# USAGE
# python real_time_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel
# import the necessary packages
import win32api
import keyboard as keyboard
import pygame as pygame
import pythoncom
import win32con
from PIL import ImageGrab
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2
import pyautogui
# construct the argument parse and parse the arguments
from keyboard._mouse_event import RIGHT
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=False,
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=False,
help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.6,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())
prott1 = r'C:\Users\ianyy\Documents\MobileNetSSD_deploy.prototxt.txt'
prott2 = r'C:\Users\ianyy\Documents\MobileNetSSD_deploy.caffemodel'
# 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(prott1, prott2)
# initialize the video stream, allow the cammera sensor to warmup,
# and initialize the FPS counter
print("[INFO] starting video stream...")
#vs = VideoStream(src=0).start()
#time.sleep(2.0)
#fps = FPS().start()
# loop over the frames from the video stream
HSX = 100;
LSX = 1000;
HSY = 100;
LSY = 1000;
HEX = 100;
LEX = 1000;
HEY = 100;
LEY = 1000;
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = np.array(ImageGrab.grab(bbox=(0, 40, 1820, 1240)))
# frame = imutils.resize(frame, width=400)
# grab the frame dimensions and convert it to a blob
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (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])
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# draw the prediction on the frame
label = "{}: {:.2f}%".format(CLASSES[idx],
confidence * 100) cv2.rectangle(frame, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(frame, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
if 'person' in label:
pygame.init()
pygame.event.get()
if pygame.mouse.get_pressed():
print 'pressing'
#tried to detect my character's offset and add the best way to exclude it, failed most tests.
if startX > 369 & startX < 1402 & startY > -1 & startY < 725 & endX > 339 & endX < 1805 & endY > 806 & endY < 1017:
print 'found myself'
else:
#print 'found somebody else'
nosum = int(round(startX * 1)) + int(round(startX * 0.06))
nosum2 = int(round(y * 1)) + int(round(y * 0.7))
halfX = (endX - startX) / 2
halfY = (endY - startY) / 2
finalX = startX + halfX
finalY = startY + halfY
# pyautogui.moveTo(finalX, finalY)
#win32api.SetCursorPos((finalX, finalY))
# win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, finalX, finalY, 0, 0)
# win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, finalX, finalY, 0, 0)
#print 'Pressed L'
if 'HSX' not in locals():
HSX = startX
if 'LSX' not in locals():
LSX = startX
if 'HSY' not in locals():
HSY = startY
if 'LSY' not in locals():
LSY = startY
if 'HEX' not in locals():
HEX = endX
if 'LEX' not in locals():
LEX = endX
if 'HEY' not in locals():
HEY = endY
if 'LEY' not in locals():
LEY = endY
if startX > HSX:
HSX = startX
if startX < LSX:
LSX = startX
if startY > HSY:
HSY = startY
if startY < LSY:
LSY = startY
if endX > HEX:
HEX = endX
if endX < LEX:
LEX = endX
if endY > HEY:
HEY = endY
if endY < LEY:
LEY = endY
print 'LStartX: ' + str(LSX)
print 'HStartY: ' + str(HSY)
print 'LStartY: ' + str(LSY)
print 'HendX: ' + str(HEX)
print 'LendX: ' + str(LEX)
print 'HendY: ' + str(HEY)
print 'LendY: ' + str(LEY)
#print args["confidence"]
# click(10,10)
# show the output frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the `k` key was pressed, break from the loop
if key == ord("k"):
break
# update the FPS counter
# stop the timer and display FPS information
# do a bit of cleanup
cv2.destroyAllWindows()
I got this code and I'm not sure whats wrong with it I'm getting the IndentationError: unindent does not match any outer indentation level error i read that is has to do with something like spacing and tabing I'm not to sure but i don't see anything wrong with it please help. I'm not sure what other detail i need to add because this is all that i can think of that you will need to help so ya, if you need more detail tell me and ill try my best to tell you.
