def hybrid2(F1,F2,sigH,sigL):
A = io.imread(F1) # for not color?
B = io.imread(F2) # for not color?
l=imfilter(A, gauss(7,7,sigH))
h=imfilter(B, gauss(7,7,sigL))
A = io.imread(F1)
l = A-l
for i in range(h.shape[0]):
for j in range(h.shape[1]):
for k in range(h.shape[2]):
l[i][j][k] = l[i][j][k]/2
h[i][j][k] = h[i][j][k]/2
return (l + h)
def diff_gauss(pixels, width, height):
bigGauss = gauss(pixels, width, height, gb_kernel).load()
smallGauss = gauss(pixels, width, height, gs_kernel).load()
image = PIL.Image.new('RGB', (width, height))
pixels = image.load()
for y in range(height):
for x in range(width):
pixels[x, y] = helpers.subtract(bigGauss[x, y], smallGauss[x, y])
return image
def hybrid1(F1, F2, sigH, sigL):
l = io.imread(F1) # for not color?
h = io.imread(F2) # for not color?
n, m = l.shape[0], l.shape[1]
l = np.array(l)
h = np.array(h)
for i in range(l.shape[2]):
l[:, :, i] = filterFFT(l[:, :, i], gauss(n, m, sigH))
l[:, :, i] = filterFFT(h[:, :, i], gauss(n, m, sigL))
A = io.imread(F1)
l[:, :, i] = A[:, :, i] - l[:, :, i]
for i in range(h.shape[0]):
for j in range(h.shape[1]):
for k in range(h.shape[2]):
h[i][j][k] = h[i][j][k] / 2
l[i][j][k] = l[i][j][k] / 2
return l + h
def harris_detector(imagepath, sigma, k, N):
win = 2 * sigma + 1
winsmooth = 2 * sigma + 1
img = Image.open(imagepath).convert('L')
im = np.array(img)
gx = gaussian.gaussx(win, sigma)
gy = gaussian.gaussy(win, sigma)
# Ix = conv2D.conv2D(im,gx) slow
# Iy = conv2D.conv2D(im,gy) slow
Ix = signal.convolve2d(im, gx)
Iy = signal.convolve2d(im, gy)
Ixx = Ix * Ix
Iyy = Iy * Iy
Ixy = Ix * Iy
gsmooth = gaussian.gauss(winsmooth, sigma)
# Wxx = conv2D.conv2D(Ixx,gsmooth) slow
# Wyy = conv2D.conv2D(Iyy,gsmooth) slow
# Wxy = conv2D.conv2D(Ixy,gsmooth) slow
Wxx = signal.convolve2d(Ixx, gsmooth)
Wyy = signal.convolve2d(Iyy, gsmooth)
Wxy = signal.convolve2d(Ixy, gsmooth)
imfxx = Image.fromarray(Wxx)
imfyy = Image.fromarray(Wyy)
imfxy = Image.fromarray(Wxy)
# imfxx.show()
# imfyy.show()
# imfxy.show()
detA = Wxx * Wyy - Wxy**2
traceA = Wxx + Wyy
H = detA - k * traceA**2
# himg = Image.fromarray(H)
# himg.show()
f = peak_local_max(H, min_distance=8, num_peaks=N, indices=True)
siz = len(f)
# print(len(y))
for k in range(siz):
cv2.circle(im, (itemgetter(1)(f[k]), itemgetter(0)(f[k])),
4, (0, 0, 0),
thickness=1,
lineType=1,
shift=0)
imgf = Image.fromarray(im)
imgf.show()
return f
fptr = open("air_resistance.txt", "r", newline=None)
list_of_results = fptr.readlines()
data = []
for result in list_of_results:
data.append(float(result))
standard_dev = 0.00573 # add in sigma here
mean_val = 0.434038
# add in the mean value here
print_file = "Gaussian_Module_Test.pdf"
gauss(standard_dev, data, mean_val, print_file)
A = plt.hist(data, 5, facecolor="m", alpha=0.75,
edgecolor="k") # plots a histogram with five bins
plt.savefig("gauss_hist.pdf")
print(
A
) # returns the amount of values in each bin, shown in first array in terminal
values = A[0] # amount of values in each bin
bin_vals = []
for i in range(0, len(values)):
bin_vals.append(int(values[i]))