import cv2
import matplotlib.pyplot as plt
import numpy as np
from keras.preprocessing import image


def load_image(image_path, grayscale=False, target_size=None):
    pil_image = image.load_img(image_path, grayscale, target_size)
    return image.img_to_array(pil_image)


def load_detection_model(model_path):
    # detection_model = cv2.CascadeClassifier(model_path)
    detection_model = cv2.dnn.readNetFromCaffe("data/data_opencv/deploy.prototxt.txt", model_path)
    return detection_model


def detect_faces(detection_model, gray_image_array):
    blob = cv2.dnn.blobFromImage(cv2.resize(gray_image_array, (300, 300)), 1.0,
                                 (300, 300), (104.0, 177.0, 123.0))
    detection_model.setInput(blob)
    return detection_model.forward()
    # return detection_model.detectMultiScale(gray_image_array, 1.3, 5)


def draw_bounding_box(face_coordinates, image_array, color):
    x, y, w, h = face_coordinates
    cv2.rectangle(image_array, (x, y), (x + w, y + h), color, 2)


def apply_offsets(face_coordinates, offsets):
    x, y, width, height = face_coordinates
    x_off, y_off = offsets
    return (x - x_off, x + width + x_off, y - y_off, y + height + y_off)


def draw_text(coordinates, image_array, text, color, x_offset=0, y_offset=0,
              font_scale=2, thickness=2):
    x, y = coordinates[:2]
    cv2.putText(image_array, text, (x + x_offset, y + y_offset),
                cv2.FONT_HERSHEY_SIMPLEX,
                font_scale, color, thickness, cv2.LINE_AA)


def get_colors(num_classes):
    colors = plt.cm.hsv(np.linspace(0, 1, num_classes)).tolist()
    colors = np.asarray(colors) * 255
    return colors