Python cv2.COLOR_GRAY2RGB Examples
The following are 30
code examples of cv2.COLOR_GRAY2RGB().
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Example #1
| Source File: dbscan_based.py From PythonPilot with Apache License 2.0 | 7 votes |
|
def draw(self, dbscan_input_array, dbscan_label, dbscan_label_n):
# convert array to image
frame_draw = np.zeros((self.__compress_height, self.__compress_width), np.uint8)
frame_draw = cv2.cvtColor(frame_draw, cv2.COLOR_GRAY2RGB)
for i in range(dbscan_input_array.shape[0]):
if not dbscan_label[i] == -1:
color_th = dbscan_label[i] / dbscan_label_n
c_r = int(cm.hsv(color_th)[0]*255)
c_g = int(cm.hsv(color_th)[1]*255)
c_b = int(cm.hsv(color_th)[2]*255)
frame_draw = cv2.circle(frame_draw, \
(int(dbscan_input_array[i][0]), \
int(dbscan_input_array[i][1])), \
1, (c_r, c_g, c_b), 1)
return frame_draw
Example #2
| Source File: imutils.py From SickZil-Machine with GNU Affero General Public License v3.0 | 7 votes |
|
def channel3img(img):
'''
If img is 3-channel img(h,w,3) then this is identity funcion.
If img is grayscale img(h,w) then convert 3-channel img.
If img is bgra img, then CONVERT to bgr(TODO: warning required!)
else return None
'''
if len(img.shape) == 2: # if grayscale image, convert.
return cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
elif len(img.shape) == 3:
_,_,c = img.shape
if c == 3: # BGR(RGB)
return img
elif c == 4: # BGRA(RGBA)
return cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
#NOTE: warning: no alpha!
#else: None
#---------------------------------------------------------------------------------
# for segmap
Example #3
| Source File: utils_image.py From KAIR with MIT License | 7 votes |
|
def imread_uint(path, n_channels=3):
# input: path
# output: HxWx3(RGB or GGG), or HxWx1 (G)
if n_channels == 1:
img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE
img = np.expand_dims(img, axis=2) # HxWx1
elif n_channels == 3:
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB
return img
# --------------------------------------------
# matlab's imwrite
# --------------------------------------------
Example #4
| Source File: cvfunctional.py From opencv_transforms_torchvision with MIT License | 7 votes |
|
def to_grayscale(img, num_output_channels=1):
"""Convert image to grayscale version of image.
Args:
img (np.ndarray): Image to be converted to grayscale.
Returns:
CV Image: Grayscale version of the image.
if num_output_channels == 1 : returned image is single channel
if num_output_channels == 3 : returned image is 3 channel with r == g == b
"""
if not _is_numpy_image(img):
raise TypeError('img should be CV Image. Got {}'.format(type(img)))
if num_output_channels == 1:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
elif num_output_channels == 3:
img = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_RGB2GRAY), cv2.COLOR_GRAY2RGB)
else:
raise ValueError('num_output_channels should be either 1 or 3')
return img
Example #5
| Source File: features.py From pyCFTrackers with MIT License | 6 votes |
|
def get_features(self, img, pos, sample_sz, scales):
feat1 = []
feat2 = []
if img.shape[2] == 1:
img = cv2.cvtColor(img.squeeze(), cv2.COLOR_GRAY2RGB)
if not isinstance(scales, list) and not isinstance(scales, np.ndarray):
scales = [scales]
patches = []
for scale in scales:
patch = self._sample_patch(img, pos, sample_sz*scale, sample_sz)
