img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img_gray = np.asarray(img_gray)
hfilt = Gauss2D((5, 5), sigma)
# Q3.1 Convolution
# you can change the parameters for Gauss2D
Im1 = myImageFilter(img_gray, hfilt)
# plt.imshow(Im1,cmap="gray");plt.show()
Im1X = myImageFilterX(img_gray, hfilt)
# cv2.normalize(Im1X,Im1X, 0, 1, cv2.NORM_MINMAX)
plt.imshow(Im1X, cmap="gray")
plt.show()
# Q3.3
ImEdge, Io, Ix, Iy = myEdgeFilter(img_gray, sigma)
# plt.imshow(ImEdge,cmap="gray");plt.show()
Thresh = 50 #0.2 # change it as needed
ThrImEdge = 1.0 * (ImEdge > Thresh)
# plt.imshow(ThrImEdge,cmap="gray");plt.show()
'''
print(ImEdge[:50, :20])
print(img_rgb.dtype)
print(ThrImEdge.dtype)
exit()
'''
# Q3.4
H, rhoScale, thetaScale = myHoughTransform(ThrImEdge, rhoRes, thetaRes)
# plt.imshow(H);plt.show()
for file in os.listdir(datadir):
if file.endswith(".jpg"):
imglist.append(file)
imglist.sort()
for i in range(len(imglist)):
# read in images
fname = os.path.join(datadir, imglist[i])
img = sp.ndimage.imread(fname)
if img.ndim == 3:
img = rgb2gray(img)
img = np.float64(img) / 255.0
# actual Hough line code function calls
Im = myEdgeFilter(img, sigma)
# H, rhoScale, thetaScale = myHoughTransform(Im, threshold, rhoRes, thetaRes)
# rhos, thetas = myHoughLines(H, nLines)
# thresh_img = Im > threshold
# lines = houghlines(thresh_img, 180 * (thetaScale / math.pi), rhoScale,
# rhos, thetas, fillgap=5, minlength=20)
# everything below here just saves the outputs to files
filename_w_ext = os.path.basename(imglist[i])
filename, file_extension = os.path.splitext(filename_w_ext)
fname = os.path.join(resultsdir, filename + '_01edge.png')
temp = np.uint8(255.0 * np.sqrt(Im / np.max(Im)))
sp.misc.imsave(fname, temp)
fname = os.path.join(resultsdir, filename + '_02threshold.png')
temp = np.uint8(255.0 * (Im > threshold))
sp.misc.imsave(fname, temp)