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main.py
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main.py
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"""
输出带操作
"""
from models.yolov3 import Yolov3
from models.resnet import resnet50
from utils.utils import process_data, scale_coords, process_coordinate, \
draw_bd_handpose, get_k, plot_one_box, load_classes, parse_data_cfg
from utils.nms import non_max_suppression
import torch
import numpy as np
import cv2
import math
import os
from javaPredict import load_java_pkg, java_close
from winOs.win_cmd import *
from models.experimental import attempt_load
def get_hand_data(im0, mirror=True):
if mirror:
im0 = cv2.flip(im0, 1, dst=None) # 水平镜像处理
img = process_data(im0, 416) # 数据预处理（用于传入yolo网络识别手的位置）
# 图片检测
img = torch.from_numpy(img).unsqueeze(0).to(device)
# pred, _ = yolov5_model(img)
pred = yolo_model(img)[0]
# 非极大值抑制nms
detections = non_max_suppression(pred, conf_thres, nms_thres)[0]
if detections is None or len(detections) == 0:
return im0, 0 # 未检测到手，直接跳过手势判断
# 将结果映射到原图
detections[:, :4] = scale_coords(416, detections[:, :4], im0.shape).round()
results = []
for *coordinate, conf, cls_conf, cls in detections:
x1, x2, y1, y2 = process_coordinate(coordinate, im0.shape)
hand_img = im0[y1:y2, x1:x2]
img_width = hand_img.shape[1]
img_height = hand_img.shape[0]
# 输入图片预处理
hand_img = cv2.resize(hand_img, (256, 256), interpolation=cv2.INTER_CUBIC)
hand_img = hand_img.astype(np.float32)
hand_img = (hand_img - 128.0) / 256.0
hand_img = hand_img.transpose(2, 0, 1)
hand_img = torch.from_numpy(hand_img)
hand_img = hand_img.unsqueeze_(0)
if use_cuda:
hand_img = hand_img.cuda() # (bs, 3, h, w)
pre_ = resnet50_model(hand_img.float()) # 模型推理
output = pre_.cpu().detach().numpy()
output = np.squeeze(output)
pts_hand = {} # 构建关键点连线可视化结构
point = []
for i in range(int(output.shape[0] / 2)):
x = (output[i * 2 + 0] * float(img_width)) + x1
y = (output[i * 2 + 1] * float(img_height)) + y1
point.append((x, y))
# cv2.putText(im0, str(i), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 0), 2)
# 绘制关键点
cv2.circle(im0, (int(x), int(y)), 3, (255, 50, 60), -1)
cv2.circle(im0, (int(x), int(y)), 1, (255, 150, 180), -1)
# pts_hand[str(i)] = {}
pts_hand[str(i)] = {
"x": x,
"y": y,
}
draw_bd_handpose(im0, pts_hand, 0, 0) # 绘制关键点连线
flag = 0
hand_label = "hand"
if abs(180 - abs(get_k(point[0], point[3]) - get_k(point[3], point[4])) * 180 / math.pi) > 135:
hand_label += "-1"
flag += 1
if abs(180 - abs(get_k(point[0], point[6]) - get_k(point[6], point[8])) * 180 / math.pi) > 135:
hand_label += "-2"
flag += 10
if abs(180 - abs(get_k(point[0], point[10]) - get_k(point[10], point[12])) * 180 / math.pi) > 135:
hand_label += "-3"
flag += 100
if abs(180 - abs(get_k(point[0], point[14]) - get_k(point[14], point[16])) * 180 / math.pi) > 135:
hand_label += "-4"
flag += 1000
if abs(180 - abs(get_k(point[0], point[18]) - get_k(point[18], point[20])) * 180 / math.pi) > 135:
hand_label += "-5"
flag += 10000
if flag == 0 or flag == 1: # 握拳动作因人而异
hand_label = "0"
elif flag == 10 or flag == 11:
hand_label = "1"
else:
hand_label = "2"
label = '%s %.2f' % (hand_label, conf)
plot_one_box(coordinate, im0, label=label, color=(100, 0, 100), line_thickness=2)
results.append({
"x": x1,
"y": y1,
"w": x2 - x1,
"h": y2 - y1,
"label": hand_label
})
return im0, results
if __name__ == '__main__':
from config import conf_thres, nms_thres, cmd, data_cfg, \
resnet50_model_path, yolov3_model_path, yolo_choose, \
yolov5s_model_path, yolov5sm_model_path
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
# 加载resnet50模型
resnet50_model = resnet50(num_classes=42, img_size=256)
resnet50_model = resnet50_model.to(device)
resnet50_model.eval()
if os.access(resnet50_model_path, os.F_OK):
chkpt = torch.load(resnet50_model_path, map_location=device)
resnet50_model.load_state_dict(chkpt)
else:
raise Exception("resnet50模型权重文件丢失，无法继续进行。")
# 加载yolov5s模型
if yolo_choose == "yolov3":
# 加载yolov3模型
classes = load_classes(parse_data_cfg(data_cfg)['names'])
num_classes = len(classes)
anchors = [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45), (59, 119), (116, 90), (156, 198),
