石头剪刀布模拟器
1.学习目的
学习摄像头的画面进行手势区域裁剪。
2.示例代码
'''
本程序遵循GPL V3协议, 请遵循协议
实验平台: DshanPI CanMV
开发板文档站点 :: https://eai.100ask.net/
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百问网官方B站 : https://space.bilibili.com/275908810
百问网官方淘宝 : https://100ask.taobao.com
'''
from libs.PipeLine import PipeLine, ScopedTiming
from libs.AIBase import AIBase
from libs.AI2D import Ai2d
from random import randint
import os
import ujson
from media.media import *
from time import *
import nncase_runtime as nn
import ulab.numpy as np
import time
import image
import aicube
import random
import gc
import sys
# 自定义手掌检测任务类
class HandDetApp(AIBase):
def __init__(self,kmodel_path,labels,model_input_size,anchors,confidence_threshold=0.2,nms_threshold=0.5,nms_option=False, strides=[8,16,32],rgb888p_size=[1920,1080],display_size=[1920,1080],debug_mode=0):
super().__init__(kmodel_path,model_input_size,rgb888p_size,debug_mode)
# kmodel路径
self.kmodel_path=kmodel_path
self.labels=labels
# 检测模型输入分辨率
self.model_input_size=model_input_size
# 置信度阈值
self.confidence_threshold=confidence_threshold
# nms阈值
self.nms_threshold=nms_threshold
self.anchors=anchors # 锚框，检测任务使用
self.strides = strides # 特征下采样倍数
self.nms_option = nms_option # NMS选项，如果为True做类间NMS,如果为False做类内NMS
# sensor给到AI的图像分辨率，宽16字节对齐
self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
# 视频输出VO分辨率，宽16字节对齐
self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
# debug模式
self.debug_mode=debug_mode
# 实例化Ai2d，用于实现模型预处理
self.ai2d=Ai2d(debug_mode)
# 设置Ai2d的输入输出格式和类型
self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT,nn.ai2d_format.NCHW_FMT,np.uint8, np.uint8)
# 配置预处理操作，这里使用了pad和resize，Ai2d支持crop/shift/pad/resize/affine，具体代码请打开/sdcard/app/libs/AI2D.py查看
def config_preprocess(self,input_image_size=None):
with ScopedTiming("set preprocess config",self.debug_mode > 0):
# 初始化ai2d预处理配置，默认为sensor给到AI的尺寸，可以通过设置input_image_size自行修改输入尺寸
ai2d_input_size = input_image_size if input_image_size else self.rgb888p_size
# 计算padding参数并应用pad操作，以确保输入图像尺寸与模型输入尺寸匹配
top, bottom, left, right = self.get_padding_param()
self.ai2d.pad([0, 0, 0, 0, top, bottom, left, right], 0, [114, 114, 114])
# 使用双线性插值进行resize操作，调整图像尺寸以符合模型输入要求
self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
# 构建预处理流程，参数是ai2d预处理的输入tensor的shape和输出tensor的shape
self.ai2d.build([1,3,ai2d_input_size[1],ai2d_input_size[0]],[1,3,self.model_input_size[1],self.model_input_size[0]])
# 自定义当前任务的后处理，results是模型输出array的列表，这里使用了aicube库的anchorbasedet_post_process接口
def postprocess(self,results):
with ScopedTiming("postprocess",self.debug_mode > 0):
dets = aicube.anchorbasedet_post_process(results[0], results[1], results[2], self.model_input_size, self.rgb888p_size, self.strides, len(self.labels), self.confidence_threshold, self.nms_threshold, self.anchors, self.nms_option)
# 返回手掌检测结果
return dets
# 计算padding参数，确保输入图像尺寸与模型输入尺寸匹配
def get_padding_param(self):
# 根据目标宽度和高度计算比例因子
dst_w = self.model_input_size[0]
dst_h = self.model_input_size[1]
input_width = self.rgb888p_size[0]
input_high = self.rgb888p_size[1]
ratio_w = dst_w / input_width
ratio_h = dst_h / input_high
# 选择较小的比例因子，以确保图像内容完整
if ratio_w < ratio_h:
ratio = ratio_w
else:
ratio = ratio_h
# 计算新的宽度和高度
new_w = int(ratio * input_width)
new_h = int(ratio * input_high)
# 计算宽度和高度的差值，并确定padding的位置
dw = (dst_w - new_w) / 2
dh = (dst_h - new_h) / 2
top = int(round(dh - 0.1))
bottom = int(round(dh + 0.1))
left = int(round(dw - 0.1))
