def crop_random_sample_from_image(crop_width, crop_height, image, scales):
height, width, _ = image.shape
def check_scale(scale):
scaled_width, scaled_height = int(width / scale), int(height / scale)
return (scaled_width > crop_width) and (scaled_height > crop_height)
valid_scales = [s for s in scales if check_scale(s)]
if len(valid_scales) == 0:
raise RuntimeError("Receive an image where "
"no scale would fit a sample")
scale = random.choice(valid_scales)
scaled_width, scaled_height = int(width / scale), int(height / scale)
min_x = random.randint(0, scaled_width - crop_width)
min_y = random.randint(0, scaled_height - crop_height)
assert (min_x >= 0) and (min_y >= 0)
# resize image, crop sample
resized_image = cmtd.resized_image(image, scaled_width, scaled_height)
assert resized_image.shape[:2] == (scaled_height, scaled_width)
sample = resized_image[min_y:min_y + crop_height,
min_x:min_x + crop_width, :]
assert sample.shape[:2] == (crop_height, crop_width)
return sample
def crop_random_sample_from_image(crop_width, crop_height, image, scales):
height, width, _ = image.shape
def check_scale(scale):
scaled_width, scaled_height = int(width / scale), int(height / scale)
return (scaled_width > crop_width) and (scaled_height > crop_height)
valid_scales = [s for s in scales if check_scale(s)]
if len(valid_scales) == 0:
raise RuntimeError("Receive an image where "
"no scale would fit a sample")
scale = random.choice(valid_scales)
scaled_width, scaled_height = int(width / scale), int(height / scale)
min_x = random.randint(0, scaled_width - crop_width)
min_y = random.randint(0, scaled_height - crop_height)
assert (min_x >= 0) and (min_y >= 0)
# resize image, crop sample
resized_image = cmtd.resized_image(image,
scaled_width, scaled_height)
assert resized_image.shape[:2] == (scaled_height, scaled_width)
sample = resized_image[min_y: min_y + crop_height,
min_x: min_x + crop_width,
:]
assert sample.shape[:2] == (crop_height, crop_width)
return sample
def create_negatives_process_file(negatives_counter, file_path,
model_width, model_height, negatives_path):
negative_image = cv2.imread(file_path)
# negative images are 1.5 bigger than the model
model_to_negative_factor = 1.5
minimum_image_shape = (model_height * model_to_negative_factor,
model_width * model_to_negative_factor)
# <= because the C++ code has some weird +1's
if (negative_image.shape[1] <= minimum_image_shape[1]) or \
(negative_image.shape[0] <= minimum_image_shape[0]):
# must resize the image
negative_filename = "negative_sample_%i.resized.png" \
% negatives_counter
# width / height
image_ratio = float(negative_image.shape[1]) / negative_image.shape[0]
if (negative_image.shape[1] <= minimum_image_shape[1]):
resized_size = (int(minimum_image_shape[1]),
int(minimum_image_shape[1] / image_ratio))
if (negative_image.shape[0] <= minimum_image_shape[0]):
resized_size = (int(minimum_image_shape[0] * image_ratio),
int(minimum_image_shape[0]))
resized_shape = (resized_size[1],
resized_size[0],
negative_image.shape[2])
assert resized_shape[0] >= minimum_image_shape[0]
assert resized_shape[1] >= minimum_image_shape[1]
negative_image_resized = resized_image(negative_image,
resized_size[0],
resized_size[1])
negative_image = negative_image_resized
else:
# we can just copy
negative_filename = "negative_sample_%i.png" % negatives_counter
image_output_path = os.path.join(negatives_path, negative_filename)
cv2.imwrite(image_output_path, negative_image)
print(".", end="")
global print_counter
print_counter += 1 # no need to be thread safe
if print_counter % 10:
sys.stdout.flush()
return
def create_negatives_process_file(negatives_counter, file_path, model_width,
model_height, negatives_path):
negative_image = cv2.imread(file_path)
# negative images are 1.5 bigger than the model
model_to_negative_factor = 1.5
minimum_image_shape = (model_height * model_to_negative_factor,
