def fit_refs_to_image(imagetoprocess, outputdirectory, signaturecollection, startDOY, doyinterval, bestguess, threshold=None, ndvalue=-3000, fitmethod=None, subset=None, meantype=None, workers=4, timebounds=None, xbounds=None, ybounds=None): """ imagepath = "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/ARC_Testing/ClipTesting/ENVI_1/test_clip_envi_3.dat" outdir = "/Users/phoetrymaster/Documents/School/Geography/Thesis/Data/OutImages/" newfoldername = "Testing" drivercode1 = 'ENVI' #ndvalue = -3000 startDOY = 1 interval = 16 threshold = 500 bestguess = 0 fitmthd = 'SLSQP' mean = geometric # Acceptable values are geometric (geometric mean) and arithmetic (arithmetic mean). It is an optional argument for the classifier. refs = { 'soy': {1: 174.5, 97: 1252.25, 65: 1139.5, 209: 7659.0, 273: 4606.75, 337: 1371.75, 17: 1055.5, 33: 1098.0, 49: 1355.25, 129: 1784.75, 257: 6418.0, 321: 1644.5, 305: 1472.75, 193: 5119.75, 289: 1878.75, 177: 3439.5, 241: 7565.75, 81: 1205.5, 225: 7729.75, 145: 1736.25, 161: 1708.25, 353: 1358.25, 113: 1340.0}, 'corn': {1: 392.25, 97: 1433.25, 65: 1258.5, 209: 6530.0, 273: 1982.5, 337: 1658.5, 17: 1179.25, 33: 1196.75, 49: 1441.25, 129: 1885.25, 257: 2490.25, 321: 1665.75, 305: 1439.0, 193: 6728.25, 289: 1634.5, 177: 6356.75, 241: 4827.25, 81: 1355.75, 225: 5547.5, 145: 2196.5, 161: 3143.25, 353: 1704.75, 113: 1716.5}, 'wheat': {1: 719.75, 97: 6594.75, 65: 1935.25, 209: 2013.5, 273: 1493.5, 337: 1498.25, 17: 1816.5, 33: 1815.0, 49: 1985.25, 129: 6758.0, 257: 1685.75, 321: 1582.5, 305: 1163.25, 193: 2186.25, 289: 1264.5, 177: 2222.5, 241: 2301.0, 81: 4070.5, 225: 1858.0, 145: 6228.5, 161: 3296.5, 353: 1372.5, 113: 7035.25} } sys.exit(fit_refs_to_image(imagepath, outdir, newfoldername, refs, startDOY, interval, threshold, bestguess, fitmthd, meantype=mean)) """ #TODO docstrings start = dt.now() print(start) try: print("\nProcessing {0}...".format(imagetoprocess)) print("Outputting files to {0}\n".format(outputdirectory)) #Open multi-date image to analyze image = openImage(imagetoprocess) imageproperties = gdalProperties(image) print("Input image dimensions are {0} columns by {1} rows and contains {2} bands.".format(imageproperties.cols, imageproperties.rows, imageproperties.bands)) array = read_image_into_array(image) # Read all bands into a 3d array representing the image stack (x, y, time orientation) image = "" if timebounds: timebounds = (bestguess + timebounds[0], bestguess + timebounds[1]) else: timebounds = (bestguess - 10, bestguess + 10) if not xbounds: xbounds = (0.6, 1.4) if not ybounds: ybounds = (0.6, 1.4) bounds = (xbounds, ybounds, timebounds) print(bounds) if subset: subset = get_px_coords_from_shapefile(imagetoprocess, subset) processes = [] for signum, signature in enumerate(signaturecollection.signatures, start=1): p = multiprocessing.Process(target=process_reference, args=(outputdirectory, signature, array, imageproperties, startDOY, doyinterval, bestguess, ndvalue), kwargs={"subset": subset, "fitmthd": fitmethod, "meantype": meantype, "thresh": threshold, "bounds": bounds}) #TODO: Problem with joining/starting processes--original thread closes before others are completed -- believe this is now fixed. p.start() processes.append(p) if len(processes) == workers: for p in processes: p.join() processes.remove(p) for p in processes: p.join() except Exception as e: import traceback exc_type, exc_value, exc_traceback = sys.exc_info() print(e) traceback.print_exception(exc_type, exc_value, exc_traceback, limit=2, file=sys.stdout) finally: print(dt.now() - start)
