python-help/md/ai.md

人工智能

自定义模型识别

from AI import CustomModel

模型初始化

CustomModel(labels,path)

参数说明

参数	类型	说明
labels	list	标签列表，必填
path	str	模型路径，必填

加载识别

loadModel(img)

参数说明

参数	类型	说明
img	image.Image()	要识别的Image对象，必填

样例

from cocopiPython import cameraPython,Screen
from AI import CustomModel

Screen = Screen() # 画布初始化
camera = cameraPython() # 摄像头初始化

labels = ["loadModel", "unload", "left", "right", "forward", "stop"] # 标签
path = "/root/preset/model/autologistics" # 模型路径
CustomModel = CustomModel(labels,path) # 初始化模型

while True:
    canvas = camera.capture() # 获取摄像头图像
    canvas = canvas.crop(48,8,224,224) # 裁剪图像为224*224
    data = CustomModel.loadModel(canvas) # 加载模型进行识别
    for i in data:
        canvas.draw_rectangle(i["X"],i["Y"],i["X"]+i["W"],i["Y"]+i["H"],color=(255,0,0),thickness=1) # 在屏幕绘制检测框
        canvas.draw_string(i["X"],i["Y"]-20,(str(i["name"])+":"+str(i["score"])),color=(255,0,0)) # 在屏幕上绘制名称与置信度
    Screen.show(canvas,48,8) # 显示画布

其中i["X"]表示识别结果的检测框起点X轴坐标，i["Y"]表示识别结果的检测框起点Y轴坐标，i["W"]表示识别结果的检测框宽度，i["H"]表示识别结果的检测框高度，i["name"]表示识别结果的名称，i["score"]表示识别结果的置信度，i["middleX"]表示识别结果的检测框中心点的X轴坐标，i["middleY"]表示识别结果的检测框中心点的Y轴坐标。

边缘检测

from AI import EdgeDetection

模型初始化

EdgeDetection()

加载识别

loadModel(img)

参数说明

参数	类型	说明
img	image.Image()	要识别的Image对象，必填

样例

from cocopiPython import cameraPython,Screen
from AI import EdgeDetection

Screen = Screen() # 画布初始化
camera = cameraPython() # 摄像头初始化
EdgeDetection = EdgeDetection() # 初始化

while True:
    img = camera.capture() # 获取摄像头数据
    img = img.crop(48,8,224,224) # 裁剪尺寸为224*224
    canvas = EdgeDetection.loadModel(img) # 加载模型
    Screen.show(canvas,48,8) # 显示画布

人脸检测

from AI import Facedetection

模型初始化

Facedetection()

加载识别

loadModel(img)

参数说明

参数	类型	说明
img	image.Image()	要识别的Image对象，必填

样例

from cocopiPython import cameraPython,Screen
from AI import Facedetection

Screen = Screen() # 画布初始化
camera = cameraPython() # 摄像头初始化
face = Facedetection() # 人脸检测初始化

while True:
    canvas = camera.capture() # 获取摄像头图像
    canvas = canvas.crop(48,8,224,224) # 裁剪图像尺寸
    data = face.loadModel(canvas) # 加载人脸检测模型
    for i in data:
        canvas.draw_rectangle(i["X"],i["Y"],i["X"]+i["W"],i["Y"]+i["H"],color=(255,0,0), thickness=1) # 在屏幕上绘制人脸检测框
        canvas.draw_string(5,5,str(len(data)), scale = 1, color = (255,255,255) , thickness = 1) # 在屏幕上显示人脸数量
    Screen.show(canvas,48,8) # 显示画布

人脸识别

from AI import FaceRecognition

模型初始化

FaceRecognition()

加载模型

加载人脸检测模型，获取人脸信息

loadmodel(img)

参数说明

参数	类型	说明
img	image.Image()	要识别的Image对象，必填

添加人脸

录入人脸信息保存到本地

add(name,feature,dataPath)

参数说明

参数	类型	说明
name	str	识别到的人脸名称，必填
feature	list	人脸数据，必填
dataPath	str	保存路径，选填，默认为"/root/user/model/recorded_face_features.py"

删除人脸

删除本地保存的最后一条人脸数据

remove(name,dataPath)

参数说明

参数	类型	说明
name	str	要删除的人脸名称，必填
dataPath	str	保存路径，选填，默认为"/root/user/model/recorded_face_features.py"

