In [3]:

    

import plotly
import plotly.plotly as py
import plotly.graph_objs as go
plotly.tools.set_credentials_file(username='otakulucy404', api_key='5y4acheGm5DgwjvuptMD')

import numpy

x= numpy.load("saveX.npy")
print(x)
y = numpy.load("saveY.npy")
z = numpy.load("saveZ.npy")
trace1 = go.Scatter3d(
    x=x,
    y=y,
    z=z,
    mode='markers',
     marker=dict(
        size=2,
        color=z,                # set color to an array/list of desired values
        colorscale='Viridis',   # choose a colorscale
        opacity=0.8
    )
)

data = [trace1]
layout = go.Layout(
    margin=dict(
        l=0,
        r=0,
        b=0,
        t=0
    )
)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='simple-3d-scatter')


    

    




[0 0 0 ..., 0 0 0]
High five! You successfuly sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~otakulucy404/0 or inside your plot.ly account where it is named 'simple-3d-scatter'






    
Out[3]:

In [32]:

    

import cv2
import numpy as np
import scipy.io as sio
import matplotlib.pyplot as plt

import Line_feat_contours as lfc
import classify_curves as cc
import label_curves as lc
import merge_lines_v4 as merge_lines_v4
import util as util
from drawedgelist import drawedgelist
from edge_detect import edge_detect
from line_match import line_match
from lineseg import lineseg

np.set_printoptions(threshold=np.nan)


    

In [34]:

    

if __name__ == '__main__':
    # This is the directory to save images to
    path = 'outputImg\\'

    for numImg in [3]:
        ##These methods are for the picture resizing
        mouseX = []
        mouseY = []
        numC = 0
    
        ###Event 4 means that the right key was clicked
        ###This saves the points that are clicked on the image
        def choosePoints(event,x,y,flags,param):
            global mouseX,mouseY, numC

            if event == 4:
                #cv2.circle(img,(x,y),100,(255,0,0),-1)
                numC += 1
                mouseX.append(x)
                mouseY.append(y)
                
        #Opens up the color image for user to click on
        orig_imgC = cv2.imread('img/clearn%d.png' %numImg, -1)
        cv2.imshow('image',orig_imgC)
        cv2.setMouseCallback('image', choosePoints)
        
        #checks and makes sure 2 points were clicked
        #if 2 points were clicked it exits the loop
        while(numC != 2):
            key = cv2.waitKey(1) & 0xFF
            """if key == ord("r"):
                print(mouseX, mouseY, "printing mousey")
                break"""

        #Closes color image once user clicks twice
        cv2.destroyAllWindows()

        # Read in depth image, -1 means w/ alpha channel.
        # This keeps in the holes with no depth data as just black.
        depth_im = 'img/learn%d.png'%numImg
        img = cv2.imread(depth_im, -1)

        #crops the depth image
        img = img[mouseY[0]:mouseY[1], mouseX[0]:mouseX[1]]
        line_pairs_im = util.normalize_depth(img, colormap=cv2.COLORMAP_BONE)

        new_imgC = orig_imgC[mouseY[0]:mouseY[1], mouseX[0]:mouseX[1]]
        
        im_size = img.shape
        height = img.shape[0]
        width = img.shape[1]
        blank_image = np.zeros((height, width, 3), np.uint8)

        # evenly increases the contrast of the entire image
        # ref: http://docs.opencv.org/3.1.0/d5/daf/tutorial_py_histogram_equalization.html
        def clahe(img, iter=1):
            for i in range(0, iter):
                clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
                img = clahe.apply(img)
            return img

        # Open a copy of the depth image
        # to change the contrast on the full-sized image
        img2 = cv2.imread(depth_im, -1)
        old_height = img2.shape[0]
        old_width = img2.shape[1]
        old_blank_image = np.zeros((old_height, old_width, 3), np.uint8)
        util.depthToPC(img2, old_blank_image, 320, 240, 300, mouseY[0], mouseX[0])

        img2 = util.normalize_depth(img2)
        img2 = clahe(img2, iter=2)
        # crops the image
        img2 = img2[mouseY[0]:mouseY[1], mouseX[0]:mouseX[1]]
        
        
        #For convenience, to see what you cropped
        fig = plt.figure()
        a=fig.add_subplot(1,3,1)
        plt.imshow(orig_imgC)
        a.set_title('Original')
        a=fig.add_subplot(1,3,2)
        plt.imshow(new_imgC)
        a.set_title('Cropped')
        a=fig.add_subplot(1,3,3)
        plt.imshow(img2)
        a.set_title('Depth image')
        plt.show()


    

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