# import face_recognition
import cv2
from skimage.feature import hog
from skimage import data, exposure
import matplotlib.pyplot as plt
import numpy as np
import dlib
import time

imagefile = "Marjo.jpg"
prototxt = "dnn/face_detector/opencv_face_detector.pbtxt"
prototxt = "dnn/face_detector/deploy.prototxt"
model = "dnn/face_detector/res10_300x300_ssd_iter_140000_fp16.caffemodel"
confidence_threshold = .0

image = cv2.imread(imagefile)
# rows = open(args["labels"]).read().strip().split("\n")
# classes = [r[r.find(" ") + 1:].split(",")[0] for r in rows]

(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,
    (300, 300), (104.0, 177.0, 123.0))

print("[INFO] loding model...")
net = cv2.dnn.readNetFromCaffe(prototxt, model)
print("Loaded")
net.setInput(blob)
start = time.time()
detections = net.forward()
end = time.time()
print("[INFO] classification took {:.5} seconds".format(end-start))
idxs = np.argsort(detections[0])[::-1][:5]

for i in range(0, detections.shape[2]):
    # extract the confidence (i.e., probability) associated with the
    # prediction
    confidence = detections[0, 0, i, 2]

    

    # compute the (x, y)-coordinates of the bounding box for the
    # object
    box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
    (startX, startY, endX, endY) = box.astype("int")

    # we always draw
    
    # First we crop the sub-rect from the image
    sub_img = image[startY:endY, startX:endX]
    rect_img = sub_img.copy()
    width = 2
    cv2.rectangle(rect_img, (0, 0), (sub_img.shape[1]-int(width/2), sub_img.shape[0]-int(width/2)),
            (0, 0, 255), width)
    # white_rect = np.ones(sub_img.shape, dtype=np.uint8) * 255

    # At least 10% opacity
    alpha = max(.1, confidence)

    res = cv2.addWeighted(sub_img, 1-alpha, rect_img, alpha, 1.0)

    # Putting the image back to its position
    image[startY:endY, startX:endX] = res

    # filter out weak detections by ensuring the `confidence` is
    # greater than the minimum confidence
    if confidence > confidence_threshold:
 
        # draw the bounding box of the face along with the associated
        # probability
        text = "{:.2f}%".format(confidence * 100)
        y = startY - 10 if startY - 10 > 10 else startY + 10
        # cv2.rectangle(image, (startX, startY), (endX, endY),
        #     (0, 0, 255), 2)
        cv2.putText(image, text, (startX, y),
            cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)

# show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)