right = int(round(dw + 0.1))
return top, bottom, left, right
# 自定义手势关键点分类任务类
class HandKPClassApp(AIBase):
def __init__(self,kmodel_path,model_input_size,rgb888p_size=[1920,1080],display_size=[1920,1080],debug_mode=0):
super().__init__(kmodel_path,model_input_size,rgb888p_size,debug_mode)
# kmodel路径
self.kmodel_path=kmodel_path
# 检测模型输入分辨率
self.model_input_size=model_input_size
# sensor给到AI的图像分辨率，宽16字节对齐
self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
# 视频输出VO分辨率，宽16字节对齐
self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
# crop参数列表
self.crop_params=[]
# debug模式
self.debug_mode=debug_mode
self.ai2d=Ai2d(debug_mode)
self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT,nn.ai2d_format.NCHW_FMT,np.uint8, np.uint8)
# 配置预处理操作，这里使用了crop和resize，Ai2d支持crop/shift/pad/resize/affine，具体代码请打开/sdcard/app/libs/AI2D.py查看
def config_preprocess(self,det,input_image_size=None):
with ScopedTiming("set preprocess config",self.debug_mode > 0):
# 初始化ai2d预处理配置，默认为sensor给到AI的尺寸，可以通过设置input_image_size自行修改输入尺寸
ai2d_input_size=input_image_size if input_image_size else self.rgb888p_size
# 计算crop参数并设置crop预处理
self.crop_params = self.get_crop_param(det)
self.ai2d.crop(self.crop_params[0],self.crop_params[1],self.crop_params[2],self.crop_params[3])
# 设置resize预处理
self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
# 构建预处理流程，参数是ai2d预处理的输入tensor的shape和输出tensor的shape
self.ai2d.build([1,3,ai2d_input_size[1],ai2d_input_size[0]],[1,3,self.model_input_size[1],self.model_input_size[0]])
# 自定义后处理，results是模型输出array的列表
def postprocess(self,results):
with ScopedTiming("postprocess",self.debug_mode > 0):
results=results[0].reshape(results[0].shape[0]*results[0].shape[1])
results_show = np.zeros(results.shape,dtype=np.int16)
results_show[0::2] = results[0::2] * self.crop_params[3] + self.crop_params[0]
results_show[1::2] = results[1::2] * self.crop_params[2] + self.crop_params[1]
gesture=self.hk_gesture(results_show)
results_show[0::2] = results_show[0::2] * (self.display_size[0] / self.rgb888p_size[0])
results_show[1::2] = results_show[1::2] * (self.display_size[1] / self.rgb888p_size[1])
return results_show,gesture
# 计算crop参数
def get_crop_param(self,det_box):
x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5]
w,h= int(x2 - x1),int(y2 - y1)
w_det = int(float(x2 - x1) * self.display_size[0] // self.rgb888p_size[0])
h_det = int(float(y2 - y1) * self.display_size[1] // self.rgb888p_size[1])
x_det = int(x1*self.display_size[0] // self.rgb888p_size[0])
y_det = int(y1*self.display_size[1] // self.rgb888p_size[1])
length = max(w, h)/2
cx = (x1+x2)/2
cy = (y1+y2)/2
ratio_num = 1.26*length
x1_kp = int(max(0,cx-ratio_num))
y1_kp = int(max(0,cy-ratio_num))
x2_kp = int(min(self.rgb888p_size[0]-1, cx+ratio_num))
y2_kp = int(min(self.rgb888p_size[1]-1, cy+ratio_num))
w_kp = int(x2_kp - x1_kp + 1)
h_kp = int(y2_kp - y1_kp + 1)
return [x1_kp, y1_kp, w_kp, h_kp]
# 求两个vector之间的夹角
def hk_vector_2d_angle(self,v1,v2):
with ScopedTiming("hk_vector_2d_angle",self.debug_mode > 0):
v1_x,v1_y,v2_x,v2_y = v1[0],v1[1],v2[0],v2[1]
v1_norm = np.sqrt(v1_x * v1_x+ v1_y * v1_y)
v2_norm = np.sqrt(v2_x * v2_x + v2_y * v2_y)
dot_product = v1_x * v2_x + v1_y * v2_y
cos_angle = dot_product/(v1_norm*v2_norm)
angle = np.acos(cos_angle)*180/np.pi
return angle
# 根据手掌关键点检测结果判断手势类别
def hk_gesture(self,results):
with ScopedTiming("hk_gesture",self.debug_mode > 0):
angle_list = []
