修改：增加众数判定
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leaf
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8bfe21e11b
commit
96c6039474
58
ai.py
58
ai.py
@ -14,6 +14,7 @@ import torch.nn as nn
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import numpy as np
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import shutil
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import math
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from scipy import stats
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from os.path import exists
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from os import mkdir
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from pathlib import Path
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@ -27,6 +28,13 @@ def rotate(angle, x, y, point_x, point_y):
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return px, py
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# 归一化
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def normalize(x):
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max_x = np.max(x)
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min_x = np.min(x)
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return (x-min_x)/(max_x-min_x)
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class CNN(nn.Module):
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def __init__(self, m):
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super(CNN, self).__init__()
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@ -45,9 +53,9 @@ class CNN(nn.Module):
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self.conv2 = nn.Sequential(
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nn.Conv2d(16, 32, 5, 1, 2),
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nn.ReLU(),
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nn.MaxPool2d(3),
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nn.MaxPool2d(2),
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)
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self.med = nn.Linear(32 * 7 * 1, 500)
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self.med = nn.Linear(32 * 11 * 2, 500)
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self.med2 = nn.Linear(1*21*3, 100)
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self.med3 = nn.Linear(100, 500)
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self.out = nn.Linear(500, m) # fully connected layer, output 10 classes
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@ -131,7 +139,7 @@ class HandDetector:
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px, py = int(lm.x * w), int(lm.y * h)
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x_list.append(px)
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y_list.append(py)
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self.lmList.append([lm.x, lm.y, lm.z])
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self.lmList.append([lm.x, lm.y, 0])
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if draw:
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cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
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x_min, x_max = min(x_list), max(x_list)
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@ -147,6 +155,10 @@ class HandDetector:
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(0, 255, 0), 2)
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self.revolve(img)
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self.re_lmList = np.array(self.re_lmList)
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if self.re_lmList.any():
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self.re_lmList = np.concatenate((np.zeros((21, 1)), self.re_lmList), axis=1)
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self.re_lmList = np.concatenate((self.re_lmList, np.zeros((1, 4))), axis=0)
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return self.re_lmList, bbox_info
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@ -176,11 +188,18 @@ class HandDetector:
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theta = theta + math.pi
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# print(theta*180/math.pi)
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for i in self.lmList:
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x, y = rotate(theta, i[0], i[1], point_x, point_y)
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px, py = int(x * w), int(y * h)
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self.re_lmList.append([x, y, i[2]])
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px, py = rotate(theta, i[0] * w, i[1] * h, point_x * w, point_y * h)
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self.re_lmList.append([px, py, 0])
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if draw:
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cv2.circle(img, (px, py), 5, (0, 0, 255), cv2.FILLED)
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cv2.circle(img, (int(px), int(py)), 5, (0, 0, 255), cv2.FILLED)
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# 归一化
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x_array = normalize(np.array(self.re_lmList)[:, 0])
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# print(x_array)
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for i in range(len(x_array)):
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self.re_lmList[i][0] = x_array[i]
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y_array = normalize(np.array(self.re_lmList)[:, 1])
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for i in range(len(y_array)):
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self.re_lmList[i][1] = x_array[i]
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else:
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self.re_lmList = self.lmList
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return self.re_lmList
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@ -263,6 +282,8 @@ class Main:
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self.camera = None
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self.detector = HandDetector()
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self.default_datasets = "Datasets"
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self.len_x = 22
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self.len_y = 4
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def make_datasets(self, datasets_dir="default", n=100):
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if datasets_dir == "default":
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@ -276,24 +297,23 @@ class Main:
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self.camera.set(4, 720)
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label = input("label:")
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while not label == "":
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data = np.zeros([n, 21, 3])
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data = np.zeros([n, self.len_x, self.len_y])
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shape_list = np.zeros([n, 2], dtype=np.int16)
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hand_type = np.zeros(n, dtype=np.int8)
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zero_data = np.zeros([21, 3])
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count = 0
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cv2.startWindowThread()
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while True:
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frame, img = self.camera.read()
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img = self.detector.find_hands(img)
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result = np.zeros((21, 3))
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result = np.zeros((self.len_x, self.len_y))
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lm_list, bbox = self.detector.find_position(img)
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for i in range(len(lm_list)):
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result[i] = np.array(lm_list[i])
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shape = bbox["shape"]
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if result.sum() > 0: # 假设矩阵不为0，即捕捉到手部时
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if result.all() != zero_data.all(): # 假设矩阵不为0，即捕捉到手部时
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shape = bbox["shape"]
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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data[count] = result
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hand_type[count] = self.detector.hand_type()
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@ -332,20 +352,26 @@ class Main:
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self.detector = HandDetector()
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cnn = torch.load("CNN.pkl")
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out_label = cnn.out_label
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result = []
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disp = ""
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while True:
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frame, img = self.camera.read()
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img = self.detector.find_hands(img)
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lm_list, bbox = self.detector.find_position(img)
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if lm_list:
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if lm_list.any():
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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data = torch.Tensor(lm_list)
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data = data.unsqueeze(0)
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data = data.unsqueeze(0)
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test_output = cnn(data)
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result = torch.max(test_output, 1)[1].data.cpu().numpy()[0]
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cv2.putText(img, str(out_label[result]), (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
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if len(result) > 5:
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disp = str(out_label[stats.mode(result)[0][0]])
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result = []
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cv2.putText(img, disp, (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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cv2.imshow("camera", img)
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@ -359,6 +385,6 @@ class Main:
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if __name__ == '__main__':
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solution = Main()
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my_datasets_dir = "test"
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solution.make_datasets(my_datasets_dir, 50)
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solution.make_datasets(my_datasets_dir, 100)
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solution.train(my_datasets_dir)
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solution.gesture_recognition()
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