In [1]:

    

%load_ext autoreload
%autoreload 2
# System modules
import os
import warnings
import pickle
import glob
import sys
import logging
import datetime as dt
import time
from copy import deepcopy

# External modules
import numpy as np
import scipy.misc
import scipy.ndimage.filters
import matplotlib.pyplot as plt
from matplotlib import gridspec
from matplotlib.backends.backend_pdf import PdfPages
import pandas as pd
import dateutil.parser
import typhon.plots
import scipy.ndimage

import PIL

from skimage.segmentation import slic
from skimage.filters import threshold_otsu
from skimage import exposure

# Internal modules
import pyclamster
from pyclamster.clustering.preprocess import LCN
from pyclamster.functions import rbDetection
from pyclamster.positioning import Projection

plt.style.use('ggplot')

warnings.catch_warnings()
warnings.filterwarnings('ignore')

#logging.basicConfig(level=logging.INFO)


    

In [2]:

    

FILE_DIR = %pwd
BASE_DIR = os.path.dirname(FILE_DIR)
#image_directory = os.path.join(BASE_DIR, "examples", "images", "lex")
image_dir = '/home/tfinn/Data/example/'
trained_models = os.path.join(BASE_DIR, "data")
plot_dir = os.path.join(BASE_DIR, 'plots')
debug = False


    

In [3]:

    

predictor = pickle.load(open(os.path.join(trained_models, "kmeans.pk"), "rb"))
global_thres = np.mean(predictor.algorithm.cluster_centers_)
cams = []
cams.append(pickle.load(open(os.path.join(trained_models, 'sessions', 'FE3_session_new_600.pk'), mode='rb')))
cams.append(pickle.load(open(os.path.join(trained_models, 'sessions', 'FE4_session_new_600.pk'), mode='rb')))
cams[0].set_images(os.path.join(image_dir, 'cam3'))
cams[1].set_images(os.path.join(image_dir, 'cam4'))
matching = pyclamster.matching.Matching(greyscale=True)
dist = np.sqrt((cams[0].position.x-cams[1].position.x)**2+(cams[0].position.y-cams[1].position.y)**2)

height = {}


    

In [4]:

    

def save_heights(path):
    max_len = np.max([len(height[i]) for i in height.keys()])
    df_height = pd.DataFrame(columns=range(max_len), index=height.keys())
    for i in df_height.index:
        for k, j in enumerate(height[i]):
            if not j is None:
                if 200<j<15000:
                    df_height.ix[i, k] = j
    df_height = df_height.sort()
    df_height.to_json(path)


    

In [5]:

    