patch = mx.nd.array(patch / 255., ctx=self._ctx)
normalized = mx.image.color_normalize(patch,
mean=mx.nd.array([0.485, 0.456, 0.406], ctx=self._ctx),
std=mx.nd.array([0.229, 0.224, 0.225], ctx=self._ctx))
normalized = normalized.transpose((2, 0, 1)).expand_dims(axis=0)
patches.append(normalized)
patches = mx.nd.concat(*patches, dim=0)
f1, f2 = self._forward(patches)
f1 = self._feature_normalization(f1)
f2 = self._feature_normalization(f2)
return f1, f2
Example #6
| Source File: test.py From sscdnet with MIT License | 6 votes |
|
def display_results(self, t0, t1, mask_pred, mask_gt):
w, h = self.w_orig, self.h_orig
t0_disp = cv2.resize(np.transpose(t0.numpy(), (1, 2, 0)).astype(np.uint8), (w, h))
t1_disp = cv2.resize(np.transpose(t1.numpy(), (1, 2, 0)).astype(np.uint8), (w, h))
mask_pred_disp = cv2.resize(cv2.cvtColor(mask_pred.numpy().astype(np.uint8), cv2.COLOR_GRAY2RGB), (w, h))
mask_gt_disp = cv2.resize(cv2.cvtColor(mask_gt.astype(np.uint8), cv2.COLOR_GRAY2RGB), (w, h))
img_out = np.zeros((h* 2, w * 2, 3), dtype=np.uint8)
img_out[0:h, 0:w, :] = t0_disp
img_out[0:h, w:w * 2, :] = t1_disp
img_out[h:h * 2, 0:w * 1, :] = mask_gt_disp
img_out[h:h * 2, w * 1:w * 2, :] = mask_pred_disp
for dn, img in zip(['mask', 'disp'], [mask_pred_disp, img_out]):
dn_save = os.path.join(self.args.checkpointdir, 'result', dn)
fn_save = os.path.join(dn_save, '{0:08d}.png'.format(self.index))
if not os.path.exists(dn_save):
os.makedirs(dn_save)
print('Writing ... ' + fn_save)
cv2.imwrite(fn_save, img)
Example #7
| Source File: image_process.py From Advanced_Lane_Lines with MIT License | 6 votes |
|
def test_yellow_grid_thresh_images(src, dst, y_low=(10,50,0), y_high=(30,255,255), sx_thresh=(20, 100)):
"""
apply the thresh to images in a src folder and output to dst foler
"""
image_files = glob.glob(src+"*.jpg")
for idx, file in enumerate(image_files):
print(file)
img = mpimg.imread(file)
image_threshed = yellow_grid_thresh(img, y_low, y_high, sx_thresh)
file_name = file.split("\\")[-1]
print(file_name)
out_image = dst+file_name
print(out_image)
# convert binary to RGB, *255, to visiual, 1 will not visual after write to file
image_threshed = cv2.cvtColor(image_threshed*255, cv2.COLOR_GRAY2RGB)
cv2.imwrite(out_image, image_threshed)
Example #8
| Source File: image_process.py From Advanced_Lane_Lines with MIT License | 6 votes |
|
def test_color_grid_thresh_dynamic(src, dst, s_thresh, sx_thresh):
"""
apply the thresh to images in a src folder and output to dst foler
"""
image_files = glob.glob(src+"*.jpg")
for idx, file in enumerate(image_files):
print(file)
img = mpimg.imread(file)
image_threshed = color_grid_thresh_dynamic(img, s_thresh=s_thresh, sx_thresh=sx_thresh)
file_name = file.split("\\")[-1]
print(file_name)
out_image = dst+file_name
print(out_image)
# convert binary to RGB, *255, to visiual, 1 will not visual after write to file
image_threshed = cv2.cvtColor(image_threshed*255, cv2.COLOR_GRAY2RGB)
cv2.imwrite(out_image, image_threshed)
Example #9
| Source File: image_process.py From Advanced_Lane_Lines with MIT License | 6 votes |
|
def test_thresh_images(src, dst, s_thresh, sx_thresh):
"""
apply the thresh to images in a src folder and output to dst foler
"""
image_files = glob.glob(src+"*.jpg")
for idx, file in enumerate(image_files):
print(file)
img = mpimg.imread(file)
