def optical_flow(one, two):
"""
method taken from (https://chatbotslife.com/autonomous-vehicle-speed-estimation-from-dashboard-cam-ca96c24120e4)
"""
one_g = cv2.cvtColor(one, cv2.COLOR_RGB2GRAY)
two_g = cv2.cvtColor(two, cv2.COLOR_RGB2GRAY)
hsv = np.zeros((120, 320, 3))
# set saturation
hsv[:,:,1] = cv2.cvtColor(two, cv2.COLOR_RGB2HSV)[:,:,1]
# obtain dense optical flow paramters
flow = cv2.calcOpticalFlowFarneback(one_g, two_g, flow=None,
pyr_scale=0.5, levels=1, winsize=15,
iterations=2,
poly_n=5, poly_sigma=1.1, flags=0)
# convert from cartesian to polar
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
# hue corresponds to direction
hsv[:,:,0] = ang * (180/ np.pi / 2)
# value corresponds to magnitude
hsv[:,:,2] = cv2.normalize(mag,None,0,255,cv2.NORM_MINMAX)
# convert HSV to int32's
hsv = np.asarray(hsv, dtype= np.float32)
rgb_flow = cv2.cvtColor(hsv,cv2.COLOR_HSV2RGB)
return rgb_flow
python类COLOR_RGB2GRAY的实例源码
def img_process(im, landmark, print_img=False):
"""
Image processing, rotate, resize, and crop the face image.
Args:
im: numpy array, Original image
landmark: 5 landmark points
Return:
Crop face region
"""
if landmark is None:
im_rez = cv2.resize(im, (cfg.crop_size, cfg.crop_size))
return im_rez
im_rot, ang, r_landmark = im_rotate(im, landmark)
im_rez, resize_scale, rez_landmark = im_resize(im_rot, r_landmark, ang)
crop = im_crop(im_rez, rez_landmark, resize_scale)
if cfg.forcegray == True:
crop = cv2.cvtColor(crop, cv2.COLOR_RGB2GRAY)
# print('Shapes' + str(im_rot.shape) + str(im_rez.shape) + str(crop.shape))
# return im_rot, im_rez, crop, (crop.astype(np.float) - cfg.PIXEL_MEANS) / cfg.scale
if print_img:
return im, im_rot, im_rez, crop
return crop
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the x and y gradients
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
absgraddir = np.arctan2(abs_sobely, abs_sobelx)
# 5) Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
# Define a function that applies Sobel x and y,
# then computes the magnitude of the gradient
# and applies a threshold
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Calculate the magnitude
gradmag = np.sqrt(sobelx**2 + sobely**2)
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# 5) Create a binary mask where mag thresholds are met
binary_output = np.zeros_like(gradmag)
binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
# Define a function that applies Sobel x or y,
# then takes an absolute value and applies a threshold.
# Note: calling your function with orient='x', thresh_min=5, thresh_max=100
# should produce output like the example image shown above this quiz.
def abs_sobel_thresh(img, orient='x', thresh_min=0, thresh_max=255):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the derivative in x or y given orient = 'x' or 'y'
if orient == 'x':
sobel = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
if orient == 'y':
sobel = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
# 3) Take the absolute value of the derivative or gradient
abs_sobel = np.absolute(sobel)
# 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# 5) Create a mask of 1's where the scaled gradient magnitude
# is > thresh_min and < thresh_max
binary_output = np.zeros_like(scaled_sobel)
binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# 6) Return this mask as your binary_output image
return binary_output
def detect_edges(images):
def blur(image):
return cv2.GaussianBlur(image, (5, 5), 0)
def canny_otsu(image):
scale_factor = 255
scaled_image = np.uint8(image * scale_factor)
otsu_threshold = cv2.threshold(
cv2.cvtColor(scaled_image, cv2.COLOR_RGB2GRAY), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[0]
lower_threshold = max(0, int(otsu_threshold * 0.5))
upper_threshold = min(255, int(otsu_threshold))
edges = cv2.Canny(scaled_image, lower_threshold, upper_threshold)
edges = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
return np.float32(edges) * (1 / scale_factor)
blurred = [blur(image) for image in images]
canny_applied = [canny_otsu(image) for image in blurred]
return canny_applied
def find_faces(self, image, draw_box=False):
"""Uses a haarcascade to detect faces inside an image.