for i in range(5):
angle = self.hk_vector_2d_angle([(results[0]-results[i*8+4]), (results[1]-results[i*8+5])],[(results[i*8+6]-results[i*8+8]),(results[i*8+7]-results[i*8+9])])
angle_list.append(angle)
thr_angle,thr_angle_thumb,thr_angle_s,gesture_str = 65.,53.,49.,None
if 65535. not in angle_list:
if (angle_list[0]>thr_angle_thumb) and (angle_list[1]>thr_angle) and (angle_list[2]>thr_angle) and (angle_list[3]>thr_angle) and (angle_list[4]>thr_angle):
gesture_str = "fist"
elif (angle_list[0]<thr_angle_s) and (angle_list[1]<thr_angle_s) and (angle_list[2]<thr_angle_s) and (angle_list[3]<thr_angle_s) and (angle_list[4]<thr_angle_s):
gesture_str = "five"
elif (angle_list[0]<thr_angle_s) and (angle_list[1]<thr_angle_s) and (angle_list[2]>thr_angle) and (angle_list[3]>thr_angle) and (angle_list[4]>thr_angle):
gesture_str = "gun"
elif (angle_list[0]<thr_angle_s) and (angle_list[1]<thr_angle_s) and (angle_list[2]>thr_angle) and (angle_list[3]>thr_angle) and (angle_list[4]<thr_angle_s):
gesture_str = "love"
elif (angle_list[0]>5) and (angle_list[1]<thr_angle_s) and (angle_list[2]>thr_angle) and (angle_list[3]>thr_angle) and (angle_list[4]>thr_angle):
gesture_str = "one"
elif (angle_list[0]<thr_angle_s) and (angle_list[1]>thr_angle) and (angle_list[2]>thr_angle) and (angle_list[3]>thr_angle) and (angle_list[4]<thr_angle_s):
gesture_str = "six"
elif (angle_list[0]>thr_angle_thumb) and (angle_list[1]<thr_angle_s) and (angle_list[2]<thr_angle_s) and (angle_list[3]<thr_angle_s) and (angle_list[4]>thr_angle):
gesture_str = "three"
elif (angle_list[0]<thr_angle_s) and (angle_list[1]>thr_angle) and (angle_list[2]>thr_angle) and (angle_list[3]>thr_angle) and (angle_list[4]>thr_angle):
gesture_str = "thumbUp"
elif (angle_list[0]>thr_angle_thumb) and (angle_list[1]<thr_angle_s) and (angle_list[2]<thr_angle_s) and (angle_list[3]>thr_angle) and (angle_list[4]>thr_angle):
gesture_str = "yeah"
return gesture_str
# 猜拳游戏任务类
class FingerGuess:
def __init__(self,hand_det_kmodel,hand_kp_kmodel,det_input_size,kp_input_size,labels,anchors,confidence_threshold=0.25,nms_threshold=0.3,nms_option=False,strides=[8,16,32],guess_mode=3,rgb888p_size=[1280,720],display_size=[1920,1080],debug_mode=0):
# 手掌检测模型路径
self.hand_det_kmodel=hand_det_kmodel
# 手掌关键点模型路径
self.hand_kp_kmodel=hand_kp_kmodel
# 手掌检测模型输入分辨率
self.det_input_size=det_input_size
# 手掌关键点模型输入分辨率
self.kp_input_size=kp_input_size
self.labels=labels
# anchors
self.anchors=anchors
# 置信度阈值
self.confidence_threshold=confidence_threshold
# nms阈值
self.nms_threshold=nms_threshold
# nms选项
self.nms_option=nms_option
# 特征图针对输入的下采样倍数
self.strides=strides
# sensor给到AI的图像分辨率，宽16字节对齐
self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
# 视频输出VO分辨率，宽16字节对齐
self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
# debug_mode模式
self.debug_mode=debug_mode
self.guess_mode=guess_mode
# 石头剪刀布的贴图array
self.five_image = self.read_file("/sdcard/examples/utils/five.bin")
self.fist_image = self.read_file("/sdcard/examples/utils/fist.bin")
self.shear_image = self.read_file("/sdcard/examples/utils/shear.bin")
self.counts_guess = -1 # 猜拳次数 计数
self.player_win = 0 # 玩家 赢次计数
self.k230_win = 0 # k230 赢次计数
self.sleep_end = False # 是否 停顿
self.set_stop_id = True # 是否 暂停猜拳
self.LIBRARY = ["fist","yeah","five"] # 猜拳 石头剪刀布 三种方案的dict