image_threshed = color_grid_thresh(img, s_thresh=s_thresh, sx_thresh=sx_thresh)
file_name = file.split("\\")[-1]
print(file_name)
out_image = dst+file_name
print(out_image)
# convert binary to RGB, *255, to visiual, 1 will not visual after write to file
image_threshed = cv2.cvtColor(image_threshed*255, cv2.COLOR_GRAY2RGB)
cv2.imwrite(out_image, image_threshed)
Example #10
| Source File: offset_ui_tool.py From TGC-Designer-Tools with Apache License 2.0 | 6 votes |
|
def drawNewLocation(ax, image_dict, result, image_scale, radio, sx, sy, event, ar):
x_offset = 0.0
y_offset = 0.0
if sx is not None and sy is not None:
x_offset = sx.val
y_offset = sy.val
vosm = np.copy(image_dict["Visible"])
vosm = OSMTGC.addOSMToImage(result.ways, vosm, pc, image_scale, x_offset, y_offset)
image_dict["Visible Golf"] = vosm
hosm = np.copy(image_dict["Heightmap"]).astype('float32')
hosm = np.clip(hosm, 0.0, 3.5*np.median( hosm[ hosm >= 0.0 ])) # Limit outlier pixels
hosm = hosm / np.max(hosm)
hosm = cv2.cvtColor(hosm, cv2.COLOR_GRAY2RGB)
hosm = OSMTGC.addOSMToImage(result.ways, hosm, pc, image_scale, x_offset, y_offset)
image_dict["Heightmap Golf"] = hosm
# Always set to Visible Golf after drawing new golf features
ax.imshow(image_dict["Visible Golf"], origin='lower')
radio.set_active(1)
Example #11
| Source File: functional.py From torch-toolbox with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def adjust_saturation(img, saturation_factor):
"""Adjust color saturation of an image.
Args:
img (CV Image): CV Image to be adjusted.
saturation_factor (float): How much to adjust the saturation. 0 will
give a black and white image, 1 will give the original image while
2 will enhance the saturation by a factor of 2.
Returns:
CV Image: Saturation adjusted image.
"""
if not _is_numpy_image(img):
raise TypeError('img should be CV Image. Got {}'.format(type(img)))
im = img.astype(np.float32)
degenerate = cv2.cvtColor(
cv2.cvtColor(
im,
cv2.COLOR_RGB2GRAY),
cv2.COLOR_GRAY2RGB)
im = (1 - saturation_factor) * degenerate + saturation_factor * im
im = im.clip(min=0, max=255)
return im.astype(img.dtype)
Example #12
| Source File: chapter2.py From OpenCV-Computer-Vision-Projects-with-Python with MIT License | 6 votes |
|
def ProcessFrame(self, frame):
# segment arm region
segment = self.SegmentArm(frame)
# make a copy of the segmented image to draw on
draw = cv2.cvtColor(segment, cv2.COLOR_GRAY2RGB)
# draw some helpers for correctly placing hand
cv2.circle(draw,(self.imgWidth/2,self.imgHeight/2),3,[255,102,0],2)
cv2.rectangle(draw, (self.imgWidth/3,self.imgHeight/3), (self.imgWidth*2/3, self.imgHeight*2/3), [255,102,0],2)
# find the hull of the segmented area, and based on that find the
# convexity defects
[contours,defects] = self.FindHullDefects(segment)
# detect the number of fingers depending on the contours and convexity defects
# draw defects that belong to fingers green, others red
[nofingers,draw] = self.DetectNumberFingers(contours, defects, draw)
# print number of fingers on image
cv2.putText(draw, str(nofingers), (30,30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255))
return draw
Example #13
| Source File: image_process.py From Advanced_Lane_Lines with MIT License | 6 votes |
|
def test_yellow_white_thresh_images(src, dst, y_low=(10,50,0), y_high=(30,255,255), w_low=(180,180,180), w_high=(255,255,255)):
"""