def generate_doppel(images):
    start_time = time.time()
    k = 0
    clouds = []
    height[images[0].time] = []
    for img in images:
        image_lcn = pyclamster.Image(img)
        image_lcn.data = LCN((50,50,3)).fit_transform(image_lcn.data)
        #image_lcn.data = image_lcn.data-scipy.ndimage.filters.uniform_filter(image_lcn.data, size=(50,50,3), mode='constant')
        #image_lcn.save(os.path.join(plot_dir, "lcn_{0:s}_{1:d}.png".format(img.time.strftime('%Y%m%d%H%M'), k)))
        #image_lcn = image_lcn.cut([50, 50, 350, 350])
        #w, h, _ = original_shape = image_lcn.data.shape
        rb_image = rbDetection(image_lcn.data)
        thres = 0.25*threshold_otsu(rb_image)+0.75*global_thres
        label = pyclamster.Labels(rb_image<thres)
        #label = predictor.predict(raw_image_lcn)
        #label.reshape((w, h), replace=True)
        #masks = label.getMaskStore()
        #cloud_mask_num = [1] # cloud - sky choose right number (0 or 1)
        #masks.denoise(cloud_mask_num, 1000)
        #cloud_labels_object, numLabels = masks.labelMask(cloud_mask_num)
        #cloud_labels_object, numLabels = masks.wsMask(cloud_mask_num, np.ones((14,14)))
        #cloud_labels_object, numLabels = masks.slicMask(img.cut([50,50,350,350]), cloud_mask_num, 30)
        #cloud_labels_object, numLabels = masks.regionMask(cloud_mask_num, (30, 30))
        segmented_image = slic(img.data, n_segments=30, sigma=1)+1
        #rb_image = img.data[:,:,0]-img.data[:,:,2]
        #thres = global_thres = threshold_otsu(rb_image[rb_image>10])
        #threshold_array = np.zeros_like(rb_image)
        #threshold_array[:] = global_thres
        #label = pyclamster.Labels(rb_image>threshold_array)
        masks = label.getMaskStore()
        masks.denoise([1], 960)
        cloud_labels_object, _ = masks.givenLabelMask(segmented_image, [1,])
        #cloud_labels_object, _ = masks.labelMask([1,])
        cloud_store = cloud_labels_object.getMaskStore()
        cloud_labels = np.unique(cloud_labels_object.labels)[1:]
        #cloud_rb_image_data = pyclamster.Image(PIL.Image.fromarray(rb_image[:,:]))
        clouds.append([cloud_store.getCloud(img, labels=[k, ]) for k in cloud_labels])
        if debug:
            j = 0
            fig = plt.figure()
            gs = gridspec.GridSpec(2,3)
            ax = plt.subplot(gs[0, 0])
            ax.imshow(img.data)
            ax = plt.subplot(gs[0, 1])
            ax.imshow(rb_image)
            ax = plt.subplot(gs[1, :])
            ax.hist(rb_image.reshape((-1)), bins=256)
            ax.axvline(x=thres)
            ax = plt.subplot(gs[0, 2])
            ax.imshow(cloud_labels_object.labels)
            plt.axis('off')
            plt.tight_layout()
            plt.savefig(os.path.join(plot_dir, "detection_{0:s}_{1:d}.pdf".format(img.time.strftime('%Y%m%d%H%M'), k)))
            print_label = label.labels
            print_label = np.ma.masked_where(print_label == 0, print_label)
            print_clouds = np.ma.masked_where(print_label == 0, cloud_labels_object.labels)
            img.save(os.path.join(plot_dir, "rectified_{0:s}_{1:d}.png".format(img.time.strftime('%Y%m%d%H%M'), k)))
            scipy.misc.imsave(
                os.path.join(plot_dir, "lables_kmean_{0:s}_{1:d}.png".format(img.time.strftime('%Y%m%d%H%M'), k)),
                             print_label)
            scipy.misc.imsave(
                os.path.join(plot_dir, "lables_used_{0:s}_{1:d}.png".format(img.time.strftime('%Y%m%d%H%M'), k)),
                             print_clouds)
            print('finished image {0:s} of camera {1:d}'.format(img.time.strftime('%Y%m%d%H%M'), k))
        k += 1
    t=0
    if not(not clouds[0] or not clouds[1]):
        matching_result, _ = matching.matching(clouds[0], clouds[1], min_match_prob=0.9)
        for result in matching_result:
            spatial_cloud = result[1]
            successful = spatial_cloud.oper_mode()
            if debug and successful:
                fig = plt.figure()
                ax = plt.subplot(1,3,1)
                ax.axis('off')
                ax.imshow(result[1].clouds[0].image.data)
                ax = plt.subplot(1,3,2)
                ax.axis('off')
                ax.imshow(result[0].prob_map, interpolation='nearest')
                ax = plt.subplot(1,3,3)
                ax.axis('off')
                ax.imshow(result[1].clouds[1].image.data)
                plt.tight_layout()
                plt.savefig(os.path.join(plot_dir, 'matching_{0:s}_{1:d}.png'.format(img.time.strftime('%Y%m%d%H%M'), t)))
                fig = plt.figure()
                gs = gridspec.GridSpec(1,3)
                ax = plt.subplot(gs[0])
                ax.axis('off')
                ax.imshow(spatial_cloud.clouds[0].image.data)
                ax = plt.subplot(gs[1])
                ax.axis('off')
                z = spatial_cloud.positions.z
                X, Y = np.meshgrid(range(z.shape[1]), range(z.shape[0]))
                CS = ax.contour(X, Y, z)
                manual_locations = [(-1, -1.4), (-0.62, -0.7), (-2, 0.5), (1.7, 1.2), (2.0, 1.4), (2.4, 1.7)]
                plt.clabel(CS, inline=1, fontsize=10)
                plt.text(0,0,'{:5.1} m height'.format(spatial_cloud.height), color='black')
                ax = plt.subplot(gs[2])
                ax.axis('off')
                ax.imshow(spatial_cloud.clouds[1].image.data)
                plt.tight_layout()
                plt.savefig(os.path.join(plot_dir, 'height{0:s}_{1:d}.png'.format(img.time.strftime('%Y%m%d%H%M'), t)))
            height[images[0].time].append(spatial_cloud.height)
            t+=1
    save_heights(os.path.join(trained_models, 'heights_temp.json'))
    try:      
        print('finished image {0:s} calculations with {1:d} heights (min: {2:.1f}, max: {3:.1f}), duration: {4:.1f} s'.format(images[0].time.strftime('%Y%m%d%H%M'), t, np.nanmin(height[images[0].time]), np.nanmax(height[images[0].time]), time.time()-start_time))
    except:
        print('finished image {0:s} calculations with {1:d} heights, duration: {2:.1f} s'.format(images[0].time.strftime('%Y%m%d%H%M'), t, time.time()-start_time))