Args:
image: The image.
draw_box: If True, the image will be marked with a rectangle.
Return:
The faces as returned by OpenCV's detectMultiScale method for
cascades.
"""
frame_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
faces = self.cascade.detectMultiScale(
frame_gray,
scaleFactor=1.3,
minNeighbors=5,
minSize=(50, 50),
flags=0)
if draw_box:
for x, y, w, h in faces:
cv2.rectangle(image, (x, y),
(x + w, y + h), (0, 255, 0), 2)
return faces
def cropCircle(img, resize=None):
if resize:
if (img.shape[0] > img.shape[1]):
tile_size = (int(img.shape[1] * resize / img.shape[0]), resize)
else:
tile_size = (resize, int(img.shape[0] * resize / img.shape[1]))
img = cv2.resize(img, dsize=tile_size, interpolation=cv2.INTER_CUBIC)
else:
tile_size = img.shape
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY);
_, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
_, contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
main_contour = sorted(contours, key=cv2.contourArea, reverse=True)[0]
ff = np.zeros((gray.shape[0], gray.shape[1]), 'uint8')
cv2.drawContours(ff, main_contour, -1, 1, 15)
ff_mask = np.zeros((gray.shape[0] + 2, gray.shape[1] + 2), 'uint8')
cv2.floodFill(ff, ff_mask, (int(gray.shape[1] / 2), int(gray.shape[0] / 2)), 1)
rect = maxRect(ff)
rectangle = [min(rect[0], rect[2]), max(rect[0], rect[2]), min(rect[1], rect[3]), max(rect[1], rect[3])]
img_crop = img[rectangle[0]:rectangle[1], rectangle[2]:rectangle[3]]
cv2.rectangle(ff, (min(rect[1], rect[3]), min(rect[0], rect[2])), (max(rect[1], rect[3]), max(rect[0], rect[2])), 3,
2)
return [img_crop, rectangle, tile_size]
def evaluate(img_col, args):
numpy.seterr(all='ignore')
assert isinstance(img_col, numpy.ndarray), 'img_col must be a numpy array'
assert img_col.ndim == 3, 'img_col must be a color image ({0} dimensions currently)'.format(img_col.ndim)
assert isinstance(args, argparse.Namespace), 'args must be of type argparse.Namespace not {0}'.format(type(args))
img_gry = cv2.cvtColor(img_col, cv2.COLOR_RGB2GRAY)
rows, cols = img_gry.shape
crow, ccol = rows/2, cols/2
f = numpy.fft.fft2(img_gry)
fshift = numpy.fft.fftshift(f)
fshift[crow-75:crow+75, ccol-75:ccol+75] = 0
f_ishift = numpy.fft.ifftshift(fshift)
img_fft = numpy.fft.ifft2(f_ishift)
img_fft = 20*numpy.log(numpy.abs(img_fft))
if args.display and not args.testing:
cv2.destroyAllWindows()
scripts.display('img_fft', img_fft)
scripts.display('img_col', img_col)
cv2.waitKey(0)
result = numpy.mean(img_fft)
return img_fft, result, result < args.thresh
def deal_image(self , image):
# index = len(self.obs_origin)
# image_end = []
# if index < pms.history_number:
# image_end = self.obs_origin[0:index]
# for i in range(pms.history_number - index):
# image_end.append(image)
# else:
# image_end = self.obs_origin[index - pms.history_number:index]
#
# image_end = np.concatenate(image_end)
# # image_end = image_end.reshape((pms.obs_height, pms.obs_width, pms.history_number))
# obs = cv2.resize(cv2.cvtColor(image_end , cv2.COLOR_RGB2GRAY) / 255. , (pms.obs_height , pms.obs_width))
obs = cv2.resize(image, (pms.obs_height, pms.obs_width))
# obs = np.transpose(np.array(obs), (2, 0, 1))
return obs
def image_preprocess(obs, resize_width, resize_height, to_gray):
"""Applies basic preprocessing for image observations.