apply the thresh to images in a src folder and output to dst foler
"""
image_files = glob.glob(src+"*.jpg")
for idx, file in enumerate(image_files):
print(file)
img = mpimg.imread(file)
image_threshed = yellow_white_thresh(img, y_low, y_high, w_low, w_high)
file_name = file.split("\\")[-1]
print(file_name)
out_image = dst+file_name
print(out_image)
# convert binary to RGB, *255, to visiual, 1 will not visual after write to file
image_threshed = cv2.cvtColor(image_threshed*255, cv2.COLOR_GRAY2RGB)
# HSV = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
# V = HSV[:,:,2]
# brightness = np.mean(V)
# info_str = "brightness is: {}".format(int(brightness))
# cv2.putText(image_threshed, info_str, (50,700), cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,255),2)
cv2.imwrite(out_image, image_threshed)
Example #14
| Source File: features.py From pyECO with MIT License | 6 votes |
|
def get_features(self, img, pos, sample_sz, scales):
feat1 = []
feat2 = []
if img.shape[2] == 1:
img = cv2.cvtColor(img.squeeze(), cv2.COLOR_GRAY2RGB)
if not isinstance(scales, list) and not isinstance(scales, np.ndarray):
scales = [scales]
patches = []
for scale in scales:
patch = self._sample_patch(img, pos, sample_sz*scale, sample_sz)
patch = mx.nd.array(patch / 255., ctx=self._ctx)
normalized = mx.image.color_normalize(patch,
mean=mx.nd.array([0.485, 0.456, 0.406], ctx=self._ctx),
std=mx.nd.array([0.229, 0.224, 0.225], ctx=self._ctx))
normalized = normalized.transpose((2, 0, 1)).expand_dims(axis=0)
patches.append(normalized)
patches = mx.nd.concat(*patches, dim=0)
f1, f2 = self._forward(patches)
f1 = self._feature_normalization(f1)
f2 = self._feature_normalization(f2)
return f1, f2
Example #15
| Source File: cvfunctional.py From opencv_transforms_torchvision with MIT License | 6 votes |
|
def adjust_saturation(img, saturation_factor):
"""Adjust color saturation of an image.
Args:
img (np.ndarray): CV Image to be adjusted.
saturation_factor (float): How much to adjust the saturation. 0 will
give a gray image, 1 will give the original image while
2 will enhance the saturation by a factor of 2.
Returns:
np.ndarray: Saturation adjusted image.
"""
if not _is_numpy_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
im = img.astype(np.float32)
degenerate = cv2.cvtColor(cv2.cvtColor(im, cv2.COLOR_RGB2GRAY), cv2.COLOR_GRAY2RGB)
im = (1-saturation_factor) * degenerate + saturation_factor * im
im = im.clip(min=0, max=255)
return im.astype(img.dtype)
Example #16
| Source File: lsun_bedroom_line2color.py From chainer-cyclegan with MIT License | 6 votes |
|
def get_example(self, i):
id = self.all_keys[i]
img = None
val = self.db.get(id.encode())
img = cv2.imdecode(np.fromstring(val, dtype=np.uint8), 1)
img = self.do_augmentation(img)
img_color = img
img_color = self.preprocess_image(img_color)
img_line = XDoG(img)
img_line = cv2.cvtColor(img_line, cv2.COLOR_GRAY2RGB)
#if img_line.ndim == 2:
# img_line = img_line[:, :, np.newaxis]
img_line = self.preprocess_image(img_line)
return img_line, img_color
Example #17
| Source File: test_warp.py From open-vot with MIT License | 6 votes |
|
def test_pad_array(self):
dataset = OTB(self.otb_dir, download=True)
npad = random.choice([0, 10, 50])
padding = random.choice([None, 0, 'avg'])
print('[cv2-pad] padding:', padding, 'npad:', npad)
img_files, anno = random.choice(dataset)
for f, img_file in enumerate(img_files):
