极客教程简单物体检测第二步——滑动窗口（Sliding Window）+ NN
对于imorimany.jpg，将Opencv 滑动窗口+HOG 中求得的各个矩形的HOG特征值输入Opencv Training 中训练好的神经网络中进行蝾螈头部识别。
在此，绘制\text{Score}（即预测是否是蝾螈头部图像的概率）大于0.7的矩形。
下面的答案内容为检测矩形的[x1, y1, x2, y2, \text{Score}]：
[[ 27. 0. 69. 21. 0.74268049]
[ 31. 0. 73. 21. 0.89631011]
[ 52. 0. 108. 36. 0.84373157]
[165. 0. 235. 43. 0.73741703]
[ 55. 0. 97. 33. 0.70987278]
[165. 0. 235. 47. 0.92333214]
[169. 0. 239. 47. 0.84030839]
[ 51. 0. 93. 37. 0.84301022]
[168. 0. 224. 44. 0.79237294]
[165. 0. 235. 51. 0.86038564]
[ 51. 0. 93. 41. 0.85151915]
[ 48. 0. 104. 56. 0.73268318]
[168. 0. 224. 56. 0.86675902]
[ 43. 15. 85. 57. 0.93562483]
[ 13. 37. 83. 107. 0.77192307]
[180. 44. 236. 100. 0.82054873]
[173. 37. 243. 107. 0.8478805 ]
[177. 37. 247. 107. 0.87183443]
[ 24. 68. 80. 124. 0.7279032 ]
[103. 75. 145. 117. 0.73725153]
[104. 68. 160. 124. 0.71314282]
[ 96. 72. 152. 128. 0.86269195]
[100. 72. 156. 128. 0.98826957]
[ 25. 69. 95. 139. 0.73449174]
[100. 76. 156. 132. 0.74963093]
[104. 76. 160. 132. 0.96620193]
[ 75. 91. 117. 133. 0.80533424]
[ 97. 77. 167. 144. 0.7852362 ]
[ 97. 81. 167. 144. 0.70371708]]
| 输入 (imori_many.jpg) | 输出 |
|---|---|
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python实现：
import cv2
import numpy as np
np.random.seed(0)
# read image
img = cv2.imread("imori_1.jpg")
H, W, C = img.shape
# Grayscale
gray = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0]
gt = np.array((47, 41, 129, 103), dtype=np.float32)
cv2.rectangle(img, (gt[0], gt[1]), (gt[2], gt[3]), (0,255,255), 1)
def iou(a, b):
area_a = (a[2] - a[0]) * (a[3] - a[1])
area_b = (b[2] - b[0]) * (b[3] - b[1])
iou_x1 = np.maximum(a[0], b[0])
iou_y1 = np.maximum(a[1], b[1])
iou_x2 = np.minimum(a[2], b[2])
iou_y2 = np.minimum(a[3], b[3])
iou_w = max(iou_x2 - iou_x1, 0)
iou_h = max(iou_y2 - iou_y1, 0)
area_iou = iou_w * iou_h
iou = area_iou / (area_a + area_b - area_iou)
return iou
def hog(gray):
h, w = gray.shape
# Magnitude and gradient
gray = np.pad(gray, (1, 1), 'edge')
gx = gray[1:h+1, 2:] - gray[1:h+1, :w]
gy = gray[2:, 1:w+1] - gray[:h, 1:w+1]
gx[gx == 0] = 0.000001
mag = np.sqrt(gx ** 2 + gy ** 2)
gra = np.arctan(gy / gx)
gra[gra<0] = np.pi / 2 + gra[gra < 0] + np.pi / 2
# Gradient histogram
gra_n = np.zeros_like(gra, dtype=np.int)
d = np.pi / 9
for i in range(9):
gra_n[np.where((gra >= d * i) & (gra <= d * (i+1)))] = i
N = 8
HH = h // N
HW = w // N
Hist = np.zeros((HH, HW, 9), dtype=np.float32)
for y in range(HH):
for x in range(HW):
for j in range(N):
for i in range(N):
Hist[y, x, gra_n[y*4+j, x*4+i]] += mag[y*4+j, x*4+i]
## Normalization
C = 3
eps = 1
for y in range(HH):
for x in range(HW):
#for i in range(9):
Hist[y, x] /= np.sqrt(np.sum(Hist[max(y-1,0):min(y+2, HH), max(x-1,0):min(x+2, HW)] ** 2) + eps)
return Hist
def resize(img, h, w):
_h, _w = img.shape
ah = 1. * h / _h
aw = 1. * w / _w
y = np.arange(h).repeat(w).reshape(w, -1)
x = np.tile(np.arange(w), (h, 1))
y = (y / ah)
x = (x / aw)
ix = np.floor(x).astype(np.int32)
iy = np.floor(y).astype(np.int32)
ix = np.minimum(ix, _w-2)
iy = np.minimum(iy, _h-2)
dx = x - ix
dy = y - iy
