a_width = annotation.size[1]
a_height = annotation.size[0]
margin_y = annotation.loc[0]
margin_x = annotation.loc[1]
num_patches_ann = np.ceil(float(config_opt.num_patches) * a_width * a_height / total_object_area)
if patch_count + num_patches_ann > config_opt.num_patches:
num_patches_ann = config_opt.num_patches - patch_count
y_ind = np.random.randint(0, a_height - config_opt.feature.cell.patch_size, num_patches_ann) + margin_y
x_ind = np.random.randint(0, a_width - config_opt.feature.cell.patch_size, num_patches_ann) + margin_x
x_ind = x_ind[:, np.newaxis, np.newaxis] + patch_pixel_map_x[np.newaxis, :, :]
y_ind = y_ind[:, np.newaxis, np.newaxis] + patch_pixel_map_y[np.newaxis, :, :]
frame = vivid.cvmat2array(gv.get_frame(image_ind))
patches[patch_count : patch_count + num_patches_ann] = frame[y_ind, x_ind].reshape((num_patches_ann, -1))
print "%s: %d" % (annotation.image_name, num_patches_ann)
patch_count += num_patches_ann
patch_means = np.mean(patches, axis=1)
patches = patches - patch_means[:, np.newaxis]
np.save(target_file, patches)
total_object_area))
if (patch_count + num_patches_ann > config_opt.num_patches):
num_patches_ann = config_opt.num_patches - patch_count
y_ind = np.random.randint(0, a_height - config_opt.feature.cell.patch_size,
num_patches_ann) + margin_y
x_ind = np.random.randint(0, a_width - config_opt.feature.cell.patch_size,
num_patches_ann) + margin_x
x_ind = x_ind[:, np.newaxis,
np.newaxis] + patch_pixel_map_x[np.newaxis, :, :]
y_ind = y_ind[:, np.newaxis,
np.newaxis] + patch_pixel_map_y[np.newaxis, :, :]
frame = vivid.cvmat2array(gv.get_frame(image_ind))
patches[patch_count:patch_count +
num_patches_ann] = (frame[y_ind, x_ind].reshape(
(num_patches_ann, -1)))
print "%s: %d" % (annotation.image_name, num_patches_ann)
patch_count += num_patches_ann
patch_means = np.mean(patches, axis=1)
patches = patches - patch_means[:, np.newaxis]
np.save(target_file, patches)
def testRead(self):
frame_uint8 = vivid.cvmat2array(self.fv.get_frame(0))
assert(np.allclose(frame_uint8[:,:,::-1], self.reference_image_uint8))
config_opts[0].image_sets_fp[options.set_type][annotation_image_ind])
annotation = config_opts[0].annotations[annotation_ind]
max_size = np.max(annotation.size)
max_dim = np.argmax(annotation.size)
o_height = config_opts[0].object_size[0]
o_width = config_opts[0].object_size[1]
scaling = float(config_opts[0].object_size[max_dim]) / max_size
annotation_location = [annotation.loc[0], annotation.loc[1], scaling]
ss.scale = scaling
gr_frame = vivid.cvmat2array(gv.get_frame(annotation_image_ind))
scaled_frame = vivid.cvmat2array(ss.get_frame(annotation_image_ind))
scaled_loc = np.array(annotation.loc) * scaling
scaled_size = np.array(annotation.size) * scaling
scaled_fixed_loc = (scaled_loc -
(np.array(config_opts[0].object_size) - np.array(scaled_size)) / 2.0)
scaled_fixed_loc = (
(scaled_fixed_loc / config_opts[0].feature.window_stride).astype('int') *
config_opts[0].feature.window_stride)
margin_up = -scaled_fixed_loc[0]
margin_left = -scaled_fixed_loc[1]
def detect_and_write(input_images, output_images):
DETECTION_THRESHOLD = -.5
config_opt = ConfigOpt(options.config_source)
if options.bootstrap:
svm_model_file = os.path.join(config_opt.svm_model_path, "modelb.svm")
else:
svm_model_file = os.path.join(config_opt.svm_model_path, "model1.svm")
lm = read_liblinear_model(svm_model_file)
w = lm.w[:-1]
b = lm.w[-1] * lm.bias
fv = vivid.ImageSource(imlist=input_images)
cs = vivid.ConvertedSource(fv,
target_type=vivid.cv.CV_32FC3,
scale=1.0 / 255.0)
gv = vivid.GreySource(cs)
clusters = np.load(config_opt.dictionary_file).reshape((
config_opt.feature.cell.dictionary_size,
config_opt.feature.cell.patch_size,
config_opt.feature.cell.patch_size))
fs = FeatureSource(gv, config_opt.feature, clusters)
all_scores = []
for fi, input_image, output_image in zip(
xrange(len(input_images)), input_images, output_images):
print('Image: {0}'.format(input_image))
fs.init_frame(fi)
frame = vivid.cvmat2array(cs.get_frame(fi))[:, :, ::-1]
while True:
try:
locs, scale = fs.init_next_scale()
print("Processing scale: {0:.2f}\t".format(scale))
num_y, num_x = locs[0].shape
scale_scores = np.empty((num_y, num_x), dtype='float32')
for yind in range(0, num_y, 10):
ymin = yind
ymax = min(num_y, yind + 10)
feas = fs.get_features_from_scale(