model_width * model_to_negative_factor)
# <= because the C++ code has some weird +1's
if (negative_image.shape[1] <= minimum_image_shape[1]) or \
(negative_image.shape[0] <= minimum_image_shape[0]):
# must resize the image
negative_filename = "negative_sample_%i.resized.png" \
% negatives_counter
# width / height
image_ratio = float(negative_image.shape[1]) / negative_image.shape[0]
if (negative_image.shape[1] <= minimum_image_shape[1]):
resized_size = (int(minimum_image_shape[1]),
int(minimum_image_shape[1] / image_ratio))
if (negative_image.shape[0] <= minimum_image_shape[0]):
resized_size = (int(minimum_image_shape[0] * image_ratio),
int(minimum_image_shape[0]))
resized_shape = (resized_size[1], resized_size[0],
negative_image.shape[2])
assert resized_shape[0] >= minimum_image_shape[0]
assert resized_shape[1] >= minimum_image_shape[1]
negative_image_resized = resized_image(negative_image, resized_size[0],
resized_size[1])
negative_image = negative_image_resized
else:
# we can just copy
negative_filename = "negative_sample_%i.png" % negatives_counter
image_output_path = os.path.join(negatives_path, negative_filename)
cv2.imwrite(image_output_path, negative_image)
print(".", end="")
global print_counter
print_counter += 1 # no need to be thread safe
if print_counter % 10:
sys.stdout.flush()
return
def compute_resized_positive_example(input_image, input_image_path, box,
box_scale, model_octave, cropping_border,
model_width, model_height):
"""
The input box is expected to be a tight box around the pedestrian
"""
assert input_image != None
assert type(model_width) is int
assert type(model_height) is int
assert type(cropping_border) is int
image_height, image_width, image_depth = input_image.shape
# adjust the ratio, add the border --
# adjusting the ratio is a bad idea, pedestrians look "fat"
# instead, we adjust the left/right border
#box = adjust_box_ratio(box, model_width, model_height)
example_width = model_width + (2 * cropping_border)
example_height = model_height + (2 * cropping_border)
model_scale = 2**model_octave
box_relative_scale = box_scale / model_scale
desired_input_box_width = example_width * box_relative_scale
desired_input_box_height = example_height * box_relative_scale
input_box_width, input_box_height = box.width(), box.height()
top_bottom_border = (desired_input_box_height - input_box_height) / 2.0
left_right_border = (desired_input_box_width - input_box_width) / 2.0
desired_input_box_shape = (desired_input_box_height,
desired_input_box_width)
if (left_right_border <= 0) or False:
print("model width, height ==", (model_width, model_height))
print("example_width ==", example_width)
print("box_scale ==", box_scale)
print("model_scale ==", model_scale)
print("box_relative_scale ==", box_relative_scale)
print("desired_input_box_width ==", desired_input_box_width)
print("input_box_width ==", input_box_width)
print("input_box_height ==", input_box_height)
print("input_box ratio ==", input_box_height / float(input_box_width))
p1 = (int(box.min_corner.x), int(box.min_corner.y))
p2 = (int(box.max_corner.x), int(box.max_corner.y))
print("input_box min_corner, max_corner == ", p1, ",", p2)
color = (255, 10, 50)
failure_image = input_image.copy()
cv2.rectangle(failure_image, p1, p2, color)
global failures_count
failure_filename = "failure_case_%i_%s" % (
failures_count, os.path.basename(input_image_path))
cv2.imwrite(failure_filename, failure_image)
print("Created %s skipping one bounding_box in picture %s" %
(failure_filename, input_image_path))
failures_count += 1
return None # indicate that something went wrong
if (top_bottom_border < 0):
print("model width, height ==", (model_width, model_height))
print("example_width ==", example_width)
print("box_scale ==", box_scale)
print("model_scale ==", model_scale)
print("box_relative_scale ==", box_relative_scale)
print("desired_input_box_width ==", desired_input_box_width)