    

In [ ]:

    

images_available = True
gens = [cams[0].iterate_over_rectified_images(), cams[1].iterate_over_rectified_images()]
images = [next(gens[0]), next(gens[1])]
while images_available:
    for k, img in enumerate(images):
        img.loadTimefromfilename('FE{0:d}_Image_%Y%m%d_%H%M%S_UTCp1.jpg'.format(k+3))
    if (images[0].time-images[1].time).seconds<60:
        generate_doppel(images)
        try:
            images = [next(gens[0]), next(gens[1])]
        except:
            images_available = False
    elif images[0].time<images[1].time:
        try:
            images[0] = next(gens[0])
        except:
            images_available = False
    elif images[0].time>images[1].time:
        try:
            images[1] = next(gens[1])
        except:
            images_available = False
print('finished image processing')


    

    




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finished image 201609031319 calculations with 5 heights (min: -448.1, max: 643.1), duration: 54.2 s
finished image 201609031320 calculations with 5 heights (min: -408.1, max: 58.8), duration: 37.7 s
finished image 201609031321 calculations with 9 heights (min: -578.1, max: 928.0), duration: 66.3 s
finished image 201609031322 calculations with 12 heights (min: -346.6, max: 280.0), duration: 96.7 s
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finished image 201609031335 calculations with 9 heights (min: -236.2, max: 463.6), duration: 72.1 s
finished image 201609031336 calculations with 10 heights (min: -90.8, max: 475.2), duration: 65.2 s
finished image 201609031337 calculations with 11 heights (min: -140.7, max: 1285.2), duration: 79.9 s
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finished image 201609031342 calculations with 10 heights (min: -1223.1, max: 745.7), duration: 63.1 s
finished image 201609031343 calculations with 18 heights (min: -3231.2, max: 1947.3), duration: 80.7 s
finished image 201609031344 calculations with 16 heights (min: -2778.7, max: 925.9), duration: 69.6 s
finished image 201609031345 calculations with 18 heights (min: -3412.2, max: 2835.4), duration: 76.1 s
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finished image 201609031350 calculations with 11 heights (min: -3608.1, max: 3050.3), duration: 57.2 s
finished image 201609031351 calculations with 15 heights (min: -2928.9, max: 397.8), duration: 46.4 s
finished image 201609031352 calculations with 9 heights (min: -2942.9, max: 2126.4), duration: 57.9 s
finished image 201609031353 calculations with 15 heights (min: -5698.4, max: 4019.4), duration: 78.3 s
finished image 201609031354 calculations with 11 heights (min: -3557.4, max: 6161.3), duration: 60.8 s
finished image 201609031355 calculations with 10 heights (min: -5672.8, max: 901.5), duration: 38.4 s
finished image 201609031356 calculations with 8 heights (min: -6772.4, max: 1147.4), duration: 29.5 s

In [16]:

    

max_len = np.max([len(height[i]) for i in height.keys()])
df_height = pd.DataFrame(columns=range(max_len), index=height.keys())
for i in df_height.index:
    for k, j in enumerate(height[i]):
        if not j is None:
            if 200<j<15000:
                df_height.ix[i, k] = j
df_height = df_height.sort()
df_height.to_json(os.path.join(trained_models, 'heights_20160903_4h_300_new.json'))


    

In [ ]: