utils.py

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt

from keras.models import load_model
from pandas.io.parsers import read_csv
from sklearn.utils import shuffle

def load_data(test=False):
    """
    Loads data from FTEST if *test* is True, otherwise from FTRAIN.
    Important that the files are in a `data` directory
    """  
    FTRAIN = 'data/training.csv'
    FTEST = 'data/test.csv'
    fname = FTEST if test else FTRAIN
    df = read_csv(os.path.expanduser(fname))  # load dataframes

    # The Image column has pixel values separated by space; convert
    # the values to numpy arrays:
    df['Image'] = df['Image'].apply(lambda im: np.fromstring(im, sep=' '))

    df = df.dropna()  # drop all rows that have missing values in them

    X = np.vstack(df['Image'].values) / 255.  # scale pixel values to [0, 1]
    X = X.astype(np.float32)
    X = X.reshape(-1, 96, 96, 1) # return each images as 96 x 96 x 1

    if not test:  # only FTRAIN has target columns
        y = df[df.columns[:-1]].values
        y = (y - 48) / 48  # scale target coordinates to [-1, 1]
        X, y = shuffle(X, y, random_state=42)  # shuffle train data
        y = y.astype(np.float32)
    else:
        y = None

    return X, y

def plot_data(img, landmarks, axis):
    """
    Plot image (img), along with normalized facial keypoints (landmarks)
    """
    axis.imshow(np.squeeze(img), cmap='gray') # plot the image
    landmarks = landmarks * 48 + 48 # undo the normalization
    # Plot the keypoints
    axis.scatter(landmarks[0::2], 
        landmarks[1::2], 
        marker='o', 
        c='c', 
        s=40)

def plot_keypoints(img_path, 
                  face_cascade=cv2.CascadeClassifier('haarcascade_frontalface_alt.xml'),
                  model_path='my_model.h5'):
    # TODO: write a function that plots keypoints on arbitrary image containing human
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    fig = plt.figure(figsize=(5,5))
    ax = fig.add_subplot(1, 1, 1, xticks=[], yticks=[])
    ax.imshow(cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB))

    if len(faces) == 0:
        plt.title('no faces detected')
    elif len(faces) > 1:
        plt.title('too many faces detected')
        for (x,y,w,h) in faces:
            rectangle = cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2)
            ax.imshow(cv2.cvtColor(rectangle, cv2.COLOR_BGR2RGB))
    elif len(faces) == 1:
        plt.title('one face detected')
        x,y,w,h = faces[0]
        bgr_crop = img[y:y+h, x:x+w] 
        orig_shape_crop = bgr_crop.shape
        gray_crop = cv2.cvtColor(bgr_crop, cv2.COLOR_BGR2GRAY)
        resize_gray_crop = cv2.resize(gray_crop, (96, 96)) / 255.
        model = load_model(model_path)
        landmarks = np.squeeze(model.predict(
            np.expand_dims(np.expand_dims(resize_gray_crop, axis=-1), axis=0)))
        ax.scatter(((landmarks[0::2] * 48 + 48)*orig_shape_crop[0]/96)+x, 
                   ((landmarks[1::2] * 48 + 48)*orig_shape_crop[1]/96)+y, 
                   marker='o', c='c', s=40)
    plt.show()