image = cv2.imread(img_file)
if image.ndim == 2:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
elif image.ndim == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = pad_array(image, npad, padding=padding)
show_frame(image[:, :, ::-1], fig_n=1)
Example #18
| Source File: test_warp.py From open-vot with MIT License | 6 votes |
|
def test_crop_array(self):
dataset = OTB(self.otb_dir, download=True)
padding = random.choice([None, 0, 'avg'])
out_size = random.choice([None, 255])
print('[cv2-crop] padding:', padding, 'out_size:', out_size)
img_files, anno = random.choice(dataset)
for f, img_file in enumerate(img_files):
image = cv2.imread(img_file)
if image.ndim == 2:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
elif image.ndim == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
bndbox = anno[f, :]
center = bndbox[:2] + bndbox[2:] / 2
patch = crop_array(image, center, bndbox[2:],
padding=padding, out_size=out_size)
show_frame(patch, fig_n=2, pause=0.1)
Example #19
| Source File: test_warp.py From open-vot with MIT License | 6 votes |
|
def test_crop_tensor(self):
dataset = OTB(self.otb_dir, download=True)
padding = random.choice([None, 0, 'avg'])
out_size = random.choice([255])
print('[PyTorch-crop] padding:', padding, 'out_size:', out_size)
img_files, anno = random.choice(dataset)
for f, img_file in enumerate(img_files):
image = cv2.imread(img_file)
if image.ndim == 2:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
elif image.ndim == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = torch.from_numpy(image).permute(
2, 0, 1).unsqueeze(0).float()
bndbox = torch.from_numpy(anno[f, :]).float()
center = bndbox[:2] + bndbox[2:] / 2
patch = crop_tensor(image, center, bndbox[2:],
padding=padding, out_size=out_size)
patch = patch.squeeze().permute(1, 2, 0).cpu().numpy().astype(np.uint8)
show_frame(patch, fig_n=1, pause=0.1)
Example #20
| Source File: test_warp.py From open-vot with MIT License | 6 votes |
|
def test_resize_tensor(self):
dataset = OTB(self.otb_dir, download=True)
out_size = random.choice([30, 100, 255])
print('[PyTorch-resize]:', out_size)
img_files, anno = random.choice(dataset)
for f, img_file in enumerate(img_files):
image = cv2.imread(img_file)
if image.ndim == 2:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
elif image.ndim == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = torch.from_numpy(image).permute(
2, 0, 1).unsqueeze(0).float()
image = resize_tensor(image, out_size)
image = image.squeeze().permute(1, 2, 0).numpy().astype(np.uint8)
show_frame(image, fig_n=2, pause=0.1)
Example #21
| Source File: __init__.py From open-vot with MIT License | 6 votes |
|
def track(self, img_files, init_rect, visualize=False):
frame_num = len(img_files)
bndboxes = np.zeros((frame_num, 4))
bndboxes[0, :] = init_rect
speed_fps = np.zeros(frame_num)
for f, img_file in enumerate(img_files):
image = cv2.imread(img_file)
if image.ndim == 2:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
elif image.ndim == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
start_time = time.time()
if f == 0:
self.init(image, init_rect)
else:
bndboxes[f, :] = self.update(image)
elapsed_time = time.time() - start_time
speed_fps[f] = 1. / elapsed_time
if visualize:
show_frame(image, bndboxes[f, :], fig_n=1)
return bndboxes, speed_fps
Example #22
