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server.py

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+from flask import Flask, request, jsonify
+import util
+
+app = Flask(__name__)
+
+
+@app.route('/classify_image', methods=['GET', 'POST'])
+def classify_image():
+    image_data = request.form['image_data']
+
+    response = jsonify(util.classify_image(image_data))
+
+    response.headers.add('Access-Control-Allow-Origin', '*')
+
+    return response
+
+if __name__ == "__main__":
+    print("Starting Python Flask Server For Sports Celebrity Image Classification")
+    util.load_saved_artifacts()
+    app.run(port=5000)

util.py

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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"))

wavelet.py

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+import numpy as np
+import pywt
+import cv2
+
+def w2d(img, mode='haar', level=1):
+    imArray = img
+    #Datatype conversions
+    #convert to grayscale
+    imArray = cv2.cvtColor( imArray,cv2.COLOR_RGB2GRAY )
+    #convert to float
+    imArray =  np.float32(imArray)
+    imArray /= 255;
+    # compute coefficients
+    coeffs=pywt.wavedec2(imArray, mode, level=level)
+
+    #Process Coefficients
+    coeffs_H=list(coeffs)
+    coeffs_H[0] *= 0;
+
+    # reconstruction
+    imArray_H=pywt.waverec2(coeffs_H, mode);
+    imArray_H *= 255;
+    imArray_H =  np.uint8(imArray_H)
+
+    return imArray_H