Args:
obs (numpy.ndarray): 2-D or 3-D uint8 type image.
resize_width (int): Resize width. To disable resize, pass None.
resize_height (int): Resize height. To disable resize, pass None.
to_gray (bool): Converts image to grayscale.
Returns (numpy.ndarray):
Processed 3-D float type image.
"""
processed_obs = np.squeeze(obs)
if to_gray:
processed_obs = cv2.cvtColor(processed_obs, cv2.COLOR_RGB2GRAY)
if resize_height and resize_width:
processed_obs = cv2.resize(processed_obs, (resize_height, resize_width))
if np.ndim(processed_obs) == 2:
processed_obs = np.expand_dims(processed_obs, 2)
return processed_obs
def step(self, action):
""" Perform an action and observe the resulting state.
Args:
action (int): Selected action ID to perform in envirnoment.
Returns:
reward (float): The change of score after performing the action.
"""
_logger.debug("Getting index of action %s" % (str(action)))
action = np.where(self.avail_actions == action)[0][0]
self.current_frame, reward, self.terminal_state, info = self.gym.step(action)
self.current_frame = cv2.resize(cv2.cvtColor(self.current_frame, cv2.COLOR_RGB2GRAY), self.frame_dims)
if self.counts_lives:
self.just_lost_live = self.has_just_lost_live()
return reward
def to_grayscale(image):
"""Converts a colored image to a grayscaled one.
Parameters
----------
image: ndarray()
An image with the shape of [height, width, 3].
Returns
---------
image: ndarray(uint8)
Returns a grayscaled image with shape [height, width, 1].
"""
img_channels = np.shape(image)[2]
if img_channels == 3:
image = cast(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
image = np.expand_dims(image, axis=2)
# introduce a 1-channel dimension to handle the indexing
# of color and gray images the same way
return image
def embed_state(self, previous_states, state, volatile=False, requires_grad=True, gpu=-1):
prev_scrs = [self.downscale_prev(s.screenshot_rs) for s in previous_states]
prev_scrs_y = [cv2.cvtColor(scr, cv2.COLOR_RGB2GRAY) for scr in prev_scrs]
#inputs = np.dstack([self.downscale(state.screenshot_rs)] + list(reversed(prev_scrs_y)))
inputs = np.array(self.downscale(state.screenshot_rs), dtype=np.float32)
inputs = inputs / 255.0
inputs = inputs.transpose((2, 0, 1))
inputs = inputs[np.newaxis, ...]
inputs = to_cuda(to_variable(inputs, volatile=volatile, requires_grad=requires_grad), gpu)
inputs_prev = np.dstack(prev_scrs_y)
inputs_prev = inputs_prev.astype(np.float32) / 255.0
inputs_prev = inputs_prev.transpose((2, 0, 1))
inputs_prev = inputs_prev[np.newaxis, ...]
inputs_prev = to_cuda(to_variable(inputs_prev, volatile=volatile, requires_grad=requires_grad), gpu)
return self.embed(inputs, inputs_prev)
def embed_state(self, previous_states, state, volatile=False, requires_grad=True, gpu=-1):
prev_scrs = [self.downscale_prev(s.screenshot_rs) for s in previous_states]
prev_scrs_y = [cv2.cvtColor(scr, cv2.COLOR_RGB2GRAY) for scr in prev_scrs]
#inputs = np.dstack([self.downscale(state.screenshot_rs)] + list(reversed(prev_scrs_y)))
inputs = np.array(self.downscale(state.screenshot_rs), dtype=np.float32)
inputs = inputs / 255.0
inputs = inputs.transpose((2, 0, 1))
inputs = inputs[np.newaxis, ...]
