, 0, 4, 4, 71, 1, 2513.243523511581, 2513.243523511581, 44610.07254233056, 628.3108808778952;
4, 4, 50, 1, 0, 0, 0, 0, 3380.337247599766, 3380.337247599766, 42254.21559499707, 845.0843118999414;
0, 0, 0, 0, 4, 4, 50, 1, 2557.822370597248, 2557.822370597248, 31972.7796324656, 639.4555926493119;
4, 4, 46, 1, 0, 0, 0, 0, 3381.587616222724, 3381.587616222724, 38888.25758656133, 845.396904055681;
0, 0, 0, 0, 4, 4, 46, 1, 2569.460510014068, 2569.460510014068, 29548.79586516178, 642.365127503517;
4, 4, 25, 1, 0, 0, 0, 0, 3391.684639092943, 3391.684639092943, 21198.02899433089, 847.9211597732357;
0, 0, 0, 0, 4, 4, 25, 1, 2663.441243136111, 2663.441243136111, 16646.50776960069, 665.8603107840277;
0, 96, 98, 1, 0, 0, 0, 0, 0, 76956.82972653268, 78560.09701250211, 801.6336429847155;
0, 0, 0, 0, 0, 96, 98, 1, 0, 65682.8899983677, 67051.28354000035, 684.1967708163302;
0, 96, 94, 1, 0, 0, 0, 0, 0, 76853.25603860538, 75252.14653780111, 800.5547504021395;
0, 0, 0, 0, 0, 96, 94, 1, 0, 65937.37528926283, 64563.67997073651, 686.8476592631544;
0, 96, 75, 1, 0, 0, 0, 0, 0, 76268.94727818707, 59585.11506108365, 794.4682008144487;
0, 0, 0, 0, 0, 96, 75, 1, 0, 67373.04865228917, 52635.19425960092, 701.8025901280123;
0, 96, 50, 1, 0, 0, 0, 0, 0, 75153.33695072016, 39142.36299516675, 782.8472599033349;
0, 0, 0, 0, 0, 96, 50, 1, 0, 70114.15427855898, 36517.78868674947, 730.3557737349894;
4, 100, 75, 1, 0, 0, 0, 0, 3182.802966382433, 79570.07415956081, 59677.55561967062, 795.7007415956082;
0, 0, 0, 0, 4, 100, 75, 1, 2830.826944396773, 70770.67360991932, 53078.00520743949, 707.7067360991932;
4, 100, 71, 1, 0, 0, 0, 0, 3176.842851499092, 79421.07128747732, 56388.96061410889, 794.2107128747731;
0, 0, 0, 0, 4, 100, 71, 1, 2846.678125949429, 71166.95314873573, 50528.53673560237, 711.6695314873573;
96, 0, 75, 1, 0, 0, 0, 0, 94501.57967461165, 0, 73829.35912079035, 984.3914549438714;
0, 0, 0, 0, 96, 0, 75, 1, 69392.97525695754, 0, 54213.26191949808, 722.8434922599744;
96, 0, 50, 1, 0, 0, 0, 0, 102665.427189569, 0, 53471.57666123386, 1069.431533224677;
0, 0, 0, 0, 96, 0, 50, 1, 76872.21110081286, 0, 40037.60994834002, 800.7521989668005;
96, 0, 25, 1, 0, 0, 0, 0, 134150.4060603281, 0, 34935.00157821043, 1397.400063128417;
0, 0, 0, 0, 96, 0, 25, 1, 105716.8927391909, 0, 27530.44081749762, 1101.217632699905;
96, 0, 21, 1, 0, 0, 0, 0, 150007.0124893856, 0, 32814.03398205309, 1562.573046764433;
0, 0, 0, 0, 96, 0, 21, 1, 120243.7808209231, 0, 26303.32705457694, 1252.539383551283;
100, 4, 75, 1, 0, 0, 0, 0, 98699.75231231008, 3947.990092492403, 74024.81423423256, 986.9975231231008;
0, 0, 0, 0, 100, 4, 75, 1, 73361.21263523313, 2934.448505409325, 55020.90947642484, 733.6121263523312;
100, 4, 71, 1, 0, 0, 0, 0, 99628.75712124966, 3985.150284849986, 70736.41755608725, 996.2875712124966;
0, 0, 0, 0, 100, 4, 71, 1, 74265.21217550985, 2970.608487020394, 52728.30064461199, 742.6521217550985;
100, 4, 50, 1, 0, 0, 0, 0, 107383.6098967354, 4295.344395869415, 53691.80494836769, 1073.836098967354;
0, 0, 0, 0, 100, 4, 50, 1, 81811.33387099921, 3272.453354839968, 40905.6669354996, 818.113338709992;
100, 4, 46, 1, 0, 0, 0, 0, 109823.0621321851, 4392.922485287403, 50518.60858080514, 1098.230621321851;
0, 0, 0, 0, 100, 4, 46, 1, 84185.12534995917, 3367.405013998367, 38725.15766098122, 841.8512534995917;
