def get_color_medio(self, roi, a,b,imprimir = False):
xl,yl,ch = roi.shape
roiyuv = cv2.cvtColor(roi,cv2.COLOR_RGB2YUV)
roihsv = cv2.cvtColor(roi,cv2.COLOR_RGB2HSV)
h,s,v=cv2.split(roihsv)
mask=(h<5)
h[mask]=200
roihsv = cv2.merge((h,s,v))
std = np.std(roiyuv.reshape(xl*yl,3),axis=0)
media = np.mean(roihsv.reshape(xl*yl,3), axis=0)-60
mediayuv = np.mean(roiyuv.reshape(xl*yl,3), axis=0)
if std[0]<12 and std[1]<12 and std[2]<12:
#if (std[0]<15 and std[2]<15) or ((media[0]>100 or media[0]<25) and (std[0]>10)):
media = np.mean(roihsv.reshape(xl*yl,3), axis=0)
# el amarillo tiene 65 de saturacion y sobre 200
if media[1]<60: #and (abs(media[0]-30)>10):
# blanco
return [-10,0,0]
else:
return media
else:
return None
python类COLOR_RGB2YUV的实例源码
def yuvPassShadowRemoval(src, shadowThreshold):
height, width = src.shape[:2]
imgYUV = cv2.cvtColor(src, cv2.COLOR_RGB2YUV)
yImg, uImg, vImg = cv2.split(imgYUV)
# for i in range(0, height):
# for j in range(0, width):
# yImg[i, j] = 0
yImg = np.zeros((height, width, 1), np.uint8)
imgYUV = cv2.merge([yImg, uImg, vImg])
rgbImg = cv2.cvtColor(imgYUV, cv2.COLOR_YUV2RGB)
rImg, gImg, bImg = cv2.split(rgbImg)
count = width * height
avg = np.sum(bImg)
avg /= count * 1.0
# for i in range(0, height):
# for j in range(0, width):
# if bImg[i, j] > ave:
# rImg[i, j] = 255
# gImg[i, j] = 255
# bImg[i, j] = 255
# else:
# rImg[i, j] = 0
# gImg[i, j] = 0
# bImg[i, j] = 0
if shadowThreshold is None:
avg = avg
else:
avg = shadowThreshold
np.where(bImg > avg, 255, 0)
_, threshold = cv2.threshold(bImg, avg, 255, cv2.THRESH_BINARY)
output = threshold
return output
def rgb2yuv(image):
"""
Convert the image from RGB to YUV (This is what the NVIDIA model does)
"""
return cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
def rgb2yuv(image):
"""
Convert the image from RGB to YUV (This is what the NVIDIA model does)
"""
return cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
def load_image(path):
img = skimage.io.imread(path)
yuv = cv2.cvtColor(np.float32(img), cv2.COLOR_RGB2YUV)
img = img - vgg19.VGG_MEAN
img = img[:,:,(2,1,0)] # rgb to bgr
return img[np.newaxis, :, :, :], yuv
def save_image(img, path, content_yuv=None):
img = np.squeeze(img)
img = img[:,:,(2,1,0)] # bgr to rgb
img = img + vgg19.VGG_MEAN
if content_yuv is not None:
yuv = cv2.cvtColor(np.float32(img), cv2.COLOR_RGB2YUV)
yuv[:,:,1:3] = content_yuv[:,:,1:3]
img = cv2.cvtColor(yuv, cv2.COLOR_YUV2RGB)
img = np.clip(img, 0, 255).astype(np.uint8)
skimage.io.imsave(path, img)
def predict_steering(self, data):
image_array = self.roi(cv2.cvtColor(data['image'], cv2.COLOR_RGB2YUV))
transformed_image_array = image_array[None, :, :, :]
return float(model.predict(transformed_image_array, batch_size=1))
# Callback functions triggered by ControlServer
def preprocess_input(self, img):
return self.roi(cv2.cvtColor(img, cv2.COLOR_RGB2YUV))
def preprocess_input(img):
return roi(cv2.cvtColor(img, cv2.COLOR_RGB2YUV))
def preprocess_image(image):
image = cv2.resize(image, (0,0), fx=fx, fy=fy)
image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
# Normalize
image = (image - 128.)/255.
return image
Traffic_Sign_Classifier.py 文件源码
项目：CarND-Traffic-Sign-Classifier-P2
作者: tomaszkacmajor
项目源码
文件源码
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def convert2YUV(img):
return cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
def extract_features(imgs, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
# Create a list to append feature vectors to
features = []
# Iterate through the list of images
for file in imgs:
file_features = []
# Read in each one by one
image = mpimg.imread(file)
# apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(image)
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
file_features.append(spatial_features)
if hist_feat == True:
# Apply color_hist()
hist_features = color_hist(feature_image, nbins=hist_bins)
file_features.append(hist_features)
if hog_feat == True:
# Call get_hog_features() with vis=False, feature_vec=True
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.append(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
hog_features = np.ravel(hog_features)
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
# Append the new feature vector to the features list
file_features.append(hog_features)
features.append(np.concatenate(file_features))
# Return list of feature vectors
return features
# Define a function that takes an image,
# start and stop positions in both x and y,
# window size (x and y dimensions),
# and overlap fraction (for both x and y)
def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, hist_range=(0, 256), orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
img_features = []
# apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
img_features.append(spatial_features)
if hist_feat == True:
# Apply color_hist()
hist_features = color_hist(feature_image, nbins=hist_bins,
bins_range=hist_range)
img_features.append(hist_features)
if hog_feat == True:
# Call get_hog_features() with vis=False, feature_vec=True
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.extend(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
# Append the new feature vector to the features list
img_features.append(hog_features)
# Return list of feature vectors
return np.concatenate(img_features)
# Convert windows to heatmap numpy array.
