def classify_image(image_base64_data, file_path=None):
imgs = get_cropped_image_if_2_eyes(file_path, image_base64_data)
result = []
for img in imgs:
scalled_raw_img = cv2.resize(img, (32, 32))
img_har = w2d(img, 'db1', 5)
scalled_img_har = cv2.resize(img_har, (32, 32))
combined_img = np.vstack(
(scalled_raw_img.reshape(32 * 32 * 3,
1), scalled_img_har.reshape(32 * 32, 1)))
len_image_array = 32 * 32 * 3 + 32 * 32
final = combined_img.reshape(1, len_image_array).astype(float)
result.append({
'class':
class_number_to_name(__model.predict(final)[0]),
'class_probability':
np.around(__model.predict_proba(final) * 100, 2).tolist()[0],
'class_dictionary':
__class_name_to_number
})
return result
def classify_image(image_base64_data, file_path=None):
imgs = cropped_face_2_eyes(file_path, image_base64_data)
result = []
for img in imgs:
scalled_raw_img = cv2.resize(img, (32, 32))
img_har = w2d(img, 'db1', 5)
scalled_img_har = cv2.resize(img_har, (32, 32))
combined_img = np.vstack(
(scalled_raw_img.reshape(32 * 32 * 3,
1), scalled_img_har.reshape(32 * 32, 1)))
len_img_array = 32 * 32 * 3 + 32 * 32
final = combined_img.reshape(1, len_img_array).astype(float)
probablity = np.round(__model.predict_proba(final) * 100,
2).tolist()[0]
c_name = [value for key, value in __class_number_to_name.items()]
probablity_name = []
for name, probab in zip(c_name, probablity):
probablity_name.append(str(name) + ':' + (str(probab)))
result.append({
'class':
class_number_to_name(__model.predict(final)[0]),
'class_probablity':
np.around(__model.predict_proba(final) * 100, 2).tolist()[0],
'class_dictionary':
__class_name_to_number,
})
return result
def v_stack(bs4):
imgs = get_crop(bs4)
result = []
for img in imgs:
scalled_raw_img = cv2.resize(img, (32, 32))
img_har = w2d(img, 'db1', 5)
scalled_img_har = cv2.resize(img_har, (32, 32))
combined_img = np.vstack(
(scalled_raw_img.reshape(32 * 32 * 3,
1), scalled_img_har.reshape(32 * 32, 1)))
len_image_array = 32 * 32 * 3 + 32 * 32
final = combined_img.reshape(1, len_image_array).astype(float)
result.append(__model.predict(final)[0])
return result
