本文整理汇总了Python中mnist.MNIST.select_emnist方法的典型用法代码示例。如果您正苦于以下问题：Python MNIST.select_emnist方法的具体用法？Python MNIST.select_emnist怎么用？Python MNIST.select_emnist使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mnist.MNIST的用法示例。
在下文中一共展示了MNIST.select_emnist方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from mnist import MNIST [as 别名]
# 或者: from mnist.MNIST import select_emnist [as 别名]
def main():
emnist_data = MNIST(path='gzip\\', return_type='numpy')
emnist_data.select_emnist('letters')
x_orig, y_orig = emnist_data.load_training()
train_x = x_orig[0:3000, :]
Y = y_orig.reshape(y_orig.shape[0], 1)
Y = Y[0:3000, :]
Y = Y[:, 0]
train_y = (np.arange(np.max(Y) + 1) == Y[:, None]).astype(int)
X_test = x_orig[3000:3500, :]
Y_test = y_orig.reshape(y_orig.shape[0], 1)
Y_test = Y_test[3000:3500, :]
Y_test = Y_test[:, 0]
letter_count = {0: 'CHECK', 1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j',
11: 'k',
12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v',
23: 'w',
24: 'x', 25: 'y', 26: 'z', 27: 'CHECK'}
d1 = Alphabet_Recognizer_LR.model(train_x.T, train_y.T, Y, X_test.T, Y_test, num_iters=800, alpha=0.00009,
print_cost=True)
w_LR = d1["w"]
b_LR = d1["b"]
d2 = Alphabet_Recognizer_NN.model_nn(train_x.T, train_y.T, Y, X_test.T, Y_test, n_h=100, num_iters=4000,
alpha=0.005,
print_cost=True)
dims = [784, 100, 80, 50, 27]
d3 = Alphabet_Recognizer_DL.model_DL(train_x.T, train_y.T, Y, X_test.T, Y_test, dims, alpha=0.05,
num_iterations=900,
print_cost=True)
cap = cv2.VideoCapture(0)
Lower_green = np.array([110, 50, 50])
Upper_green = np.array([130, 255, 255])
pts = deque(maxlen=512)
blackboard = np.zeros((480, 640, 3), dtype=np.uint8)
digit = np.zeros((200, 200, 3), dtype=np.uint8)
ans1 = 0
ans2 = 0
ans3 = 0
while (cap.isOpened()):
ret, img = cap.read()
img = cv2.flip(img, 1)
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(imgHSV, Lower_green, Upper_green)
blur = cv2.medianBlur(mask, 15)
blur = cv2.GaussianBlur(blur, (5, 5), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[1]
center = None
if len(cnts) >= 1:
cnt = max(cnts, key=cv2.contourArea)
if cv2.contourArea(cnt) > 250:
((x, y), radius) = cv2.minEnclosingCircle(cnt)
cv2.circle(img, (int(x), int(y)), int(radius), (0, 255, 255), 2)
cv2.circle(img, center, 5, (0, 0, 255), -1)
M = cv2.moments(cnt)
center = (int(M['m10'] / M['m00']), int(M['m01'] / M['m00']))
pts.appendleft(center)
for i in range(1, len(pts)):
if pts[i - 1] is None or pts[i] is None:
continue
cv2.line(blackboard, pts[i - 1], pts[i], (255, 255, 255), 10)
cv2.line(img, pts[i - 1], pts[i], (0, 0, 255), 5)
elif len(cnts) == 0:
if len(pts) != []:
blackboard_gray = cv2.cvtColor(blackboard, cv2.COLOR_BGR2GRAY)
blur1 = cv2.medianBlur(blackboard_gray, 15)
blur1 = cv2.GaussianBlur(blur1, (5, 5), 0)
thresh1 = cv2.threshold(blur1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
blackboard_cnts = cv2.findContours(thresh1.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[1]
if len(blackboard_cnts) >= 1:
cnt = max(blackboard_cnts, key=cv2.contourArea)
print(cv2.contourArea(cnt))
if cv2.contourArea(cnt) > 2000:
x, y, w, h = cv2.boundingRect(cnt)
digit = blackboard_gray[y:y + h, x:x + w]
newImage = cv2.resize(digit, (28, 28))
newImage = np.array(newImage)
newImage = newImage.flatten()
newImage = newImage.reshape(newImage.shape[0], 1)
ans1 = Alphabet_Recognizer_LR.predict_for_cv(w_LR, b_LR, newImage)
ans2 = Alphabet_Recognizer_NN.predict_nn_for_cv(d2, newImage)
ans3 = Alphabet_Recognizer_DL.predict_for_cv(d3, newImage)
pts = deque(maxlen=512)
blackboard = np.zeros((480, 640, 3), dtype=np.uint8)
cv2.putText(img, "Logistic Regression : " + str(letter_count[ans1]), (10, 410),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(img, "Shallow Network : " + str(letter_count[ans2]), (10, 440),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(img, "Deep Network : " + str(letter_count[ans3]), (10, 470),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.imshow("Frame", img)
#.........这里部分代码省略.........