| Source File: OpenCVQImage.py From vidpipe with GNU General Public License v3.0 | 6 votes |
|
def __init__( self, opencvBgrImg ):
# depth = cv2.IPL_DEPTH_8U
if len( opencvBgrImg.shape ) == 3:
h, w, nChannels = opencvBgrImg.shape
opencvRgbImg = np.zeros( ( h, w, 3 ), np.uint8 )
opencvRgbImg = cv2.cvtColor( opencvBgrImg, cv2.COLOR_BGR2RGB )
else:
# img_format = QtGui.QImage.Format_Mono
h, w = opencvBgrImg.shape
# opencvRgbImg = np.zeros( ( h, w, 3 ), np.uint8 )
opencvRgbImg = cv2.cvtColor( opencvBgrImg, cv2.COLOR_GRAY2RGB )
# cv2.mixChannels( [ opencvBgrImg ], [ opencvRgbImg ], [ 0, 2 ] )
# if depth != cv.IPL_DEPTH_8U or nChannels != 3:
# raise ValueError("the input image must be 8-bit, 3-channel")
self._imgData = opencvRgbImg.tostring()
super( OpenCVQImage, self ).__init__( self._imgData, w, h, QtGui.QImage.Format_RGB888 )
Example #23
| Source File: coco.py From chainer-mask-rcnn with MIT License | 6 votes |
|
def get_example(self, i):
img_id = self.img_ids[i]
ann_ids = self.coco.getAnnIds(imgIds=img_id)
anns = self.coco.loadAnns(ann_ids)
img_fname = self.img_fname.format(img_id)
img = skimage.io.imread(img_fname)
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
example = self._annotations_to_example(
anns, img.shape[0], img.shape[1])
# img, bboxes, labels, masks
# or img, bboxes, labels, masks, crowds
# or img, bboxes, labels, masks, areas
# or img, bboxes, labels, masks, crowds, areas
return tuple([img] + example)
Example #24
| Source File: visual_odometry.py From pyslam with GNU General Public License v3.0 | 6 votes |
|
def drawFeatureTracks(self, img, reinit = False):
draw_img = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
num_outliers = 0
if(self.stage == VoStage.GOT_FIRST_IMAGE):
if reinit:
for p1 in self.kps_cur:
a,b = p1.ravel()
cv2.circle(draw_img,(a,b),1, (0,255,0),-1)
else:
for i,pts in enumerate(zip(self.track_result.kps_ref_matched, self.track_result.kps_cur_matched)):
drawAll = False # set this to true if you want to draw outliers
if self.mask_match[i] or drawAll:
p1, p2 = pts
a,b = p1.ravel()
c,d = p2.ravel()
cv2.line(draw_img, (a,b),(c,d), (0,255,0), 1)
cv2.circle(draw_img,(a,b),1, (0,0,255),-1)
else:
num_outliers+=1
if kVerbose:
print('# outliers: ', num_outliers)
return draw_img
Example #25
| Source File: utils_draw.py From pyslam with GNU General Public License v3.0 | 6 votes |
|
def combine_images_vertically(img1, img2):
if img1.ndim<=2:
img1 = cv2.cvtColor(img1,cv2.COLOR_GRAY2RGB)
if img2.ndim<=2:
img2 = cv2.cvtColor(img2,cv2.COLOR_GRAY2RGB)
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
img3 = np.zeros((h1+h2, max(w1, w2),3), np.uint8)
img3[:h1, :w1,:3] = img1
img3[h1:h1+h2,:w2,:3] = img2
return img3
# draw features matches (images are combined horizontally)
# input:
# - kps1 = [Nx2] array of keypoint coordinates
# - kps2 = [Nx2] array of keypoint coordinates
# - kps1_sizes = [Nx1] array of keypoint sizes
# - kps2_sizes = [Nx1] array of keypoint sizes
# output: drawn image
Example #26
| Source File: test-mvs.py From dfc2019 with MIT License | 6 votes |
|
def category_to_color(category_image):
# define colors
# color table is here: https://www.rapidtables.com/web/color/RGB_Color.html
colors = []
colors.append((165, 42, 42)) # 0 brown (ground)
colors.append((0, 128, 0)) # 1 green (trees)
colors.append((255, 0, 0)) # 2 red (buildings)
colors.append((0, 0, 255)) # 3 blue (water)
