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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")) | |