inputs = to_cuda(to_variable(inputs, volatile=volatile, requires_grad=requires_grad), gpu)
inputs_prev = np.dstack(prev_scrs_y)
inputs_prev = inputs_prev.astype(np.float32) / 255.0
inputs_prev = inputs_prev.transpose((2, 0, 1))
inputs_prev = inputs_prev[np.newaxis, ...]
inputs_prev = to_cuda(to_variable(inputs_prev, volatile=volatile, requires_grad=requires_grad), gpu)
return self.embed(inputs, inputs_prev)
def binary_extraction(self,image, ksize=3):
# undistort first
#image = self.undistort(image)
color_bin = self.color_thresh(image,thresh=(90, 150)) # initial values 110, 255
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize)
gradx = self.abs_sobel_thresh(sobelx, thresh=(100, 190)) # initial values 40, 160
grady = self.abs_sobel_thresh(sobely, thresh=(100, 190)) # initial values 40, 160
mag_binary = self.mag_thresh(sobelx, sobely, mag_thresh=(100, 190)) # initial values 40, 160
#dir_binary = self.dir_threshold(sobelx, sobely, thresh=(0.7, 1.3))
combined = np.zeros_like(gradx)
#combined[(((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))) | (color_bin==1) ] = 1
combined[(((gradx == 1) & (grady == 1)) | (mag_binary == 1)) | (color_bin==1) ] = 1
#combined[(((gradx == 1) & (grady == 1)) | (mag_binary == 1)) ] = 1
return combined
# transform perspective
def get_contour(self, arg_frame, arg_export_index, arg_export_path, arg_export_filename, arg_binaryMethod):
# Otsu's thresholding after Gaussian filtering
tmp = cv2.cvtColor(arg_frame, cv2.COLOR_RGB2GRAY)
blur = cv2.GaussianBlur(tmp,(5,5),0)
if arg_binaryMethod== 0:
ret, thresholdedImg= cv2.threshold(blur.copy() , self.threshold_graylevel, 255 , 0)
elif arg_binaryMethod == 1:
ret,thresholdedImg = cv2.threshold(blur.copy(),0 ,255 ,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
elif arg_binaryMethod== 2:
thresholdedImg = cv2.adaptiveThreshold(blur.copy(),255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,5,0)
result = cv2.cvtColor(thresholdedImg, cv2.COLOR_GRAY2RGB)
ctrs, hier = cv2.findContours(thresholdedImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
ctrs = filter(lambda x : cv2.contourArea(x) > self.threshold_size , ctrs)
rects = [[cv2.boundingRect(ctr) , ctr] for ctr in ctrs]
for rect , cntr in rects:
cv2.drawContours(result, [cntr], 0, (0, 128, 255), 3)
if arg_export_index:
cv2.imwrite(arg_export_path+ arg_export_filename+'.jpg', result)
print "Get Contour success"
return result
def find_lines(img, acc_threshold=0.25, should_erode=True):
if len(img.shape) == 3 and img.shape[2] == 3: # if it's color
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img = cv2.GaussianBlur(img, (11, 11), 0)
img = cv2.adaptiveThreshold(
img,
255,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY,
5,
2)
img = cv2.bitwise_not(img)
# thresh = 127
# edges = cv2.threshold(img, thresh, 255, cv2.THRESH_BINARY)[1]
# edges = cv2.Canny(blur, 500, 500, apertureSize=3)
if should_erode:
element = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 4))
img = cv2.erode(img, element)
theta = np.pi/2000
angle_threshold = 2
horizontal = cv2.HoughLines(
img,
1,
theta,
int(acc_threshold * img.shape[1]),
min_theta=np.radians(90 - angle_threshold),
max_theta=np.radians(90 + angle_threshold))
vertical = cv2.HoughLines(
img,
1,
theta,
int(acc_threshold * img.shape[0]),
min_theta=np.radians(-angle_threshold),
max_theta=np.radians(angle_threshold),
)
horizontal = list(horizontal) if horizontal is not None else []
vertical = list(vertical) if vertical is not None else []
horizontal = [line[0] for line in horizontal]
vertical = [line[0] for line in vertical]
horizontal = np.asarray(horizontal)
vertical = np.asarray(vertical)
return horizontal, vertical
def abs_sobel_thresh(img, orient='x', thresh_min=0, thresh_max=255):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# Return the result
return binary_output
def classify_image(image):
"""Call ResXception to return emotion probabilities
from Keras model.
