#todo get all the parameters including image url from cmd line

# import the necessary packages

import numpy as np
import argparse
import cv2
import urllib.request as urlreq
import requests
import json

url = 'http://192.168.1.100:8080/snapshot?topic=/camera/color/image_raw'
server_url ='http://localhost:53983/api/DetectPeoples'
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
#ap.add_argument("-i", "--image", required=True,
#                help="path to input image")
ap.add_argument("-p", "--prototxt", required=True,
                help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
                help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
                help="minimum probability to filter weak detections")
args = vars(ap.parse_args())


# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
           "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]

COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# load the input image and construct an input blob for the image
# by resizing to a fixed 300x300 pixels and then normalizing it
# (note: normalization is done via the authors of the MobileNet SSD
# implementation)
while True:
    #save and count all detections
    person_count = 0
    boxes = ""
    #TODO check for responce success
    #get a single image at a time
    resp = urlreq.urlopen(url)
    image = np.asarray(bytearray(resp.read()), dtype='uint8')
    image = cv2.imdecode(image, cv2.IMREAD_COLOR)
    
    original_shape = image.shape
    (h, w) = image.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)

    # pass the blob through the network and obtain the detections and
    # predictions

    net.setInput(blob)
    detections = net.forward()

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

        # filter out weak detections by ensuring the `confidence` is
        # greater than the minimum confidence
        if confidence > args["confidence"]:
            # extract the index of the class label from the `detections`,
            # then compute the (x, y)-coordinates of the bounding box for
            # the object
            idx = int(detections[0, 0, i, 1])
            if CLASSES[idx] is not 'person':
                continue
            
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")
            box_str = '{}:{}:{}:{}'.format(startX, startY, endX, endY)
            # display the prediction
            # label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
            # print("[INFO] {}".format(label))
            accuracy = '{:.2f}'.format(confidence * 100)
            

            #draw rectangle for debugging

            #cv2.rectangle(image, (startX, startY), (endX, endY),
            #              COLORS[idx], 2)
            #y = startY - 15 if startY - 15 > 15 else startY + 15
            #cv2.putText(image, label, (startX, y),
            #            cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)

            person_count += 1
            boxes += accuracy +':'+ box_str + '%%%'
    #send a post request of detections and empty list
   
    r = requests.post(server_url, data = {"NumberOfPeople":person_count,"ValuesString":boxes})


    #show the output image for debugging

    #cv2.resize(image, original_shape[:2])
    #cv2.imshow("Output", image)
    #if cv2.waitKey(1) & 0xFF == ord('q'):
    #    break
cv2.destroyAllWindows()


