Python find_local_max Exemples

Exemple #1

Fichier : basic_test.py Projet : Resonanz/trackpy

def test_iden():
    SEED = 314
    random.seed(SEED)

    X = range(10, 200, 20)
    Y = range(10, 200, 20)
    random.shuffle(X)
    random.shuffle(Y)
    img = tid.gen_fake_data(np.vstack([X, Y]), 5, 2.5, (210, 210))
    bp_img = tid.band_pass(img, 2, 2.5)

    res_lm = tid.find_local_max(bp_img, 3, .5)

    locs, mass, r2 = tid.subpixel_centroid(bp_img, res_lm, 3)
    assert np.all(np.abs(locs - res_lm) < .05)

Exemple #2

Fichier : basic_test.py Projet : elizan/trackpy

def test_iden():
    SEED = 314
    random.seed(SEED)

    X = range(10, 200, 20)
    Y = range(10, 200, 20)
    random.shuffle(X)
    random.shuffle(Y)
    img = tid.gen_fake_data(np.vstack([X, Y]), 5, 2.5, (210, 210))
    bp_img = tid.band_pass(img, 2, 2.5)

    res_lm = tid.find_local_max(bp_img, 3, .5)

    locs, mass, r2 = tid.subpixel_centroid(bp_img, res_lm, 3)
    assert np.all(np.abs(locs - res_lm) < .05)

Exemple #3

import trackpy.tracking as pt
import trackpy.identification as tid
import matplotlib.pyplot as plt
import random
import numpy as np

X = range(10, 200, 20)
Y = range(10, 200, 20)
random.shuffle(X)
random.shuffle(Y)

img = tid.gen_fake_data(np.vstack([X, Y]), 5, 2.5, (210, 210))
bp_img = tid.band_pass(img, 2, 2.5)

res_lm = tid.find_local_max(bp_img, 3, .5)

locs, mass, r2 = tid.subpixel_centroid(bp_img, res_lm, 3)

# make figure
fig = plt.figure()
ax = fig.gca()
# display image
ax.imshow(bp_img, cmap='gray', interpolation='nearest')

# add pixval like output
ax.format_coord = lambda x, y: 'r=%d,c=%d,v=%0.2f' % (
    int(x + .5), int(y + .5), bp_img[int(x + .5), int(y + .5)]
    if int(x + .5) < bp_img.shape[0] and int(y + .5) < bp_img.shape[1] else 0)

ax.plot(*locs, linestyle='none', marker='o')

Exemple #4

Fichier : identification_script_testing.py Projet : pfigliozzi/Python_Data_Analysis

root = Tkinter.Tk()
root.withdraw()
f_path,f_name = os.path.split(tkFileDialog.askopenfilename())
im= plt.imread(os.path.join(f_path,f_name))
'''X = range(10, 200, 20)
Y = range(10, 200, 20)
Z = range(100)
random.shuffle(X)
random.shuffle(Y)

img = tid.gen_fake_data(np.vstack([X, Y]), 5, 2.5, (210, 210))'''
bp_img = tid.band_pass(im, 2, 2.5)


res_lm = tid.find_local_max(bp_img, 3, .5)

locs, mass, r2 = tid.subpixel_centroid(bp_img, res_lm, 3)

# make figure
fig = plt.figure()
ax = fig.gca()
# display image
ax.imshow(bp_img, cmap='gray', interpolation='nearest')

# add pixval like output
ax.format_coord = lambda x, y: 'r=%d,c=%d,v=%0.2f' % (int(x + .5),
                                                      int(y + .5),
                                                      bp_img[int(x + .5), int(y + .5)] if
                                                      int(x + .5) < bp_img.shape[0] and int(y + .5) < bp_img.shape[1] else 0)

Exemple #5

Fichier : python_iden_script.py Projet : pfigliozzi/Python_Data_Analysis

def iden_image(img_array, p_rad, hwhm, d_rad, threshold, mask_rad):
	'''Takes an image array and all the parameters necessary to do the identification'''
	bp_img = tid.band_pass(img_array, p_rad, hwhm)
	res_lm = tid.find_local_max(bp_img, d_rad, threshold)
	locs, mass, r2 = tid.subpixel_centroid(bp_img, res_lm, mask_rad)
	return locs, mass, r2