#! /usr/bin/python3
#
import argparse
import os
import time
import json
import requests
import numpy as np
from pypylon import pylon
import cv2

# Configuration
slackHook = "https://hooks.slack.com/services/String/String"
apiEndPoint = "https://String.execute-api.ap-southeast-2.amazonaws.com/prod/"

def sendToSlack(message):
    try:
        slackText = { 'text': message }
        slackHeaders = { 'Content-Type': 'application/json' }
        slackResp = requests.post(slackHook,
            data = json.dumps(slackText),
            headers = slackHeaders)
    except Exception as ex:
        print("[ERROR] Slack error {0}".format(str(ex)))

def getObject(objectToIdentify):
    try:
        payload = {'objectName': objectToIdentify}
        response = requests.get(apiEndPoint + "ident", params = payload)
        ret = json.loads(response.text)
        return ret
    except Exception as ex:
        print("[ERROR] Request error {0}".format(str(ex)))

# Parse the arguments.  argparse does the hard work for us.
ap = argparse.ArgumentParser()
ap.add_argument("-y", "--yolo", required=True, help="base path to YOLO directory")
ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections")
ap.add_argument("-t", "--threshold", type=float, default=0.3, help="threshold when applyong non-maxima suppression")
args = vars(ap.parse_args())

# Load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")

# Initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
	dtype="uint8")

# Derive the paths to the YOLO weights and model configuration
weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])

# Load our YOLO object detector trained on COCO dataset (80 classes)
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)


camera = pylon.InstantCamera(pylon.TlFactory.GetInstance().CreateFirstDevice())
camera.Open()
print("[INFO] Camera: {0}".format(camera.GetDeviceInfo().GetModelName()))
# The next line can be used to dump the camera's settings
#pylon.FeaturePersistence.Save("camera.cfg", camera.GetNodeMap())
pylon.FeaturePersistence.Load("camera.cfg", camera.GetNodeMap())

camera.StartGrabbing(pylon.GrabStrategy_LatestImageOnly)
converter = pylon.ImageFormatConverter()

converter.OutputPixelFormat = pylon.PixelType_BGR8packed
converter.OutputBitAlignment = pylon.OutputBitAlignment_MsbAligned

while camera.IsGrabbing():
    grabResult = camera.RetrieveResult(5000, pylon.TimeoutHandling_ThrowException)

    if grabResult.GrabSucceeded():
        # Access the image data.
        print("[INFO] Size {0} x {1}".format(grabResult.Width, grabResult.Height))
        image = converter.Convert(grabResult)
        img = image.GetArray()
        (H, W) = img.shape[:2]
        # Determine only the *output* layer names that we need from YOLO
        ln = net.getLayerNames()
        ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
        # Construct a blob from the input image and then perform a forward
        # pass of the YOLO object detector, giving us our bounding boxes and
        # associated probabilities
        blob = cv2.dnn.blobFromImage(img, 1 / 255.0, (416, 416), swapRB=True, crop=False)
        net.setInput(blob)
        start = time.time()
        layerOutputs = net.forward(ln)
        end = time.time()
        # Show timing information on YOLO
        print("[INFO] YOLO took {:.6f} seconds".format(end - start))

        # Initialize our lists of detected bounding boxes, confidences, and
        # class IDs, respectively
        boxes = []
        confidences = []
        classIDs = []

        for output in layerOutputs:
            # Loop over each of the detections
            for detection in output:
                # Extract the class ID and confidence (i.e., probability) of
                # the current object detection
                scores = detection[5:]
                classID = np.argmax(scores)
                confidence = scores[classID]

                # Filter out weak predictions by ensuring the detected
                # probability is greater than the minimum probability
                if confidence > args["confidence"]:
                    # Scale the bounding box coordinates back relative to the
                    # size of the image, keeping in mind that YOLO actually
                    # returns the center (x, y)-coordinates of the bounding
                    # box followed by the boxes' width and height
                    box = detection[0:4] * np.array([W, H, W, H])
                    (centerX, centerY, width, height) = box.astype("int")

                    # Use the center (x, y)-coordinates to derive the top and
                    # and left corner of the bounding box
                    x = int(centerX - (width / 2))
                    y = int(centerY - (height / 2))

                    # Update our list of bounding box coordinates, confidences,
                    # and class IDs
                    boxes.append([x, y, int(width), int(height)])
                    confidences.append(float(confidence))
                    classIDs.append(classID)

        # Apply non-maxima suppression to suppress weak, overlapping bounding
        # boxes
        idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"])

        # Ensure at least one detection exists
        if len(idxs) > 0:
            # Loop over the indexes we are keeping
            for i in idxs.flatten():
                # Extract the bounding box coordinates
                (x, y) = (boxes[i][0], boxes[i][1])
                (w, h) = (boxes[i][2], boxes[i][3])

                # Draw a bounding box rectangle and label on the image
                color = [int(c) for c in COLORS[classIDs[i]]]
                cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
                text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
                cv2.putText(img, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
                print("[INFO] Object: {0} Confidence: {1:.4f}%".format(LABELS[classIDs[i]], confidences[i] * 100))
                try:
                    print("Lookup: {0}".format(getObject(LABELS[classIDs[i]])))
                except:
                    print("[ERROR] failed in getObject()")



        cv2.namedWindow('title', cv2.WINDOW_NORMAL)
        cv2.imshow('title', img)
        k = cv2.waitKey(1000)
        if k == 27:
            break
    
    grabResult.Release()