(373, 326)] # yolov5先验框
yolo_model = Yolov3(num_classes=num_classes, anchors=anchors)
yolo_model = yolo_model.to(device)
yolo_model.eval()
if os.access(yolov3_model_path, os.F_OK):
yolo_model.load_state_dict(torch.load(yolov3_model_path, map_location=device)['model'])
else:
raise Exception("yolov3模型权重文件丢失，无法继续进行。")
elif yolo_choose == "yolov5sm":
if os.access(yolov5sm_model_path, os.F_OK):
yolo_model = attempt_load(yolov5sm_model_path, map_location=device)
else:
raise Exception("yolov5sm模型权重文件丢失，无法继续进行。")
else:
if os.access(yolov5s_model_path, os.F_OK):
yolo_model = attempt_load(yolov5s_model_path, map_location=device)
else:
raise Exception("yolov5sm模型权重文件丢失，无法继续进行。")
# 加载java环境,并返回预测对象
recognizer, recorderInterface = load_java_pkg()
# video_capture = cv2.VideoCapture(1, cv2.CAP_DSHOW)
video_capture = cv2.VideoCapture(0) # 选择默认摄像头
# video_capture = cv2.VideoCapture('./video/3.mp4') # 选择视频
isSet = 0
maxHeight = 0
setFrame = 15
middleLine = 240
hands = {}
d = {}
with torch.no_grad(): # 设置无梯度运行
while True:
ret, im0 = video_capture.read() # 读取图片流
if ret:
im0, result = get_hand_data(im0, mirror=True)
if result:
if isSet < setFrame and len(result) == 2:
"""初始化（需要setFrame帧内手势最大位置变化不大）"""
newMaxHeight = max(result[0]["y"], result[1]["y"])
if abs(maxHeight - newMaxHeight) > 20:
maxHeight = max(result[0]["y"], result[1]["y"])
isSet = 0
else:
isSet += 1
if isSet == setFrame:
maxHeight -= 40 # 略大保证终止
recognizer.setDockLevel(maxHeight)
middleLine = (result[0]["x"] + result[0]["w"] // 2 + result[1]["x"] + result[1]["w"] // 2) // 2 # 取两手中点为中线
hands = {}
print("初始化成功,maxHeight:", maxHeight, "middleLine:", middleLine)
else:
"""初始化完成后的判断"""
if not hands:
if len(result) > 1:
"""有多个手，取边缘两只手进行比较，根据相对位置判断左右手"""
if result[0]["x"] + result[0]["w"] // 2 < result[-1]["x"] + result[-1]["w"] // 2:
hands['0'] = result[0]
hands['1'] = result[1]
else:
hands['0'] = result[1]
hands['1'] = result[0]
else:
t1 = None
t2 = None
m1 = 999999
m2 = 999999
if hands.get("0", None): # 寻找与左手最近的手
v = hands.get("0")
for hand in result:
"""找出结果中和当前选择手最近的那个"""
l = (hand['x'] - v['x']) * (hand['x'] - v['x']) + (hand['y'] - v['y']) * (hand['y'] - v['y'])
if l < m1:
m1 = l
t1 = hand
if hands.get("1", None): # 寻找与右手最近的手
v = hands.get("1")
for hand in result:
"""找出结果中和当前选择手最近的那个"""
l = (hand['x'] - v['x']) * (hand['x'] - v['x']) + (hand['y'] - v['y']) * (hand['y'] - v['y'])
if l < m2:
m2 = l
t2 = hand
if t1 == t2:
if m1 < m2:
hands["0"] = t1
else:
hands["1"] = t2
else:
if t1:
hands["0"] = t1
if t2:
hands["1"] = t2
if hands:
for (k, v) in hands.items():
ans = recorderInterface.addOne(
v["x"],
v["y"],
v["w"],
v["h"],
int(v["label"]),
int(k)
)
if ans:
handpose = str(ans.getAction())
if handpose == "CLICK":
handpose = "点击"
elif handpose == "PAN":
if str(ans.getLocus()) == "LEFT":
handpose = "向左平移"
elif str(ans.getLocus()) == "RIGHT":
handpose = "向右平移"
elif handpose == "ZOOM":
if str(ans.getLocus()) == "IN":
handpose = "缩放"
elif str(ans.getLocus()) == "OUT":
handpose = "放大"
elif handpose == "GRAB":
handpose = "抓取"
elif handpose == "PUNCH":
handpose = "重置"
elif handpose == "ROTATE":
if str(ans.getLocus()) == "COUNTER_CLOCKWISE_ARC":
handpose = "逆时针旋转"
elif str(ans.getLocus()) == "CLOCKWISE_ARC":
handpose = "顺时针旋转"
if d.get("动作：" + handpose + ";手：" + str(ans.getHand())):
d["动作：" + handpose + ";手：" + str(ans.getHand())] += 1
else:
d["动作：" + handpose + ";手：" + str(ans.getHand())] = 1
print("动作：" + handpose + ";手：" + str(ans.getHand()))
if handpose == "重置":
isSet = 0
maxHeight = 0
hands = {}
recognizer.setDockLevel(maxHeight)
if cmd == 1:
cmdPPT(handpose)
elif cmd == 2:
cmdPic(handpose)
if isSet == setFrame:
"""画初始线"""
cv2.line(im0, (0, maxHeight), (im0.shape[1], maxHeight), (255, 0, 0), 3)
cv2.line(im0, (middleLine, 0), (middleLine, im0.shape[0]), (255, 0, 0), 3)
for (k, hand) in hands.items():
cv2.putText(im0, k, (hand["x"], hand['y']), cv2.FONT_HERSHEY_PLAIN, 2, (0, 255, 0), 3)
cv2.namedWindow('image', 0)
cv2.imshow("image", im0)
key = cv2.waitKey(1)
if key == 27:
break
elif key == 13:
isSet = 0
maxHeight = 0
hands = {}
recognizer.setDockLevel(maxHeight)
else:
break
cv2.destroyAllWindows()
java_close()
print(d)