python - 使用 Python 在浏览器上进行图像识别
问题描述
我想实现一个软件来计算二十一点的牌,使用一些图像识别来自动化这个过程。但我不知道从哪里开始。我认为问题可以分为以下几个步骤:
1- 在游戏中从浏览器中获取图像(它基本上是一个 Adobe Flash 游戏)
2-处理图像,通过一些图像识别来识别所有卡片。
3- 使用 Hi-Lo 策略更新计数器
4-在屏幕上显示结果
我怎么能用python做到这一点?有哪些图书馆可以帮助我?这对我来说是一个全新的领域。我将尝试根据您的建议实施该问题。
编辑1:
Selenium Webdriver工作正常,到目前为止我已经使用这种和平的代码来获取主页的屏幕截图,但我无法进入游戏,因为我没有钱玩大声笑:
from selenium import webdriver
browser = webdriver.Chrome()
browser.get('https://www.888casino.it/giochi-da-casino/')
browser.save_screenshot('screenie.png')
browser.quit()
但基本上我需要browser.get()
用钩住浏览器的东西代替,而不是打开新页面的东西。然后我需要实现一个 for 循环,在我玩游戏时每秒截取一次屏幕截图,然后我可以开始处理这些图像。
编辑2:
我将尝试使用 TensorFlow API进行图像处理,但我没有找到任何识别卡片的训练模型。所以我必须创建一个全新的模型,我发现这个教程可以帮助我训练自己的对象识别模型。如果您知道现有的训练模型,请链接它。
编辑 3:
使用 Tensorflow 我能够创建自己的对象识别模型,现在我需要在 python 脚本中使用该模型。现在我已经使用了这个示例脚本,它打开一个图像并在卡片周围绘制矩形。
import os
import cv2
import numpy as np
import tensorflow as tf
import sys
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util
# Name of the directory containing the object detection module we're using
MODEL_NAME = 'inference_graph'
IMAGE_NAME = 'test1.jpg'
# Grab path to current working directory
CWD_PATH = os.getcwd()
# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')
# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')
# Path to image
PATH_TO_IMAGE = os.path.join(CWD_PATH,IMAGE_NAME)
# Number of classes the object detector can identify
NUM_CLASSES = 13
# Load the label map.
# Label maps map indices to category names, so that when our convolution
# network predicts `5`, we know that this corresponds to `king`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
# Define input and output tensors (i.e. data) for the object detection classifier
# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Load image using OpenCV and
# expand image dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
image = cv2.imread(PATH_TO_IMAGE)
image_expanded = np.expand_dims(image, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_expanded})
# Draw the results of the detection (aka 'visulaize the results')
vis_util.visualize_boxes_and_labels_on_image_array(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh=0.80)
# All the results have been drawn on image. Now display the image.
cv2.imshow('Object detector', image)
# Press any key to close the image
cv2.waitKey(0)
# Clean up
cv2.destroyAllWindows()
现在我需要创建自己的识别卡片的脚本,并且为每张卡片更新一个必须在屏幕上显示的计数器。这是最棘手的部分,因为我不知道从哪里开始。这一步我有几个问题,首先脚本必须能够区分离开牌组的牌和新牌,这样每次截图时它都不会弄乱计数器。其次,计数器应该更新为 -1 表示高牌(10 - ace),+1 表示低牌(2-6),0 表示中性牌(7-8-9),并且必须在屏幕上可见。
编辑 4:我已经构建了软件的第一个版本,但是有一些问题,计数器没有正确更新。这是代码:
import pyscreenshot as ImageGrab
from win32api import GetSystemMetrics
import os
import cv2
import numpy as np
import tensorflow as tf
import sys
import warnings
import h5py
def UpdateCounter(labels, c):
for i in labels:
if labels['ace'] > 0:
c = c - 1
if labels['king'] > 0:
c = c - 1
if labels['queen'] > 0:
c = c - 1
if labels['jack'] > 0:
c = c - 1
if labels['ten'] > 0:
c = c - 1
if labels['six'] > 0:
c = c + 1
if labels['five'] > 0:
c = c + 1
if labels['four'] > 0:
c = c + 1
if labels['three'] > 0:
c = c + 1
if labels['two'] > 0:
c = c + 1
return c
if __name__ == '__main__':
sys.path.append("..")
from utils import label_map_util
from utils import visualization_utils as vis_util
MODEL_NAME = 'inference_graph'
IMAGE_NAME = 'test1.jpg'
CWD_PATH = os.getcwd()
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')
PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')
PATH_TO_IMAGE = os.path.join(CWD_PATH,IMAGE_NAME)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
NUM_CLASSES = 13
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
c = 0
while True:
labels = {"ace" : 0, "king": 0, "queen": 0, "jack": 0, "ten": 0, "nine": 0, "eight": 0,"seven": 0, "six": 0, "five": 0, "four":0, "three": 0, "two": 0}
with warnings.catch_warnings():
warnings.filterwarnings("ignore",category=FutureWarning)
screenshot=ImageGrab.grab(bbox=(42,42, GetSystemMetrics(0),GetSystemMetrics(1)))
screenshot.save(IMAGE_NAME)
image = cv2.imread(PATH_TO_IMAGE)
image_expanded = np.expand_dims(image, axis=0)
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_expanded})
data = [category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.9]
for ch in data:
if ch['name'] == "ace":
labels["ace"] += 1
elif ch['name'] == "king":
labels["king"] += 1
elif ch['name'] == "queen":
labels["queen"] += 1
elif ch['name'] == "jack":
labels["jack"] += 1
elif ch['name'] == "ten":
labels["ten"] += 1
elif ch['name'] == "nine":
labels["nine"] += 1
elif ch['name'] == "eight":
labels["eight"] += 1
elif ch['name'] == "seven":
labels["seven"] += 1
elif ch['name'] == "six":
labels["six"] += 1
elif ch['name'] == "five":
labels["five"] += 1
elif ch['name'] == "four":
labels["four"] += 1
elif ch['name'] == "three":
labels["three"] += 1
elif ch['name'] == "two":
labels["two"] += 1
print(UpdateCounter(labels, c))
请问我该如何解决这个问题?仅当识别出新卡时,我才需要显示计数器,并且我还需要修复程序遇到的不良匹配。
解决方案
我相信你可以通过使用你提到的硒来实现这一目标。
这将类似于以下内容:
from selenium import webdriver
import time
browser = webdriver.Chrome()
browser.get('https://www.888casino.it/giochi-da-casino/')
while True:
browser.save_screenshot('screenie.png')
#do the image processing...
time.sleep(1)
browser.quit()
对于图像处理本身,您将面临识别图像上所需元素的问题,在您的情况下,卡片将进一步单独处理它们中的每一个。所以你在这方面有一个两步的任务。
有一个 tensorflow 对象检测 API 可能会为您提供:https ://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API
祝你好运!
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