print("input_box_width ==", input_box_width)
print("input_box_height ==", input_box_height)
print("input_box ratio ==", input_box_height / float(input_box_width))
print("desired width ==", desired_input_box_width)
print("desired height==", desired_input_box_height)
p1 = (int(box.min_corner.x), int(box.min_corner.y))
p2 = (int(box.max_corner.x), int(box.max_corner.y))
print("input_box min_corner, max_corner == ", p1, ",", p2)
color = (255, 10, 50)
failure_image = input_image.copy()
cv2.rectangle(failure_image, p1, p2, color)
global failures_count
failure_filename = "failure_case_%i_%s" % (
failures_count, os.path.basename(input_image_path))
cv2.imwrite(failure_filename, failure_image)
print("Created %s skipping one bounding_box in picture %s" %
(failure_filename, input_image_path))
failures_count += 1
raise "adflkj"
return None # indicate that something went wrong
box = adjust_box_border(box, top_bottom_border, left_right_border)
# the box now has the desired_input_box_width and _height
box_shape = (box.height(), box.width())
#print(box_shape, "=?=", desired_input_box_shape)
assert abs(box_shape[0] - desired_input_box_shape[0]) <= 1.0
assert abs(box_shape[1] - desired_input_box_shape[1]) <= 1.0
# crop the box --
# which part of the box is outside the image ?
left_border = int(max(0, 0 - box.min_corner.x))
top_border = int(max(0, 0 - box.min_corner.y))
right_border = int(max(0, box.max_corner.x - image_width))
bottom_border = int(max(0, box.max_corner.y - image_height))
# define the part of the box that is inside the image
inner_box = box.copy()
inner_box.min_corner.x = max(0, box.min_corner.x)
inner_box.min_corner.y = max(0, box.min_corner.y)
inner_box.max_corner.x = min(box.max_corner.x, image_width)
inner_box.max_corner.y = min(box.max_corner.y, image_height)
# crop
cropped_image = input_image[
inner_box.min_corner.y:inner_box.max_corner.y,
inner_box.min_corner.x:inner_box.max_corner.x, :]
# extrapolate --
#border_type = cv2.BORDER_CONSTANT
border_type = cv2.BORDER_REPLICATE
#border_type = cv2.BORDER_WRAP
#border_type = cv2.BORDER_REFLECT
#border_type = cv2.BORDER_REFLECT_101
cropped_image = cv2.copyMakeBorder(cropped_image, top_border,
bottom_border, left_border,
right_border, border_type)
#print(cropped_image.shape[:2], "=?=", box_shape)
#assert cropped_image.shape[:2] == box_shape
# rescale the box so it fits the desired dimensions --
resized_positive_example = cmtd.resized_image(cropped_image, example_width,
example_height)
assert resized_positive_example.shape == (example_height, example_width,
image_depth)
return resized_positive_example
def compute_resized_positive_example(
input_image, input_image_path, box, box_scale, model_octave, cropping_border, model_width, model_height
):
"""
The input box is expected to be a tight box around the pedestrian
"""
assert input_image != None
assert type(model_width) is int
assert type(model_height) is int
assert type(cropping_border) is int
image_height, image_width, image_depth = input_image.shape
# adjust the ratio, add the border --
# adjusting the ratio is a bad idea, pedestrians look "fat"
# instead, we adjust the left/right border
# box = adjust_box_ratio(box, model_width, model_height)
example_width = model_width + (2 * cropping_border)
example_height = model_height + (2 * cropping_border)
model_scale = 2 ** model_octave
box_relative_scale = box_scale / model_scale
desired_input_box_width = example_width * box_relative_scale
desired_input_box_height = example_height * box_relative_scale
input_box_width, input_box_height = box.width(), box.height()
top_bottom_border = (desired_input_box_height - input_box_height) / 2.0
left_right_border = (desired_input_box_width - input_box_width) / 2.0
desired_input_box_shape = (desired_input_box_height, desired_input_box_width)
if (left_right_border <= 0) or False:
print("model width, height ==", (model_width, model_height))