@param image: RGB cropped face image
@return: map from emotion to probability
"""
nn_input_shape = nn.input_shape[1:3]
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
gray_image = cv2.resize(gray_image, (nn_input_shape))
gray_image = clean_image(gray_image)
gray_image = np.expand_dims(gray_image, 0)
gray_image = np.expand_dims(gray_image, -1)
with graph.as_default():
emotion_proba = nn.predict(gray_image)
emotion_map = index_to_emotion(emotion_proba[0].tolist())
return emotion_map
def cropCircle(img):
'''
there many imaged taken thresholded, which means many images is
present as a circle with black surrounded. This function is to
find the largest inscribed rectangle to the thresholed image and
then crop the image to the rectangle.
input: img - the cv2 module
return: img_crop, rectangle, tile_size
'''
if(img.shape[0] > img.shape[1]):
tile_size = (int(img.shape[1]*256/img.shape[0]),256)
else:
tile_size = (256, int(img.shape[0]*256/img.shape[1]))
img = cv2.resize(img, dsize=tile_size)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY);
_, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
_, contours, _ = cv2.findContours(thresh.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
main_contour = sorted(contours, key = cv2.contourArea, reverse = True)[0]
ff = np.zeros((gray.shape[0],gray.shape[1]), 'uint8')
cv2.drawContours(ff, main_contour, -1, 1, 15)
ff_mask = np.zeros((gray.shape[0]+2,gray.shape[1]+2), 'uint8')
cv2.floodFill(ff, ff_mask, (int(gray.shape[1]/2), int(gray.shape[0]/2)), 1)
rect = maxRect(ff)
rectangle = [min(rect[0],rect[2]), max(rect[0],rect[2]), min(rect[1],rect[3]), max(rect[1],rect[3])]
img_crop = img[rectangle[0]:rectangle[1], rectangle[2]:rectangle[3]]
cv2.rectangle(ff,(min(rect[1],rect[3]),min(rect[0],rect[2])),(max(rect[1],rect[3]),max(rect[0],rect[2])),3,2)
return [img_crop, rectangle, tile_size]
def cropCircle(img):
'''
there many imaged taken thresholded, which means many images is
present as a circle with black surrounded. This function is to
find the largest inscribed rectangle to the thresholed image and
then crop the image to the rectangle.
input: img - the cv2 module
return: img_crop, rectangle, tile_size
'''
if(img.shape[0] > img.shape[1]):
tile_size = (int(img.shape[1]*256/img.shape[0]),256)
else:
tile_size = (256, int(img.shape[0]*256/img.shape[1]))
img = cv2.resize(img, dsize=tile_size)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY);
_, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)
_, contours, _ = cv2.findContours(thresh.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
main_contour = sorted(contours, key = cv2.contourArea, reverse = True)[0]
ff = np.zeros((gray.shape[0],gray.shape[1]), 'uint8')
cv2.drawContours(ff, main_contour, -1, 1, 15)
ff_mask = np.zeros((gray.shape[0]+2,gray.shape[1]+2), 'uint8')
cv2.floodFill(ff, ff_mask, (int(gray.shape[1]/2), int(gray.shape[0]/2)), 1)
rect = maxRect(ff)
rectangle = [min(rect[0],rect[2]), max(rect[0],rect[2]), min(rect[1],rect[3]), max(rect[1],rect[3])]
img_crop = img[rectangle[0]:rectangle[1], rectangle[2]:rectangle[3]]
cv2.rectangle(ff,(min(rect[1],rect[3]),min(rect[0],rect[2])),(max(rect[1],rect[3]),max(rect[0],rect[2])),3,2)
return [img_crop, rectangle, tile_size]
def process_image(image):
"""
Args:
image: The image to process
Returns:
sub_image: The rotated and extracted.
