def sliding_window(cls, pview):
#Perspective View coordinates
x1, y1, x2, y2 = pview
#Initial height,width & aspect ratio of perspective view
iheight = y2 - y1
iwidth = x2 - x1
aspect_ratio = iwidth / iheight
#Resized height,width
nheight = 64
nwidth = int(nheight * aspect_ratio)
#Resized Perspective view
pmg = cv2.resize(cls.image[y1:y2, x1:x2, :], (nwidth, nheight))
#Hog features of the entire perspective view
hog_features = FeatureExtractor.extract_hog_features(pmg)
#Sliding window on perspective view with a sliding of 8 pixel increments
#Slide size is 8 pixels is to be able to subsample the hog features as it has 8 pixels per block
img_block_size = 64
slide_size = 8
hog_block_size = 8
xend = nwidth - img_block_size + 1
for idx, vx in enumerate(range(0, xend, slide_size)):
#Select the sub-image specified by the sliding window
smg = pmg[:, vx:vx + img_block_size]
#Get the features for the current sliding window
sfeatures = FeatureExtractor.extract_spatial_features(smg)
hfeatures = FeatureExtractor.extract_histogram_features(smg)
gfeatures0 = hog_features[0][:, idx:idx + hog_block_size].ravel()
gfeatures1 = hog_features[1][:, idx:idx + hog_block_size].ravel()
gfeatures2 = hog_features[2][:, idx:idx + hog_block_size].ravel()
features = np.concatenate(
(sfeatures, hfeatures, gfeatures0, gfeatures1, gfeatures2))
pred = cls.model.predict(features)
#bmg=cv2.cvtColor(smg,cv2.COLOR_HSV2BGR)
#cv2.imwrite("genimgs/img" + str(random.randint(0,1000)) + ".png" ,bmg)
if (pred == 1):
#Calculating the window coordinates in main image basing on the resized image coordinates
rx1 = int(vx * (iwidth / nwidth)) + x1
ry1 = y1
rx2 = rx1 + iheight
ry2 = ry1 + iheight
cls.hot_windows.append(((rx1, ry1), (rx2, ry2)))
#cv2.rectangle(cls.frame_image,(rx1,ry1),(rx2,ry2), (0,255,0), 1)
if (vx > xend - 32):
cls.hot_windows.append(((rx1, ry1), (rx2, ry2)))
def get_features_labels(cls):
v_features = []
n_features = []
t1=time.time()
for img in cls.v_images:
sfeatures = FE.extract_spatial_features(img)
hfeatures = FE.extract_histogram_features(img)
gfeatures = FE.extract_hog_features(img)
features=np.concatenate( (sfeatures,hfeatures,gfeatures[0].ravel(),gfeatures[1].ravel(),gfeatures[2].ravel()) )
v_features.append(features)
for img in cls.n_images:
sfeatures = FE.extract_spatial_features(img)
hfeatures = FE.extract_histogram_features(img)
gfeatures = FE.extract_hog_features(img)
features=np.concatenate( (sfeatures,hfeatures,gfeatures[0].ravel(),gfeatures[1].ravel(),gfeatures[2].ravel()) )
n_features.append(features)
cls.features = np.vstack((v_features, n_features)).astype(np.float64)
cls.labels = np.hstack((np.ones(len(v_features)), np.zeros(len(n_features))))
t2=time.time()
print("Feature & Label time - " ,round(t2-t1, 2) )