ymin=yind, ymax=ymax,
xmin=0, xmax=num_x)
scale_scores[ymin:ymax, :] = (feas *
w[np.newaxis, np.newaxis, :]).sum(axis=2) + b
detections = scale_scores >= DETECTION_THRESHOLD
for yi, xi, detection_score in zip(
locs[0][detections],
locs[1][detections],
scale_scores[detections]):
if detection_score >= 0:
box_color = [1.0, 0, 0]
else:
box_color = [1.0, 1.0, 0]
print("Detection at: y: {0}, x: {1}, s: {2:.2f}".format(
yi, xi, scale))
frame = draw_bounding_box(frame, np.array([yi, xi, scale]),
text="%.3f" % detection_score,
color=box_color)
except EndOfScales:
break
imsave(output_image, frame)
def init_frame(self, frame_num,
margin_up=None, margin_down=None,
margin_left=None, margin_right=None,
scales=None):
self.scales = scales
self.feature_frame = frame_num
self.last_scale = -1
"""
this is the entry point, call this function to compute features
margin is in terms of window strides
"""
min_offset = self.feature_opt.cell.patch_size / 2
if margin_up == None:
margin_up = -min_offset
if margin_down == None:
margin_down = -min_offset
if margin_left == None:
margin_left = -min_offset
if margin_right == None:
margin_right = -min_offset
self.margins = [margin_up, margin_down, margin_left, margin_right]
frame_size = np.array([self.origin.get_frame(frame_num).height,
self.origin.get_frame(frame_num).width])
self.scaled_sources = []
if self.scales == None:
scale_factor = 1.0
self.scales = []
while (True):
cs = vivid.CachedSource(vivid.ScaledSource(
self.origin,
scale=1.0 / scale_factor),
cache_size=1)
frame_size = np.array(vivid.cvmat2array(
cs.get_frame(self.feature_frame)).shape, dtype='float32')
#see if we can fit a window in this scale
h_windows = ((frame_size[0] - margin_up + margin_down) /
self.feature_opt.window_size[0])
w_windows = ((frame_size[1] + margin_left + margin_right) /
self.feature_opt.window_size[1])
#print "h: %.2f\t%.2f" % (h_windows, w_windows)
if h_windows < 1 or w_windows < 1:
break
self.scales.append(1.0 / scale_factor)
scale_factor *= self.feature_opt.scale_step
self.scaled_sources.append(cs)
else:
for s in scales:
self.scaled_sources.append(
vivid.ScaledSource(self.origin, scale=s))
config_opts[0].image_sets_fp[options.set_type][annotation_image_ind])
annotation = config_opts[0].annotations[annotation_ind]
max_size = np.max(annotation.size)
max_dim = np.argmax(annotation.size)
o_height = config_opts[0].object_size[0]
o_width = config_opts[0].object_size[1]
scaling = float(config_opts[0].object_size[max_dim]) / max_size
annotation_location = [annotation.loc[0], annotation.loc[1], scaling]
ss.scale = scaling
gr_frame = vivid.cvmat2array(gv.get_frame(annotation_image_ind))
scaled_frame = vivid.cvmat2array(ss.get_frame(annotation_image_ind))
scaled_loc = np.array(annotation.loc) * scaling
scaled_size = np.array(annotation.size) * scaling
scaled_fixed_loc = (
scaled_loc -
(np.array(config_opts[0].object_size) - np.array(scaled_size)) / 2.0)
scaled_fixed_loc = ((scaled_fixed_loc /
config_opts[0].feature.window_stride).astype('int') *
config_opts[0].feature.window_stride)
margin_up = -scaled_fixed_loc[0]
import pathfinder import vivid from matplotlib.pyplot import imshow, show, gray # Create the reader for a list of image files iv = vivid.ImageSource(imlist=["./media/kewell1.jpg"]) # Source for converting to float and scaling to [0,1] cs = vivid.ConvertedSource(iv, vivid.cv.CV_32FC3, 1.0 / 255.0) # Source for covnerting to grayscale gs = vivid.GreySource(cs) # Source for resizing ss = vivid.ScaledSource(gs, 0.5) # Get the first image frame = cs.get_frame(0) # Get the grayscale version of the first image frame_gray = gs.get_frame(0) # Get the scaled version of the grayscale image frame_gray_scaled = ss.get_frame(0) # Convert into numpy arrays fr = vivid.cvmat2array(frame) fr_gray = vivid.cvmat2array(frame_gray) fr_gray_scaled = vivid.cvmat2array(frame_gray_scaled)
import pathfinder
import vivid
import numpy as np
# Create the reader for a list of image files
iv = vivid.ImageSource(imlist=['./media/kewell1.jpg'])
cs = vivid.ConvertedSource(iv, vivid.cv.CV_32FC3, 1.0 / 255.0)
sqs = vivid.SquaredSource(cs)
sq_frame = vivid.cvmat2array(sqs.get_frame(0))
reference_sq_frame = vivid.cvmat2array(iv.get_frame(0)).astype('float32') / 255.0
reference_sq_frame *= reference_sq_frame
print "Check: " + str(np.allclose(reference_sq_frame, sq_frame))