100, 4, 25, 1, 0, 0, 0, 0, 141059.7182762867, 5642.388731051467, 35264.92956907167, 1410.597182762867;
0, 0, 0, 0, 100, 4, 25, 1, 114581.0097655446, 4583.240390621784, 28645.25244138615, 1145.810097655446;
96, 96, 98, 1, 0, 0, 0, 0, 83904.83905262669, 83904.83905262669, 85652.85653288974, 874.0087401315279;
0, 0, 0, 0, 96, 96, 98, 1, 72995.44277461393, 72995.44277461393, 74516.18116575171, 760.3691955688951;
96, 96, 94, 1, 0, 0, 0, 0, 84021.59669072446, 84021.59669072446, 82271.1467596677, 875.2249655283798;
0, 0, 0, 0, 96, 96, 94, 1, 73575.81721996517, 73575.81721996517, 72042.98769454923, 766.4147627079706;
96, 96, 75, 1, 0, 0, 0, 0, 84712.67045349024, 84712.67045349024, 66181.77379178925, 882.4236505571899;
0, 0, 0, 0, 96, 96, 75, 1, 77010.98050603706, 77010.98050603706, 60164.82852034145, 802.1977136045526;
96, 96, 50, 1, 0, 0, 0, 0, 86206.34878960505, 86206.34878960505, 44899.13999458597, 897.9827998917193;
0, 0, 0, 0, 96, 96, 50, 1, 84435.70020728504, 84435.70020728504, 43976.92719129429, 879.5385438258859;
100, 100, 98, 1, 0, 0, 0, 0, 87539.46210370047, 87539.46210370047, 85788.67286162646, 875.3946210370046;
0, 0, 0, 0, 100, 100, 98, 1, 76664.75192364832, 76664.75192364832, 75131.45688517536, 766.6475192364833;
100, 100, 75, 1, 0, 0, 0, 0, 88416.27638616599, 88416.27638616599, 66312.20728962449, 884.16276386166;
0, 0, 0, 0, 100, 100, 75, 1, 81008.14162095707, 81008.14162095707, 60756.10621571781, 810.0814162095708;
100, 100, 71, 1, 0, 0, 0, 0, 88614.85267838663, 88614.85267838663, 62916.5454016545, 886.1485267838663;
0, 0, 0, 0, 100, 100, 71, 1, 81991.80970097007, 81991.80970097007, 58214.18488768875, 819.9180970097007;
100, 100, 50, 1, 0, 0, 0, 0, 90038.77512167525, 90038.77512167525, 45019.38756083763, 900.3877512167526;
0, 0, 0, 0, 100, 100, 50, 1, 89045.35592350175, 89045.35592350175, 44522.67796175087, 890.4535592350175;
100, 100, 46, 1, 0, 0, 0, 0, 90415.3917433265, 90415.3917433265, 41591.08020193018, 904.153917433265;
0, 0, 0, 0, 100, 100, 46, 1, 90910.96508045508, 90910.96508045508, 41819.04393700934, 909.1096508045508]
p =
[0.006441365659365744;
-0.006935698812720837;
-0.03488896901596035;
-0.7622591852949104;
0.004771957238156334;
-0.01133107207303337;
-0.02452697177582621;
-0.6456783687203944;
-1.258567018901194e-05;
1.123537587555102e-05;
4.154374841821949e-05;
0.0008967755121116598]
P =
[7.182807260423624, -7.734041261217285, -38.90490824599617, -849.9999999999995;
5.321239455925471, -12.63534956072988, -27.35018011148848, -719.9999999999993;
-0.0140343597913107, 0.01252863813051139, 0.04632569451009583, 1]
B =
[7.182807260423624, -7.734041261217285, -38.90490824599617;
5.321239455925471, -12.63534956072988, -27.35018011148848;
-0.0140343597913107, 0.01252863813051139, 0.04632569451009583]
b =
[-849.9999999999995;
-719.9999999999993;
1]
K_ =
[649999.9999999985, 479999.9999999995, -799.9999999999992;
479999.9999999995, 374400.0000000002, -600.0000000000002;
-799.9999999999992, -600.0000000000002, 1]
A =
[99.99999999999883, -1.940255363782255e-12, -799.9999999999992;
0, 119.9999999999995, -600.0000000000002;
0, 0, 1]
What other values are you getting? Generally, the camera center is never exactly at the center of the image, but it should be reasonably close.