"""
# Convert image to black and white - we cannot take the photos in black and white as we
# must first search for the red triangle.
if len(image.shape) == 3:
processed_img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
else:
processed_img = image
if config.real_hardware:
num_iterations = 8
else:
num_iterations = 8
processed_img = cv2.GaussianBlur(processed_img, (21, 21), 0)
_, processed_img = cv2.threshold(processed_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# Put a border around the image to stop the edges of the images creating artifacts.
padded_image = np.zeros((processed_img.shape[0] + 10, processed_img.shape[1] + 10), np.uint8)
padded_image[5:processed_img.shape[0]+5, 5:processed_img.shape[1]+5] = processed_img
kernel = np.array([[0, 1, 1, 1, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[0, 1, 1, 1, 0]], np.uint8)
padded_image = cv2.erode(padded_image, kernel, iterations=num_iterations)
processed_img = padded_image[25:padded_image.shape[0] - 25, 25:padded_image.shape[1] - 25]
#cv2.imshow('Padded Image', padded_image)
#cv2.imshow('Processed image', processed_img)
#cv2.waitKey(0)
# Debugging code - useful to show the images are being eroded correctly.
#spacer = processed_img[:, 0:2].copy()
#spacer.fill(100)
#combined_image = np.concatenate((processed_img, spacer), axis=1)
#combined_image = np.concatenate((combined_image, image), axis=1)
#cv2.imshow('PreProcessed and Processed Image', combined_image)
#cv2.waitKey(0)
# Save sub_image to debug folder if required.
if __debug__:
iadebug.save_processed_image(processed_img)
return processed_img
def render(self,frame):
numDownSamples = 2
img_rgb = frame
# number of downscaling steps
numBilateralFilters = 7
# number of bilateral filtering steps
# -- STEP 1 --
# downsample image using Gaussian pyramid
img_color = img_rgb
for _ in xrange(numDownSamples):
img_color = cv2.pyrDown(img_color)
# repeatedly apply small bilateral filter instead of applying
# one large filter
for _ in xrange(numBilateralFilters):
img_color = cv2.bilateralFilter(img_color, 9, 9, 7)
# upsample image to original size
for _ in xrange(numDownSamples):
img_color = cv2.pyrUp(img_color)
# convert to grayscale and apply median blur
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)
img_blur = cv2.medianBlur(img_gray, 7)
# detect and enhance edges
img_edge = cv2.adaptiveThreshold(img_blur, 255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY, 9, 2)
# -- STEP 5 --
# convert back to color so that it can be bit-ANDed with color image
img_edge = cv2.cvtColor(img_edge, cv2.COLOR_GRAY2RGB)
final = cv2.bitwise_and(img_color, img_edge)
return cv2.medianBlur(final,7)
def render(self,frame):
canvas = cv2.imread("pen.jpg", cv2.CV_8UC1)
numDownSamples = 2
img_rgb = frame
# number of downscaling steps
numBilateralFilters = 3
# number of bilateral filtering steps
# -- STEP 1 --
# downsample image using Gaussian pyramid
img_color = img_rgb
for _ in xrange(numDownSamples):
img_color = cv2.pyrDown(img_color)
# repeatedly apply small bilateral filter instead of applying
# one large filter
for _ in xrange(numBilateralFilters):
img_color = cv2.bilateralFilter(img_color, 9, 9, 3)
# upsample image to original size
for _ in xrange(numDownSamples):
img_color = cv2.pyrUp(img_color)
# convert to grayscale and apply median blur
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)
img_blur = cv2.medianBlur(img_gray, 3)
