Update app.py

app.py

@@ -5,4 +5,90 @@ import json
 import numpy as np
 import cv2
 
-st.title("Welcome!")
+st.title("Welcome!")
+
+__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_new(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 get_cropped_image_if_2_eyes_new(image_path, image_base64_data):
+    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+    eye_cascade = cv2.CascadeClassifier('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_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 load_saved_artifacts():
+    print("loading saved artifacts...start")
+    global __class_name_to_number
+    global __class_number_to_name
+
+    with open("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:
+      __model = pickle.load(open('saved_model.pkl','rb'))
+    st.text("loading saved artifacts...done")
+
+def class_number_to_name(class_num):
+    return __class_number_to_name[class_num]
+
+def get_b64_test_image_for_virat():
+    with open("b64.txt") as f:
+        return f.read()
+
+uploaded_image = st.file_uploader('Choose an image')
+load_saved_artifacts()
+
+st.text(classify_image(get_b64_test_image_for_virat(), "sharapova1.jpg"))
+