Test

This notebook tests the functions and classes in myimagelib.

import os
cwd = os.getcwd()
import sys
sys.path.insert(0, os.path.abspath(os.path.join(cwd, "..", "..", "..", "src")))
import requests
import matplotlib.pyplot as plt
from io import BytesIO
from skimage.io import imread
google_download_handle = "https://drive.google.com/uc?export=download&id={}"
import numpy as np
import cv2
from myimagelib import readdata, show_progress, to8bit, bestcolor, rawImage, imfindcircles, xy_bin, PIV, compact_PIV, apply_mask
import pandas as pd

myImageLib

readdata

l = readdata(".", "ipynb")
l

	Name	Dir
0	compact_PIV	.\compact_PIV.ipynb
1	find_circles	.\find_circles.ipynb
2	test	.\test.ipynb

show_progress

show_progress(0.5, label="test", bar_length=80)

test [########################################----------------------------------------] 50.0%

to8bit

1. Download the test image.

# Download condensation image from the cloud
url = google_download_handle.format("1OTTXhvSrfgPmFcqjTQEaam9e5vgSEP79")  # replace with your image URL
response = requests.get(url, stream=True)
img = imread(BytesIO(response.content))

2. Convert to 8-bit and enhance the contrast.

fig, ax = plt.subplots(1, 2, figsize=(6, 4))
ax[0].imshow(img, cmap="gray")
ax[0].set_title("Raw image")
ax[0].axis("off")
# Convert to 8-bit and enhance contrast
img8 = to8bit(img)
ax[1].imshow(img8, cmap="gray")
ax[1].set_title("8-bit image")
ax[1].axis("off")

(-0.5, 399.5, 499.5, -0.5)

imfindcircles

1. Download the test image.

# Download condensation image from the cloud
url = google_download_handle.format("1LFRt5ozQjJ_WVrWBoPQOFVNl5ZWb8ytP")  # replace with your image URL
response = requests.get(url, stream=True)
img = imread(BytesIO(response.content))

# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
plt.imshow(gray, cmap="gray")
# find circles
circles = imfindcircles(gray, [50, 100], sensitivity=0.7, smooth_window=21)
for _, row in circles.iterrows():
    circ = plt.Circle((row["x"], row["y"]), row["r"], color='r', fill=False)
    plt.gca().add_patch(circ)
plt.axis("off")

(-0.5, 3935.5, 2623.5, -0.5)

xy_bin

1. Generate dense data

# craete a dense data
s = int(1e6)
x = np.arange(s)
y = np.sin(0.00001*x) + np.random.rand(s)*.1
# plot
plt.plot(x, y, label="raw")
xb, yb = xy_bin(x, y, n=50, mode="lin")
plt.scatter(xb, yb, color=bestcolor(1), label="binned", zorder=10)
plt.legend(frameon=False)

<matplotlib.legend.Legend at 0x28d80306f40>

bestcolor

for i in range(10):
    plt.scatter(i, 0, s=100, color=bestcolor(i))

pivLib

PIV

# Download condensation image from the cloud
url = google_download_handle.format("1lc5JlOipgvTGRtvSEavF82xe26iLyHFX")  # replace with your image URL
response = requests.get(url, stream=True)
I0 = imread(BytesIO(response.content))
url = google_download_handle.format("1QXTD6glGy_MAF2sSp9tiv6KeEEeuVz1q")  # replace with your image URL
response = requests.get(url, stream=True)
I1 = imread(BytesIO(response.content))

x, y, u, v = PIV(I0, I1, 32)
plt.imshow(I0, cmap="gray")
plt.quiver(x, y, u, -v, color="yellow")

<matplotlib.quiver.Quiver at 0x28d803926d0>

to_dataframe

from myimagelib.pivLib import to_dataframe
pivData = to_dataframe(x, y, u, v)
pivData.head()

	x	y	u	v
0	16.0	18.0	0.004263	-0.047905
1	32.0	18.0	0.017182	-0.063848
2	48.0	18.0	0.023226	-0.040136
3	64.0	18.0	0.040622	-0.049772
4	80.0	18.0	0.117607	-0.147990

apply_mask

url = google_download_handle.format("1r4Ry9eR-eKQoeTjxmJS9ZZ1aSaiHr-df")  # replace with your image URL
response = requests.get(url, stream=True)
mask = imread(BytesIO(response.content))

pivData = to_dataframe(x, y, u, v)
masked_pivData = apply_mask(pivData, mask)
masked_pivData.head()

	x	y	u	v	mask
0	16.0	18.0	0.004263	-0.047905	False
1	32.0	18.0	0.017182	-0.063848	False
2	48.0	18.0	0.023226	-0.040136	False
3	64.0	18.0	0.040622	-0.049772	False
4	80.0	18.0	0.117607	-0.147990	True

to_matrix

from myimagelib.pivLib import to_matrix
x, y, u, v = to_matrix(masked_pivData)

corrLib

from myimagelib.corrLib import corrS, corrI, divide_windows, distance_corr, \
    density_fluctuation, local_df, compute_energy_density, compute_wavenumber_field, \
        energy_spectrum, autocorr1d, vacf_piv

corrS

x, y, u, v = to_matrix(pivData)
X, Y, CA, CV = corrS(x, y, u, v)

fig, ax = plt.subplots(1, 2, figsize=(6, 4))
ax[0].imshow(CA), ax[0].axis("off")
ax[1].imshow(CV), ax[1].axis("off")

(<matplotlib.image.AxesImage at 0x28d8058c580>, (-0.5, 11.5, 14.5, -0.5))

divide_windows

X, Y, I = divide_windows(I0, windowsize=[32, 32])

plt.imshow(I)
plt.axis("off")

(-0.5, 36.5, 46.5, -0.5)

distance_corr

X, Y, CA, CV = corrS(x, y, u, v)
dc = distance_corr(X, Y, CV)
plt.plot(dc.R, dc.C)

[<matplotlib.lines.Line2D at 0x28d8487abb0>]

energy_spectrum

es = energy_spectrum(pivData, d=25*0.33)

plt.plot(es.k, es.E)

[<matplotlib.lines.Line2D at 0x28d848f50d0>]

autocorr1d

t = np.linspace(0, 10)
x = np.sin(t)
corr, t = autocorr1d(x, t)

plt.plot(t, corr)

[<matplotlib.lines.Line2D at 0x28d848992e0>]