Attachments in Redmine Python Rest API

dir(sttachments) will give the following value.
[u'author', u'content_type', u'content_url', u'created_on', u'description', u'filename', u'filesize', u'id', u'thumbnail_url']
But I cannot able to read the container_id of attachments, due to this I can't able to map my attachments with issue.
How to read the container_id of attachments and how to map the attachments with issue
from redmine import Redmine
conn_red = Redmine('http://localhost:3000', username='admin', password='admin')
issue = conn_red.issue.all()
attachment = {}
att_list = []
for id in issue:
for att in id.attachments:
attachment['file_name'] = att.content_url
attachment['created_date'] = att.created_on
att_list.append((0, 0, attachment))
print "issue",id.id,"attachments",att.id,"att_list",att_list
The above code give the following result
issue 7 attachments 5 att_list [(0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)})]
issue 6 attachments 5 att_list [(0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)})]
issue 5 attachments 5 att_list [(0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)})]
issue 4 attachments 5 att_list [(0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)})]
issue 3 attachments 5 att_list [(0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/5/3BasicSkills_Eng.pdf', 'created_date': datetime.datetime(2016, 6, 2, 8, 43, 51)})]
issue 2 attachments 3 att_list [(0, 0, {'file_name': u'http://localhost:3000/attachments/download/3/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 15, 42)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/3/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 15, 42)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/3/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 15, 42)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/3/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 15, 42)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/3/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 15, 42)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/3/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 15, 42)})]
issue 1 attachments 4 att_list [(0, 0, {'file_name': u'http://localhost:3000/attachments/download/4/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 16, 40)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/4/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 16, 40)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/4/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 16, 40)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/4/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 16, 40)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/4/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 16, 40)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/4/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 16, 40)}), (0, 0, {'file_name': u'http://localhost:3000/attachments/download/4/page.png', 'created_date': datetime.datetime(2016, 5, 30, 8, 16, 40)})]
The second list is repeated with same data.
EDIT ONE
I here by attach the screen shot of attachments table, here container_id is associated with issues number, so I want to prepare a list based on issue with attachments,
For example Issue no 1 i.e container_id 1 has two attachments i.e ID 1 and 4, so I have to prepare a list [(0,0,{filename:'vignesh.png'}),(0,0,{filename:'page.png'})], so issue number 1 has two issue that should be created as a list.
So as far as I understood the problem is the repeated data, but it has nothing to do with Python-Redmine or Redmine's REST API. The problem happens because your att_list = [] is defined on module level, if you define it inside first for() loop then everything should work as expected, e.g.:
from redmine import Redmine
conn_red = Redmine('http://localhost:3000', username='admin', password='admin')
issues = conn_red.issue.all()
for issue in issues:
att_list = []
for att in issue.attachments:
attachment = {
'file_name': att.content_url,
'created_date': att.created_on
}
att_list.append((0, 0, attachment))
print "issue",issue.id, "att_list", att_list