self.hand_det=HandDetApp(self.hand_det_kmodel,self.labels,model_input_size=self.det_input_size,anchors=self.anchors,confidence_threshold=self.confidence_threshold,nms_threshold=self.nms_threshold,nms_option=self.nms_option,strides=self.strides,rgb888p_size=self.rgb888p_size,display_size=self.display_size,debug_mode=0)
self.hand_kp=HandKPClassApp(self.hand_kp_kmodel,model_input_size=self.kp_input_size,rgb888p_size=self.rgb888p_size,display_size=self.display_size)
self.hand_det.config_preprocess()
# run函数
def run(self,input_np):
# 先进行手掌检测
det_boxes=self.hand_det.run(input_np)
boxes=[]
gesture_res=[]
for det_box in det_boxes:
# 对检测的手做手势识别
x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5]
w,h= int(x2 - x1),int(y2 - y1)
if (h<(0.1*self.rgb888p_size[1])):
continue
if (w<(0.25*self.rgb888p_size[0]) and ((x1<(0.03*self.rgb888p_size[0])) or (x2>(0.97*self.rgb888p_size[0])))):
continue
if (w<(0.15*self.rgb888p_size[0]) and ((x1<(0.01*self.rgb888p_size[0])) or (x2>(0.99*self.rgb888p_size[0])))):
continue
self.hand_kp.config_preprocess(det_box)
results_show,gesture=self.hand_kp.run(input_np)
boxes.append(det_box)
gesture_res.append(gesture)
return boxes,gesture_res
# 绘制效果
def draw_result(self,pl,dets,gesture_res):
pl.osd_img.clear()
# 手掌的手势分类得到用户的出拳，根据不同模式给出开发板的出拳，并将对应的贴图放到屏幕上显示
if (len(dets) >= 2):
pl.osd_img.draw_string_advanced( self.display_size[0]//2-50,self.display_size[1]//2-50,60, "请保证只有一只手入镜！", color=(255,255,0,0))
elif (self.guess_mode == 0):
draw_img_np = np.zeros((self.display_size[1],self.display_size[0],4),dtype=np.uint8)
draw_img = image.Image(self.display_size[0], self.display_size[1], image.ARGB8888, alloc=image.ALLOC_REF,data = draw_img_np)
if (gesture_res[0] == "fist"):
draw_img_np[:400,:400,:] = self.shear_image
elif (gesture_res[0] == "five"):
draw_img_np[:400,:400,:] = self.fist_image
elif (gesture_res[0] == "yeah"):
draw_img_np[:400,:400,:] = self.five_image
pl.osd_img.copy_from(draw_img)
elif (self.guess_mode == 1):
draw_img_np = np.zeros((self.display_size[1],self.display_size[0],4),dtype=np.uint8)
draw_img = image.Image(self.display_size[0], self.display_size[1], image.ARGB8888, alloc=image.ALLOC_REF,data = draw_img_np)
if (gesture_res[0] == "fist"):
draw_img_np[:400,:400,:] = self.five_image
elif (gesture_res[0] == "five"):
draw_img_np[:400,:400,:] = self.shear_image
elif (gesture_res[0] == "yeah"):
draw_img_np[:400,:400,:] = self.fist_image
pl.osd_img.copy_from(draw_img)
else:
draw_img_np = np.zeros((self.display_size[1],self.display_size[0],4),dtype=np.uint8)
draw_img = image.Image(self.display_size[0], self.display_size[1], image.ARGB8888, alloc=image.ALLOC_REF,data = draw_img_np)
if (self.sleep_end):
time.sleep_ms(2000)
self.sleep_end = False
if (len(dets) == 0):
self.set_stop_id = True
return
if (self.counts_guess == -1 and gesture_res[0] != "fist" and gesture_res[0] != "yeah" and gesture_res[0] != "five"):
draw_img.draw_string_advanced( self.display_size[0]//2-50,self.display_size[1]//2-50,60, "游戏开始", color=(255,255,0,0))
draw_img.draw_string_advanced( self.display_size[0]//2-50,self.display_size[1]//2-50,60, "第一回合", color=(255,255,0,0))
elif (self.counts_guess == self.guess_mode):
draw_img.clear()
if (self.k230_win > self.player_win):
draw_img.draw_string_advanced( self.display_size[0]//2-50,self.display_size[1]//2-50,60, "你输了！", color=(255,255,0,0))
elif (self.k230_win < self.player_win):
draw_img.draw_string_advanced( self.display_size[0]//2-50,self.display_size[1]//2-50,60, "你赢了！", color=(255,255,0,0))
else:
draw_img.draw_string_advanced( self.display_size[0]//2-50,self.display_size[1]//2-50,60, "平局", color=(255,255,0,0))
self.counts_guess = -1
self.player_win = 0
self.k230_win = 0
self.sleep_end = True
else:
if (self.set_stop_id):
if (self.counts_guess == -1 and (gesture_res[0] == "fist" or gesture_res[0] == "yeah" or gesture_res[0] == "five")):
self.counts_guess = 0
if (self.counts_guess != -1 and (gesture_res[0] == "fist" or gesture_res[0] == "yeah" or gesture_res[0] == "five")):
k230_guess = randint(1,10000) % 3
if (gesture_res[0] == "fist" and self.LIBRARY[k230_guess] == "yeah"):
self.player_win += 1
elif (gesture_res[0] == "fist" and self.LIBRARY[k230_guess] == "five"):
self.k230_win += 1
if (gesture_res[0] == "yeah" and self.LIBRARY[k230_guess] == "fist"):
self.k230_win += 1
elif (gesture_res[0] == "yeah" and self.LIBRARY[k230_guess] == "five"):
self.player_win += 1
if (gesture_res[0] == "five" and self.LIBRARY[k230_guess] == "fist"):
self.player_win += 1
elif (gesture_res[0] == "five" and self.LIBRARY[k230_guess] == "yeah"):
self.k230_win += 1
if (self.LIBRARY[k230_guess] == "fist"):
draw_img_np[:400,:400,:] = self.fist_image
elif (self.LIBRARY[k230_guess] == "five"):
draw_img_np[:400,:400,:] = self.five_image
elif (self.LIBRARY[k230_guess] == "yeah"):
draw_img_np[:400,:400,:] = self.shear_image
self.counts_guess += 1
draw_img.draw_string_advanced(self.display_size[0]//2-50,self.display_size[1]//2-50,60,"第" + str(self.counts_guess) + "回合", color=(255,255,0,0))
self.set_stop_id = False
self.sleep_end = True
else:
draw_img.draw_string_advanced(self.display_size[0]//2-50,self.display_size[1]//2-50,60,"第" + str(self.counts_guess+1) + "回合", color=(255,255,0,0))
pl.osd_img.copy_from(draw_img)
# 读取石头剪刀布的bin文件方法
def read_file(self,file_name):
image_arr = np.fromfile(file_name,dtype=np.uint8)
image_arr = image_arr.reshape((400,400,4))
return image_arr
if __name__=="__main__":
# 显示模式，默认"hdmi",可以选择"hdmi"和"lcd"
display_mode="lcd"
# k230保持不变，k230d可调整为[640,360]
rgb888p_size = [1920, 1080]
if display_mode=="hdmi":
display_size=[1920,1080]
else:
display_size=[800,480]
# 手掌检测模型路径
hand_det_kmodel_path="/sdcard/examples/kmodel/hand_det.kmodel"
# 手掌关键点模型路径
hand_kp_kmodel_path="/sdcard/examples/kmodel/handkp_det.kmodel"
# 其它参数
anchors_path="/sdcard/examples/utils/prior_data_320.bin"
hand_det_input_size=[512,512]
hand_kp_input_size=[256,256]
confidence_threshold=0.2
nms_threshold=0.5
labels=["hand"]
anchors = [26,27, 53,52, 75,71, 80,99, 106,82, 99,134, 140,113, 161,172, 245,276]
# 猜拳模式 0 玩家稳赢 ， 1 玩家必输 ， n > 2 多局多胜
guess_mode = 3
# 初始化PipeLine，只关注传给AI的图像分辨率，显示的分辨率
pl=PipeLine(rgb888p_size=rgb888p_size,display_size=display_size,display_mode=display_mode)
pl.create()
hkc=FingerGuess(hand_det_kmodel_path,hand_kp_kmodel_path,det_input_size=hand_det_input_size,kp_input_size=hand_kp_input_size,labels=labels,anchors=anchors,confidence_threshold=confidence_threshold,nms_threshold=nms_threshold,nms_option=False,strides=[8,16,32],guess_mode=guess_mode,rgb888p_size=rgb888p_size,display_size=display_size)
try:
while True:
os.exitpoint()
with ScopedTiming("total",1):
img=pl.get_frame() # 获取当前帧
det_boxes,gesture_res=hkc.run(img) # 推理当前帧
hkc.draw_result(pl,det_boxes,gesture_res) # 绘制推理结果
pl.show_image() # 展示推理结果
gc.collect()
except Exception as e:
sys.print_exception(e)
finally:
hkc.hand_det.deinit()
hkc.hand_kp.deinit()
pl.destroy()
3.实验结果
点击运行代码后可以查看石头剪刀布模拟器结果：