加载人脸信息

加载人脸采集时保存到本地的人脸信息数据

loadface(DataPath)

参数说明

参数	类型	说明
dataPath	str	保存路径，选填，默认为"/root/user/model/recorded_face_features.py"

样例

人脸采集

import time
from cocopiPython import cameraPython,Screen
from AI import FaceRecognition
from CocoPi import BUTTON

# 按钮初始化
key_C = BUTTON(13)
key_D = BUTTON(7)

Screen = Screen()
camera = cameraPython()

FaceRecognition = FaceRecognition()
People_list = ["小明","小红","小刚","小花"]

FaceNum = 0
faceScore = 0
color = (255,0,0)

while True:
    canvas = camera.capture() # 获取摄像头数据
    canvas = canvas.crop(48,8,224,224) # 裁剪图片尺寸
    data = FaceRecognition.loadmodel(canvas) # 加载人脸检测模型
    for i in data:
        canvas.draw_rectangle(i["X"],i["Y"],i["X"]+i["W"],i["Y"]+i["H"],color=color,thickness=1) # 检测框
        if key_C.is_pressed() and FaceNum < len(People_list):
            while not key_C.is_pressed()== False:
                time.sleep(0.01)
            FaceRecognition.add(People_list[len(FaceRecognition)],i["feature"]) # 录入人脸保存
            canvas.draw_rectangle(0,0,320,20,color=(0,0,0),thickness=-1)
            canvas.draw_string(0,0,"已采集"+str(People_list[FaceNum])+"人脸数据",scale=1,color=(255,255,255),thickness=1)
            Screen.show(canvas,48,8)
            FaceNum = FaceNum + 1
            time.sleep(1)
    if key_D.is_pressed() and FaceNum > 0:
        while not key_D.is_pressed()== False:
            time.sleep(0.01)
        FaceRecognition.remove(People_list[len(FaceRecognition)-1]) # 删除人脸
        canvas.draw_rectangle(0,0,320,20,color=(0,0,0),thickness=-1)
        canvas.draw_string(0,0,"已删除"+str(People_list[FaceNum-1])+"人脸数据",scale=1,color=(255,255,255),thickness=1)
        Screen.show(canvas,48,8)
        FaceNum = FaceNum - 1
        time.sleep(1)
    Screen.show(canvas,48,8)

人脸识别

from cocopiPython import cameraPython,Screen
from AI import FaceRecognition

Screen = Screen()
camera = cameraPython()
FaceRecognition = FaceRecognition()

faceScore = 0
color = (255,0,0)
    
while True:
    canvas = camera.capture() # 获取摄像头数据
    canvas = canvas.crop(48,8,224,224) # 裁剪图片尺寸
    data = FaceRecognition.loadmodel(canvas) # 加载人脸检测模型
    FaceRecognition.loadface()
    for i in data:
        faceScore = round(i["recognizeScore"],2)
        canvas.draw_rectangle(i["X"],i["Y"],i["X"]+i["W"],i["Y"]+i["H"],color=color,thickness=1) # 检测框
        canvas.draw_string(i["X"],i["Y"],str(i["name"])+":"+str(faceScore),scale=1,color=color,thickness=1)
        if faceScore > 80:
            color = (0,255,0)
        else:
            color = (255,0,0)
    Screen.show(canvas,48,8)

人脸识别+人脸采集

import time
from cocopiPython import cameraPython,Screen
from AI import FaceRecognition
from CocoPi import BUTTON

# 按钮初始化
key_C = BUTTON(13)
key_D = BUTTON(7)

Screen = Screen()
camera = cameraPython()

FaceRecognition = FaceRecognition()
People_list = ["小明","小红","小刚","小花"]

FaceNum = 0
faceScore = 0
color = (255,0,0)