out = (1-dx) * (1-dy) * img[iy, ix] + dx * (1 - dy) * img[iy, ix+1] + (1 - dx) * dy * img[iy+1, ix] + dx * dy * img[iy+1, ix+1]
out[out>255] = 255
return out
class NN:
def __init__(self, ind=2, w=64, w2=64, outd=1, lr=0.1):
self.w1 = np.random.normal(0, 1, [ind, w])
self.b1 = np.random.normal(0, 1, [w])
self.w2 = np.random.normal(0, 1, [w, w2])
self.b2 = np.random.normal(0, 1, [w2])
self.wout = np.random.normal(0, 1, [w2, outd])
self.bout = np.random.normal(0, 1, [outd])
self.lr = lr
def forward(self, x):
self.z1 = x
self.z2 = sigmoid(np.dot(self.z1, self.w1) + self.b1)
self.z3 = sigmoid(np.dot(self.z2, self.w2) + self.b2)
self.out = sigmoid(np.dot(self.z3, self.wout) + self.bout)
return self.out
def train(self, x, t):
# backpropagation output layer
#En = t * np.log(self.out) + (1-t) * np.log(1-self.out)
En = (self.out - t) * self.out * (1 - self.out)
grad_wout = np.dot(self.z3.T, En)
grad_bout = np.dot(np.ones([En.shape[0]]), En)
self.wout -= self.lr * grad_wout
self.bout -= self.lr * grad_bout
# backpropagation inter layer
grad_u2 = np.dot(En, self.wout.T) * self.z3 * (1 - self.z3)
grad_w2 = np.dot(self.z2.T, grad_u2)
grad_b2 = np.dot(np.ones([grad_u2.shape[0]]), grad_u2)
self.w2 -= self.lr * grad_w2
self.b2 -= self.lr * grad_b2
grad_u1 = np.dot(grad_u2, self.w2.T) * self.z2 * (1 - self.z2)
grad_w1 = np.dot(self.z1.T, grad_u1)
grad_b1 = np.dot(np.ones([grad_u1.shape[0]]), grad_u1)
self.w1 -= self.lr * grad_w1
self.b1 -= self.lr * grad_b1
def sigmoid(x):
return 1. / (1. + np.exp(-x))
# crop and create database
Crop_num = 200
L = 60
H_size = 32
F_n = ((H_size // 8) ** 2) * 9
db = np.zeros((Crop_num, F_n+1))
for i in range(Crop_num):
x1 = np.random.randint(W-L)
y1 = np.random.randint(H-L)
x2 = x1 + L
y2 = y1 + L
crop = np.array((x1, y1, x2, y2))
_iou = iou(gt, crop)
if _iou >= 0.5:
cv2.rectangle(img, (x1, y1), (x2, y2), (0,0,255), 1)
label = 1
else:
cv2.rectangle(img, (x1, y1), (x2, y2), (255,0,0), 1)
label = 0
crop_area = gray[y1:y2, x1:x2]
crop_area = resize(crop_area, H_size, H_size)
_hog = hog(crop_area)
db[i, :F_n] = _hog.ravel()
db[i, -1] = label
## train neural network
nn = NN(ind=F_n, lr=0.01)
for i in range(10000):
nn.forward(db[:, :F_n])
nn.train(db[:, :F_n], db[:, -1][..., None])
# read detect target image
img2 = cv2.imread("imori_many.jpg")
H2, W2, C2 = img2.shape
# Grayscale
gray2 = 0.2126 * img2[..., 2] + 0.7152 * img2[..., 1] + 0.0722 * img2[..., 0]
# [h, w]
recs = np.array(((42, 42), (56, 56), (70, 70)), dtype=np.float32)
detects = np.ndarray((0, 5), dtype=np.float32)
# sliding window
for y in range(0, H2, 4):
for x in range(0, W2, 4):
for rec in recs:
dh = int(rec[0] // 2)
dw = int(rec[1] // 2)
x1 = max(x-dw, 0)
x2 = min(x+dw, W2)
y1 = max(y-dh, 0)
y2 = min(y+dh, H2)
region = gray2[max(y-dh,0):min(y+dh,H2), max(x-dw,0):min(x+dw,W2)]
region = resize(region, H_size, H_size)
region_hog = hog(region).ravel()
score = nn.forward(region_hog)
if score >= 0.7:
cv2.rectangle(img2, (x1, y1), (x2, y2), (0,0,255), 1)
detects = np.vstack((detects, np.array((x1, y1, x2, y2, score))))
print(detects)
cv2.imwrite("out.jpg", img2)
cv2.imshow("result", img2)
cv2.waitKey(0)