def classify_and_assess_accuracy(outputdir, cropimgpath, searchstringsvals, filevalist, nodata, thresholdsandlength, classifiedimagename=None, numberofprocesses=4): """ """ #TODO Docstring #from multiprocessing import Pool #pool = Pool(numberofprocesses) if classifiedimagename is None: today = dt.now() classifiedimagename = today.strftime("%Y-%m-%d_%H%M_") + os.path.splitext(os.path.basename(cropimgpath))[0] classificationimage = os.path.join(outputdir, classifiedimagename + ".tif") accuracyimage = os.path.join(outputdir, classifiedimagename + "_accuracy.tif") accuracyreport = os.path.join(outputdir, classifiedimagename + ".txt") #np.set_printoptions(threshold=np.nan) # For debug: Makes numpy print whole contents of an array. #Crop image is constant for all iterations cropimg = openImage(cropimgpath) cropimgproperties = gdalProperties(cropimg) croparray = read_image_into_array(cropimg) cropimg = None arraylist = [(read_image_into_array(openImage(f[0])), f[1]) for f in filevalist] # fit images with crop vals writestring = "The fit rasters and truth values used for this classification process are:\n" for f in filevalist: writestring = writestring + "\t{0}\t{1}\n".format(f[0], f[1]) writestring = writestring + "\n" bestacc = 0 bestthresh = None thresholdlist, lengthofthresholdlist = thresholdsandlength try: if lengthofthresholdlist == 1: writestring = writestring + "\n\n**Only using a single threshold value--not iterating.**\n\n" bestthresh = thresholdlist[0] else: #TODO: Refactor to allow use of multiprocessing.Pool.map -- need to reason about the output/logging for thresh in thresholdlist: start = dt.now() accuracy, classification, outstring = classify_with_threshold(croparray, arraylist, searchstringsvals, thresh, nodata, cropimgproperties.nodata) writestring = writestring + outstring if accuracy > bestacc: bestacc = accuracy bestthresh = thresh elapsed = dt.now() - start toprint = [thresh, "{}:{}".format(elapsed.seconds, str(elapsed.microseconds).zfill(6)), accuracy, bestacc, bestthresh] width = (6 * len(arraylist)) sys.stdout.write("Thresh: {: <{width}} Time: {} Acc: {: <14} Best: {: <14} at {}\r".format(*toprint, width=width)) sys.stdout.flush() except Exception as e: import traceback exc_type, exc_value, exc_traceback = sys.exc_info() print e traceback.print_exception(exc_type, exc_value, exc_traceback, limit=2, file=sys.stdout) finally: accuracy, classificationarray, outstring = classify_with_threshold(croparray, arraylist, searchstringsvals, bestthresh, nodata, cropimgproperties.nodata) writestring = writestring + outstring accuracyarray = find_correct_incorrect_array(croparray, classificationarray, ndvalue=nodata, truthndvalue=cropimgproperties.nodata) with open(accuracyreport, 'w') as text: text.write("Classification using fit images from {0}\n\n".format(os.path.dirname(filevalist[0][0]))) text.write("{0}\nBest:\n{1} {2}".format(writestring, bestthresh, accuracy)) print("\n{0}, {1}".format(bestthresh, accuracy)) driver = gdal.GetDriverByName("ENVI") driver.Register() write_output_image(cropimgproperties, classificationimage, classificationarray, nodata) write_output_image(cropimgproperties, accuracyimage, accuracyarray, nodata) print("outputted") return 0