colors.append((128, 128, 128)) # 4 gray (elevated road)
colors.append((0, 0, 0)) # 5 black (other)
# convert categories to color image
rows = category_image.shape[0]
cols = category_image.shape[1]
categories = category_image.astype(np.uint8)
categories = np.reshape(categories, [rows, cols])
rgb_image = cv2.cvtColor(categories, cv2.COLOR_GRAY2RGB)
for i in range(cols):
for j in range(rows):
rgb_image[j, i, :] = colors[categories[j, i]]
return rgb_image
Example #27
| Source File: test-icnet.py From dfc2019 with MIT License | 6 votes |
|
def category_to_color(category_image):
# define colors
# color table is here: https://www.rapidtables.com/web/color/RGB_Color.html
colors = []
colors.append((165,42,42)) # 0 brown (ground)
colors.append((0,128,0)) # 1 green (trees)
colors.append((255,0,0)) # 2 red (buildings)
colors.append((0,0,255)) # 3 blue (water)
colors.append((128,128,128)) # 4 gray (elevated road)
colors.append((0,0,0)) # 6 black (other)
# convert categories to color image
rows = category_image.shape[0]
cols = category_image.shape[1]
categories = category_image.astype(np.uint8)
categories = np.reshape(categories, [rows, cols])
rgb_image = cv2.cvtColor(categories,cv2.COLOR_GRAY2RGB)
for i in range(cols):
for j in range(rows):
rgb_image[j,i,:] = colors[categories[j,i]]
return rgb_image
Example #28
| Source File: train.py From dfc2019 with MIT License | 6 votes |
|
def category_to_color(self, category_image):
# define colors
# color table is here: https://www.rapidtables.com/web/color/RGB_Color.html
colors = []
colors.append((165, 42, 42)) # 0 brown (ground)
colors.append((0, 128, 0)) # 1 green (trees)
colors.append((255, 0, 0)) # 2 red (buildings)
colors.append((0, 0, 255)) # 3 blue (water)
colors.append((128, 128, 128)) # 4 gray (elevated road / bridge)
colors.append((0, 0, 0)) # 5 black (other)
# convert categories to color image
rows = category_image.shape[0]
cols = category_image.shape[1]
categories = category_image.astype(np.uint8)
categories = np.reshape(categories, [rows, cols])
rgb_image = cv2.cvtColor(categories, cv2.COLOR_GRAY2RGB)
for i in range(cols):
for j in range(rows):
rgb_image[j, i, :] = colors[categories[j, i]]
return rgb_image
# save image with truth and prediction
Example #29
| Source File: functional.py From torch-toolbox with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def to_tensor(pic):
"""Convert a ``numpy.ndarray`` image to tensor.
See ``ToTensor`` for more details.
Args:
pic (numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
if _is_numpy_image(pic):
if pic.ndim == 2:
pic = cv2.cvtColor(pic, cv2.COLOR_GRAY2RGB)
img = torch.from_numpy(pic.transpose((2, 0, 1)))
# backward compatibility
if isinstance(img, torch.ByteTensor):
return img.float().div(255)
else:
return img
else:
raise TypeError('pic should be ndarray. Got {}.'.format(type(pic)))
Example #30
| Source File: postprocessing.py From nni with MIT License | 5 votes |
|
def save_pseudo_label_masks(submission_file):
df = pd.read_csv(submission_file, na_filter=False)
print(df.head())
img_dir = os.path.join(settings.TEST_DIR, 'masks')
for i, row in enumerate(df.values):
decoded_mask = run_length_decoding(row[1], (101,101))
filename = os.path.join(img_dir, '{}.png'.format(row[0]))
rgb_mask = cv2.cvtColor(decoded_mask,cv2.COLOR_GRAY2RGB)
print(filename)
cv2.imwrite(filename, decoded_mask)
if i % 100 == 0:
print(i)
