import numpy as np
def kMeans(X, K, maxIters = 10, plot_progress = None):
centroids = X[np.random.choice(np.arange(len(X)), K), :]
for i in range(maxIters):
# Cluster Assignment step
C = np.array([np.argmin([np.dot(x_i-y_k, x_i-y_k) for y_k in centroids]) for x_i in X])
# Move centroids step
centroids = [X[C == k].mean(axis = 0) for k in range(K)]
if plot_progress != None: plot_progress(X, C, np.array(centroids))
return np.array(centroids) , Cimport numpy as np
class Network(object):
def update_mini_batch(self, mini_batch, eta):
"""Update the network's weights and biases by applying
gradient descent using backpropagation to a single mini batch.
The "mini_batch" is a list of tuples "(x, y)", and "eta"
is the learning rate."""
#初始化
nabla_b = [np.zeros(b.shape) for b in self.biases]
nabla_w = [np.zeros(w.shape) for w in self.weights]
#更新梯度
for x, y in mini_batch:
delta_nabla_b, delta_nabla_w = self.backprop(x, y)
nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
#更新权重
self.weights = [w-(eta/len(mini_batch))*nw
for w, nw in zip(self.weights, nabla_w)]
self.biases = [b-(eta/len(mini_batch))*nb
for b, nb in zip(self.biases, nabla_b)]
def backprop(self, x, y):
"""Return a tuple "(nabla_b, nabla_w)" representing the
gradient for the cost function C_x. "nabla_b" and
"nabla_w" are layer-by-layer lists of numpy arrays, similar
to "self.biases" and "self.weights"."""
nabla_b = [np.zeros(b.shape) for b in self.biases]
nabla_w = [np.zeros(w.shape) for w in self.weights]
# feedforward
activation = x
activations = [x] # list to store all the activations, layer by layer
zs = [] # list to store all the z vectors, layer by layer
for b, w in zip(self.biases, self.weights):
z = np.dot(w, activation)+b
zs.append(z)
activation = sigmoid(z)
activations.append(activation)
# backward pass
delta = self.cost_derivative(activations[-1], y) * \
sigmoid_prime(zs[-1])
nabla_b[-1] = delta
nabla_w[-1] = np.dot(delta, activations[-2].transpose())
for l in range(2, self.num_layers):
z = zs[-l]
sp = sigmoid_prime(z)
delta = np.dot(self.weights[-l+1].transpose(), delta) * sp
nabla_b[-l] = delta
nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())
return (nabla_b, nabla_w)
def cost_derivative(self, output_activations, y):
"""Return the vector of partial derivatives \partial C_x /
\partial a for the output activations."""
return (output_activations-y)
def sigmoid(z):
"""The sigmoid function."""
return 1.0/(1.0+np.exp(-z))
def sigmoid_prime(z):
"""Derivative of the sigmoid function."""
return sigmoid(z)*(1-sigmoid(z))#Pyorch版本
def shuffle_up_torch(inputs, scale):
N, C, iH, iW = inputs.size()
oH = iH * scale
oW = iW * scale
oC = C // (scale ** 2)
output = inputs.view(N, oC, scale, scale, iH, iW)
output = output.permute(0, 1, 5, 2, 4, 3).contiguous()
output = output.view(N, oC, oH, oW)
return output
# numpy版本
def PS(I, r):
assert len(I.shape) == 3
assert r>0
r = int(r)
O = np.zeros((I.shape[0]*r, I.shape[1]*r, I.shape[2]/(r*2)))
for x in range(O.shape[0]):
for y in range(O.shape[1]):
for c in range(O.shape[2]):
c += 1
a = np.floor(x/r).astype("int")
b = np.floor(y/r).astype("int")
d = c*r*(y%r) + c*(x%r)
print a, b, d
O[x, y, c-1] = I[a, b, d]
return O def eval_pr(self, y_pred, y, num):
prec, recall = torch.zeros(num), torch.zeros(num)
thlist = torch.linspace(0, 1 - 1e-10, num)
for i in range(num):
y_temp = (y_pred >= thlist[i]).float()
tp = (y_temp * y).sum()
prec[i], recall[i] = tp / (y_temp.sum() + 1e-20), tp / y.sum()
return prec, recall# 分割iou
def _iou(pred, target, size_average = True):
b = pred.shape[0]
IoU = 0.0
for i in range(0,b):
#compute the IoU of the foreground
Iand1 = torch.sum(target[i,:,:,:]*pred[i,:,:,:])
Ior1 = torch.sum(target[i,:,:,:]) + torch.sum(pred[i,:,:,:])-Iand1
IoU1 = Iand1/Ior1
#IoU loss is (1-IoU1)
IoU = IoU + (1-IoU1)
return IoU/b
#检测iou
def bb_intersection_over_union(boxA, boxB):
# determine the (x, y)-coordinates of the intersection rectangle
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
# compute the area of intersection rectangle
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
# compute the area of both the prediction and ground-truth
# rectangles
boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
# compute the intersection over union by taking the intersection
# area and dividing it by the sum of prediction + ground-truth
# areas - the interesection area
iou = interArea / float(boxAArea + boxBArea - interArea)
# return the intersection over union value
return iou#coding=utf-8
import numpy as np
def py_cpu_nms(dets, thresh):
"""Pure Python NMS baseline."""
# tl_x,tl_y,br_x,br_y及score
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
#计算每个检测框的面积，并对目标检测得分进行降序排序
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = [] #保留框的结果集合
while order.size > 0:
i = order[0]
keep.append(i) #保留该类剩余box中得分最高的一个
# 计算最高得分矩形框与剩余矩形框的相交区域
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
#计算相交的面积,不重叠时面积为0
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
#计算IoU：重叠面积 /（面积1+面积2-重叠面积）
ovr = inter / (areas[i] + areas[order[1:]] - inter)
#保留IoU小于阈值的box
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1] #注意这里索引加了1,因为ovr数组的长度比order数组的长度少一个
return keep
if __name__ == '__main__':
dets = np.array([[100,120,170,200,0.98],
[20,40,80,90,0.99],
[20,38,82,88,0.96],
[200,380,282,488,0.9],
[19,38,75,91, 0.8]])
py_cpu_nms(dets, 0.5)#-------------------------------------#
# 调用摄像头检测
#-------------------------------------#
from ssd import SSD
from PIL import Image
import numpy as np
import cv2
import time
ssd = SSD()
# 调用摄像头
capture=cv2.VideoCapture(0) # capture=cv2.VideoCapture("1.mp4")
fps = 0.0
while(True):
t1 = time.time()
# 读取某一帧
ref,frame=capture.read()
# 格式转变，BGRtoRGB
frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
# 转变成Image
frame = Image.fromarray(np.uint8(frame))
# 进行检测
frame = np.array(ssd.detect_image(frame))
# RGBtoBGR满足opencv显示格式
frame = cv2.cvtColor(frame,cv2.COLOR_RGB2BGR)
#不求平均版
#fps = 1./(time.time()-t1)
#求平均
fps = ( fps + (1./(time.time()-t1)) ) / 2
print("fps= %.2f"%(fps))
frame = cv2.putText(frame, "fps= %.2f"%(fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.imshow("video",frame)
c= cv2.waitKey(30) & 0xff
if c==27:
capture.release()
break