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Question_91_100/answers/answer_98.py

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+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)