def compare_transforms(letter):
img = img_utils.generate_letter(letter)
img_translated = img_utils.translate(img, axis=0, pixels=20)
img_scaled = img_utils.zoom(img, times=1.2)
img_rotated = img_utils.rotate(img, degrees=45)
img_noise = img_utils.noise(img, low=-30, high=30)
img_flipped = img_utils.flip(img, axis=0)
Hu_moments = OrderedDict()
Hu_moments["original"] = cv2.HuMoments(cv2.moments(img)).flatten()
Hu_moments["translated"] = cv2.HuMoments(
cv2.moments(img_translated)).flatten()
Hu_moments["scaled"] = cv2.HuMoments(cv2.moments(img_scaled)).flatten()
Hu_moments["rotated"] = cv2.HuMoments(cv2.moments(img_rotated)).flatten()
Hu_moments["noise"] = cv2.HuMoments(cv2.moments(img_noise)).flatten()
Hu_moments["flipped"] = cv2.HuMoments(cv2.moments(img_flipped)).flatten()
x_labels = [
"Original", "Translated", "Scaled", "Rotated", "Noise", "Flipped"
]
xs = list(range(6))
for i in range(7):
plt.title(f"Hu moment {i + 1} for {letter}")
plt.xticks(xs, x_labels)
ys = [moments[i] for moments in Hu_moments.values()]
plt.scatter(xs, ys)
plt.savefig(
os.path.join("plots", "moment_" + str(i + 1) + "_" + letter))
plt.clf()
def make_thumb(fid, filedata):
key = 'z' + fid
key = str(key)
if not CACHE.get(key):
filedata = zoom(filedata, 200, 200)
CACHE.set(key, filedata)
return True
return False
def check_invariance(letter):
img = img_utils.generate_letter(letter)
img_translated_1 = img_utils.translate(img, axis=0, pixels=20)
img_translated_2 = img_utils.translate(img, axis=1, pixels=-20)
img_scaled_1 = img_utils.zoom(img, times=1.2)
img_scaled_2 = img_utils.zoom(img, times=1.5)
img_rotated_1 = img_utils.rotate(img, degrees=45)
img_rotated_2 = img_utils.rotate(img, degrees=-90)
hu_moments = OrderedDict()
hu_moments["original"] = cv2.HuMoments(cv2.moments(img)).flatten()
hu_moments["translated_1"] = cv2.HuMoments(
cv2.moments(img_translated_1)).flatten()
hu_moments["translated_2"] = cv2.HuMoments(
cv2.moments(img_translated_2)).flatten()
hu_moments["scaled_1"] = cv2.HuMoments(cv2.moments(img_scaled_1)).flatten()
hu_moments["scaled_2"] = cv2.HuMoments(cv2.moments(img_scaled_2)).flatten()
hu_moments["rotated_1"] = cv2.HuMoments(
cv2.moments(img_rotated_1)).flatten()
hu_moments["rotated_2"] = cv2.HuMoments(
cv2.moments(img_rotated_2)).flatten()
for i in range(7):
ith_moment_vals = np.array(
[moments[i] for moments in hu_moments.values()])
# get only moments for transforms
ith_moment_vals = ith_moment_vals[1:]
stdev = ith_moment_vals.std()
original_val = hu_moments["original"][i]
print(original_val, stdev)
moment_1_vals = []
for _ in range(1000):
degrees = np.random.randint(low=-90, high=90)
new_img = img_utils.rotate(img, degrees)
moment_1_vals.append(cv2.HuMoments(cv2.moments(new_img))[0])
moment_1_vals = np.array(moment_1_vals)
print(letter, moment_1_vals.std())
def thumbnail(fid):
key = 'z' + fid
key = str(key)
filedata = CACHE.get(key)
if not filedata:
filedata = get_file_data(fid)
if not filedata:
abort(404, 'File not found')
filedata = zoom(filedata, 200, 200)
CACHE.set(key, filedata)
response = app.make_response(filedata)
response.headers['Content-Type'] = mimetype(filedata[:1024])
response.headers['Content-Length'] = len(filedata)
return response