# 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()
我得到了这段代码，但我不确定它出了什么问题我得到了IndentationError：unindent与我读到的任何外部缩进级别错误都不匹配，该错误与间距和制表符有关，我不是可以肯定，但是我没有发现任何问题，请帮忙。我不确定我需要补充哪些其他细节，因为这就是我能想到的，您需要帮助，所以，如果您需要更多细节，请告诉我，请尽我所能告诉您。
答案 0 :(得分：0)
在此代码段中，label = ...行仅缩进三个空格，而其所有相邻行都缩进四个空格。
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)
答案 1 :(得分：0)
您的上面的代码具有多个不一致的缩进（请参阅代码中的注释），应该有用于缩进代码的一致的空格/制表符。 另外，您可能需要查看以下内容：How to fix Python indentation
您还可以使用编辑器来帮助您轻松指出缩进错误。我个人在使用python进行编码时会使用PyCharm，因为它对PEP标准有很好的支持。
# 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))) # corrected indent
# 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) # inconsistent indentation here
cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2) # and also here
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: # another here
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(): # another here
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 # and another here
# update the FPS counter
# stop the timer and display FPS information
# do a bit of cleanup
cv2.destroyAllWindows()