while True:
    canvas = camera.capture() # 获取摄像头图像
    canvas = canvas.crop(48,8,224,224) # 裁剪图片尺寸
    data = FaceRecognition.loadmodel(canvas) # 加载人脸检测模型
    for i in data:
        canvas.draw_rectangle(i["X"],i["Y"],i["X"]+i["W"],i["Y"]+i["H"],color=color,thickness=1) # 检测框
        if key_C.is_pressed() and FaceNum < len(People_list):
            while not key_C.is_pressed()== False:
                time.sleep(0.01)
            FaceRecognition.add(People_list[len(FaceRecognition)],i["feature"]) # 录入人脸保存
            canvas.draw_rectangle(0,0,320,20,color=(0,0,0),thickness=-1)
            canvas.draw_string(0,0,"已采集"+str(People_list[FaceNum])+"人脸数据",scale=1,color=(255,255,255),thickness=1)
            Screen.show(canvas,48,8)
            FaceNum = FaceNum + 1
            time.sleep(1)
        faceScore = round(i["recognizeScore"],2)
        if faceScore > 70:
            color = (0,255,0)
            canvas.draw_string(i["X"],i["Y"],str(i["name"])+":"+str(faceScore),scale=1,color=color,thickness=1)
        else:
            color = (255,0,0)
    if key_D.is_pressed() and FaceNum > 0:
        while not key_D.is_pressed()== False:
            time.sleep(0.01)
        FaceRecognition.remove(People_list[len(FaceRecognition)-1]) # 删除人脸
        canvas.draw_rectangle(0,0,320,20,color=(0,0,0),thickness=-1)
        canvas.draw_string(0,0,"已删除"+str(People_list[FaceNum-1])+"人脸数据",scale=1,color=(255,255,255),thickness=1)
        Screen.show(canvas,48,8)
        FaceNum = FaceNum - 1
        time.sleep(1)
    Screen.show(canvas,48,8)

数字识别

from AI import Digitalecognition

模型初始化

Digitalecognition()

加载识别

loadModel(img)

参数说明

参数	类型	说明
img	image.Image()	要识别的Image对象，必填

样例

from cocopiPython import cameraPython,Screen
from AI import Digitalecognition

Screen = Screen() # 画布初始化
camera = cameraPython() # 摄像头初始化
Digit = Digitalecognition() # 人脸检测初始化

while True:
    canvas = camera.capture() # 获取摄像头图像
    canvas = canvas.crop(48,8,224,224) # 裁剪图像尺寸
    data = Digit.loadModel(canvas)
    for i in data:
        canvas.draw_rectangle(i["X"],i["Y"],i["X"]+i["W"],i["Y"]+i["H"],color=(255,0,0),thickness=1)
        canvas.draw_string(i["X"],i["Y"]-20,(str(i["name"])+":"+str(i["score"])),color=(255,0,0))
    Screen.show(canvas,48,8)

物体检测

from AI import ObjectDetection

模型初始化

ObjectDetection()

加载识别

loadModel(img)

参数说明

参数	类型	说明
img	image.Image()	要识别的Image对象，必填

样例

from cocopiPython import cameraPython,Screen
from AI import ObjectDetection

Screen = Screen() # 画布初始化
camera = cameraPython() # 摄像头初始化
object = ObjectDetection() # 物体检测初始化

while True:
    canvas = camera.capture() # 获取摄像头图像
    canvas = canvas.crop(48,8,224,224) # 裁剪图像尺寸
    data = object.loadModel(canvas)
    for i in data:
        canvas.draw_rectangle(i["X"],i["Y"],i["X"]+i["W"],i["Y"]+i["H"],color=(255,0,0),thickness=1)
        canvas.draw_string(i["X"],i["Y"]-20,(str(i["name"])+":"+str(i["score"])),color=(255,0,0))
    Screen.show(canvas,48,8)

猜拳识别

from AI import RockPaperScissors

模型初始化

RockPaperScissors()

加载识别

loadModel(img)

参数说明

参数	类型	说明
img	image.Image()	要识别的Image对象，必填

样例

from cocopiPython import cameraPython,Screen
from AI import RockPaperScissors

Screen = Screen()
camera = cameraPython()
RockPaperScissors = RockPaperScissors()

while True:
    canvas = camera.capture()
    canvas = canvas.crop(48,8,224,224)
    data = RockPaperScissors.loadModel(canvas)
    for i in data:
        canvas.draw_rectangle(i["X"],i["Y"],i["X"]+i["W"],i["Y"]+i["H"],color=(255,0,0),thickness=1)
        canvas.draw_string(i["X"],i["Y"]-10,(str(i["name"])+":"+str(i["score"])),color=(255,0,0))
    Screen.show(canvas,48,8)

自学习识别

from AI import SelfLearning

模型初始化

SelfLearning(lables)

参数说明

参数	类型	说明
lables	列表	要识别的物体名称列表，必填

添加类别

addClass(img)

参数说明

参数	类型	说明
img	image.Image()	要识别的Image对象，必填

训练

trainSave(path)