# detect and enhance edges
img_edge = cv2.adaptiveThreshold(img_blur, 255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY, 9, 2)
return cv2.multiply(cv2.medianBlur(img_edge,7), canvas, scale=1./256)
def run(self):
while not rospy.is_shutdown():
if self.im_data:
im = self.im_data.popleft()
self.im_im.set_data(im)
self.im_fig.canvas.draw()
# Get grayscale image for converting to DVS
gray_im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
diff_im = gray_im.astype(np.int16) - self.prev_im
diff_im[np.where((diff_im < self.tol) & (diff_im > -self.tol))] = 0
diff_im[np.where(diff_im >= self.tol)] = 1
diff_im[np.where(diff_im <= -self.tol)] = -1
self.prev_im = np.copy(gray_im)
self.dv_im.set_data(diff_im)
self.dv_fig.canvas.draw()
atari_wrappers_deprecated.py 文件源码
项目：distributional_perspective_on_RL
作者: Kiwoo
项目源码
文件源码
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def process(frame):
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
frame = cv2.resize(frame, (84, 84), interpolation=cv2.INTER_AREA)
return frame.reshape(84, 84, 1)
def split_input(img):
"""
img: an 512x256x3 image
:return: [input, output]
"""
input, output = img[:,:img_cols,:], img[:,img_cols:,:]
if args.mode == 'BtoA':
input, output = output, input
if IN_CH == 1:
input = cv2.cvtColor(input, cv2.COLOR_RGB2GRAY)
if OUT_CH == 1:
output = cv2.cvtColor(output, cv2.COLOR_RGB2GRAY)
return [input, output]
def optical_flow(one, two):
"""
method taken from https://chatbotslife.com/autonomous-vehicle-speed-estimation-from-dashboard-cam-ca96c24120e4
input: image_current, image_next (RGB images)
calculates optical flow magnitude and angle and places it into HSV image
"""
one_g = cv2.cvtColor(one, cv2.COLOR_RGB2GRAY)
two_g = cv2.cvtColor(two, cv2.COLOR_RGB2GRAY)
hsv = np.zeros((120, 320, 3))
# set saturation
hsv[:,:,1] = cv2.cvtColor(two, cv2.COLOR_RGB2HSV)[:,:,1]
# obtain dense optical flow paramters
flow = cv2.calcOpticalFlowFarneback(one_g, two_g, flow=None,
pyr_scale=0.5,
levels=1,
winsize=10,
iterations=2,
poly_n=5,
poly_sigma=1.1,
flags=0)
# convert from cartesian to polar
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
# hue corresponds to direction
hsv[:,:,0] = ang * (180/ np.pi / 2)
# value corresponds to magnitude
hsv[:,:,2] = cv2.normalize(mag,None,0,255,cv2.NORM_MINMAX)
# convert HSV to int32's
hsv = np.asarray(hsv, dtype= np.float32)
rgb_flow = cv2.cvtColor(hsv,cv2.COLOR_HSV2RGB)
return rgb_flow
def recognizeDigit(digit, method = REC_METHOD_TEMPLATE_MATCHING, threshold= 55):
"""
Finds the best match for the given digit(RGB or gray color scheme). And returns the result and percentage as an integer.
@threshold percentage of similarity
"""
__readDigitTemplates()
digit = digit.copy()
if digit.shape[2] == 3:
digit = cv2.cvtColor(digit, cv2.COLOR_RGB2GRAY)
ret, digit = cv2.threshold(digit, 90, 255, cv2.THRESH_BINARY_INV)
bestDigit = -1
if method == REC_METHOD_TEMPLATE_MATCHING:
bestMatch = None
for i in range(len(__DIGIT_TEMPLATES)):
template = __DIGIT_TEMPLATES[i].copy()
if digit.shape[1] < template.shape[1]:
template = cv2.resize(template, (digit.shape[1], digit.shape[0]))
else:
digit = cv2.resize(digit, (template.shape[1], template.shape[0]))
result = cv2.matchTemplate(digit, template, cv2.TM_CCORR_NORMED)#cv2.TM_CCOEFF_NORMED)
(_, max_val, _, max_loc) = cv2.minMaxLoc(result)
if bestMatch is None or max_val > bestMatch:
bestMatch = max_val
bestDigit = i
print("New Best Match:", bestMatch, bestDigit)
if (bestMatch * 100) >= threshold:
return (bestDigit, bestMatch * 100)
return (-1, 0)