print("example_width ==", example_width)
print("box_scale ==", box_scale)
print("model_scale ==", model_scale)
print("box_relative_scale ==", box_relative_scale)
print("desired_input_box_width ==", desired_input_box_width)
print("input_box_width ==", input_box_width)
print("input_box_height ==", input_box_height)
print("input_box ratio ==", input_box_height / float(input_box_width))
p1 = (int(box.min_corner.x), int(box.min_corner.y))
p2 = (int(box.max_corner.x), int(box.max_corner.y))
print("input_box min_corner, max_corner == ", p1, ",", p2)
color = (255, 10, 50)
failure_image = input_image.copy()
cv2.rectangle(failure_image, p1, p2, color)
global failures_count
failure_filename = "failure_case_%i_%s" % (failures_count, os.path.basename(input_image_path))
cv2.imwrite(failure_filename, failure_image)
print("Created %s skipping one bounding_box in picture %s" % (failure_filename, input_image_path))
failures_count += 1
return None # indicate that something went wrong
if top_bottom_border < 0:
print("model width, height ==", (model_width, model_height))
print("example_width ==", example_width)
print("box_scale ==", box_scale)
print("model_scale ==", model_scale)
print("box_relative_scale ==", box_relative_scale)
print("desired_input_box_width ==", desired_input_box_width)
print("input_box_width ==", input_box_width)
print("input_box_height ==", input_box_height)
print("input_box ratio ==", input_box_height / float(input_box_width))
print("desired width ==", desired_input_box_width)
print("desired height==", desired_input_box_height)
p1 = (int(box.min_corner.x), int(box.min_corner.y))
p2 = (int(box.max_corner.x), int(box.max_corner.y))
print("input_box min_corner, max_corner == ", p1, ",", p2)
color = (255, 10, 50)
failure_image = input_image.copy()
cv2.rectangle(failure_image, p1, p2, color)
global failures_count
failure_filename = "failure_case_%i_%s" % (failures_count, os.path.basename(input_image_path))
cv2.imwrite(failure_filename, failure_image)
print("Created %s skipping one bounding_box in picture %s" % (failure_filename, input_image_path))
failures_count += 1
raise "adflkj"
return None # indicate that something went wrong
box = adjust_box_border(box, top_bottom_border, left_right_border)
# the box now has the desired_input_box_width and _height
box_shape = (box.height(), box.width())
# print(box_shape, "=?=", desired_input_box_shape)
assert abs(box_shape[0] - desired_input_box_shape[0]) <= 1.0
assert abs(box_shape[1] - desired_input_box_shape[1]) <= 1.0
# crop the box --
# which part of the box is outside the image ?
left_border = int(max(0, 0 - box.min_corner.x))
top_border = int(max(0, 0 - box.min_corner.y))
right_border = int(max(0, box.max_corner.x - image_width))
bottom_border = int(max(0, box.max_corner.y - image_height))
# define the part of the box that is inside the image
inner_box = box.copy()
inner_box.min_corner.x = max(0, box.min_corner.x)
inner_box.min_corner.y = max(0, box.min_corner.y)
inner_box.max_corner.x = min(box.max_corner.x, image_width)
inner_box.max_corner.y = min(box.max_corner.y, image_height)
# crop
cropped_image = input_image[
inner_box.min_corner.y : inner_box.max_corner.y, inner_box.min_corner.x : inner_box.max_corner.x, :
]
# extrapolate --
# border_type = cv2.BORDER_CONSTANT
border_type = cv2.BORDER_REPLICATE
# border_type = cv2.BORDER_WRAP
# border_type = cv2.BORDER_REFLECT
# border_type = cv2.BORDER_REFLECT_101
cropped_image = cv2.copyMakeBorder(cropped_image, top_border, bottom_border, left_border, right_border, border_type)
# print(cropped_image.shape[:2], "=?=", box_shape)
# assert cropped_image.shape[:2] == box_shape
# rescale the box so it fits the desired dimensions --
resized_positive_example = cmtd.resized_image(cropped_image, example_width, example_height)
assert resized_positive_example.shape == (example_height, example_width, image_depth)
return resized_positive_example