参数说明

参数	类型	说明
path	string	保存路径，选填，默认为“/root/user/model/module.bin”

加载识别

loadModel(img,path)

参数说明

参数	类型	说明
img	image.Image()	要识别的Image对象，必填
path	string	保存路径，选填，默认为“/root/user/model/module.bin”

样例

采集

import time
from cocopiPython import cameraPython,Screen
from AI import SelfLearning
from CocoPi import BUTTON

key_C = BUTTON(13)
key_D = BUTTON(7)

Screen = Screen()
camera = cameraPython()
lables=(['鼠标','键盘','水杯'])
SL = SelfLearning(lables)

is_train = 0
count = 0
str_display = "按C键添加类别，按D键進行学习"

while True:
    # 获取摄像头数据
    canvas = camera.capture()
    canvas = canvas.crop(48,8,224,224)
    if key_C.is_pressed():
        while not key_C.is_pressed()== False:
            time.sleep(0.01)
        if count < len(lables):
            # 采集数据添加到列表中
            time.sleep(1)
            SL.addClass(canvas)
            count += 1
    if key_D.is_pressed():
        while not key_D.is_pressed()== False:
            time.sleep(0.01)
        if count >= len(lables) and is_train == 0:
            time.sleep(1)
            # 训练并保存，参数：path 为保存路径默认/root/user/model/module.bin
            SL.train()
            is_train = 1
            str_display = "学习已完成"
    canvas.draw_string(0,0,str_display,scale=1,color=(255,0,0),thickness=1)
    canvas.draw_string(0,16,"采集次数："+str(count),scale=1,color=(255,0,0),thickness=1)
    Screen.show(canvas,48,8)

识别

from cocopiPython import cameraPython,Screen
from AI import SelfLearning

Screen = Screen()
camera = cameraPython()
SL= SelfLearning(['鼠标','键盘','水杯'])
SL.load()

while True:
    # 获取摄像头数据
    img = camera.capture()
    canvas = img.crop(48,8,224,224)
    data = SL.predict(canvas)
    canvas.draw_string(0,5,(str(data["name"])+":"+str(data["score"])),scale=1,color=(255,0,0),thickness=1)
    Screen.show(canvas,48,8)

拼音识别

from AI import PinyinRecognition

模型初始化

PinyinRecognition(keywords)

参数说明

参数	类型	说明
keywords	列表	拼音参数，文字的拼音与音调，必填

获取结果

getresult()

样例

from cocopiPython import Screen
from AI import PinyinRecognition

Screen = Screen()
canvas = Screen.create()

keywords = [["ni3 hao3",0.1],["hao3 de1",0.1],["wan3 shang4 hao3",0.1]] # 设置拼音参数
PinyinRecognition = PinyinRecognition(keywords)

data =""
while True:
    canvas.clear()
    data = str(PinyinRecognition.getResult()) # 获取拼音识别结果
    print("jieguo:"+str(data))
    canvas.draw_string(0,0, "请对准麦克风说出指令", scale = 2, color = (255,0,0) , thickness = 1)
    if data != "None":
        canvas.draw_string(0, 30, "拼音识别结果:", scale=2, color=(255,0,0), thickness = 1)
        canvas.draw_string(30, 60, (data), scale=2, color=(255,255,255), thickness = 1)
    Screen.show(canvas)

车牌识别

from AI import PlateRecognition

模型初始化

PlateRecognition()

加载识别

loadModel(img)

参数说明

参数	类型	说明
img	image.Image()	要识别的Image对象，必填

样例

from cocopiPython import cameraPython,Screen
from AI import PlateRecognition

Screen = Screen() # 画布初始化
camera = cameraPython() # 摄像头初始化
PlateRecognition = PlateRecognition() # 车牌识别初始化
while True:
    canvas = camera.capture() # 获取摄像头图像
    canvas = canvas.crop(48,8,224,224) # 裁剪图像尺寸
    data = PlateRecognition.load(canvas) # 加载识别
    for i in data:
        canvas.draw_rectangle(i["X"],i["Y"],i["X"]+i["W"],i["Y"]+i["H"],color=(255,0,0),thickness=1) # 检测框
        canvas.draw_string(i["X"],i["Y"]-10,(str(i["name"])),color=(255,0,0)) # 名称
    Screen.show(canvas,48,8) # 显示