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import joblib
import json
import numpy as np
import base64
import cv2
from wavelet import w2d
__class_name_to_number = {}
__class_number_to_name = {}
__model = None
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 class_number_to_name(class_num):
return __class_number_to_name[class_num]
def load_saved_artifacts():
print("loading saved artifacts...start")
global __class_name_to_number
global __class_number_to_name
with open("./artifacts/class_dictionary.json", "r") as f:
__class_name_to_number = json.load(f)
__class_number_to_name = {v:k for k,v in __class_name_to_number.items()}
global __model
if __model is None:
with open('./artifacts/saved_model.pkl', 'rb') as f:
__model = joblib.load(f)
print("loading saved artifacts...done")
def get_cv2_image_from_base64_string(b64str):
'''
credit: https://stackoverflow.com/questions/33754935/read-a-base-64-encoded-image-from-memory-using-opencv-python-library
:param uri:
:return:
'''
encoded_data = b64str.split(',')[1]
nparr = np.frombuffer(base64.b64decode(encoded_data), np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
return img
def get_cropped_image_if_2_eyes(image_path, image_base64_data):
face_cascade = cv2.CascadeClassifier('./opencv/haarcascades/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('./opencv/haarcascades/haarcascade_eye.xml')
if image_path:
img = cv2.imread(image_path)
else:
img = get_cv2_image_from_base64_string(image_base64_data)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
cropped_faces = []
for (x,y,w,h) in faces:
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
eyes = eye_cascade.detectMultiScale(roi_gray)
if len(eyes) >= 2:
cropped_faces.append(roi_color)
return cropped_faces
def get_b64_test_image_for_virat():
with open("b64.txt") as f:
return f.read()
if __name__ == '__main__':
load_saved_artifacts()
print(classify_image(get_b64_test_image_for_virat(), None))
# print(classify_image(None, "./test_images/federer1.jpg"))
# print(classify_image(None, "./test_images/federer2.jpg"))
# print(classify_image(None, "./test_images/virat1.jpg"))
# print(classify_image(None, "./test_images/virat2.jpg"))
# print(classify_image(None, "./test_images/virat3.jpg")) # Inconsistent result could be due to https://github.com/scikit-learn/scikit-learn/issues/13211
# print(classify_image(None, "./test_images/serena1.jpg"))
# print(classify_image(None, "./test_images/serena2.jpg"))
# print(classify_image(None, "./test_images/sharapova1.jpg"))
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