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This script shows how to extract face area and corsponding facial
landmark points from IBUG dataset.
import json
import os
import numpy as np
import cv2
import pts_tools as pt
DATA_DIR = "/home/robin/Documents/landmark/dataset/300VW_Dataset_2015_12_14/044"
TARGET_DIR = "/home/robin/Documents/landmark/223K/300vw"
def read_image(point_file):
"""Read the corsponding image."""
head, tail = os.path.split(point_file)
image_file = tail.split('.')[-2]
img_jpg = os.path.join(head, image_file + ".jpg")
img_png = os.path.join(head, image_file + ".png")
if os.path.exists(img_jpg):
img = cv2.imread(img_jpg)
img = cv2.imread(img_png)
return img
def get_valid_points(box, points):
"""Update points locations according to new image size"""
left_x = box[0]
top_y = box[1]
right_x = box[2]
bottom_y = box[3]
width = right_x - left_x
height = bottom_y - top_y
# Shift points first.
for point in points:
point[0] -= left_x
point[1] -= top_y
# Then normalize the coordinates.
for point in points:
point[0] /= width
point[1] /= height
return points
def extract_face(image, points):
"""Extract face area from image and pts file."""
# Get a valid face area box.
valid_box = pt.get_valid_box(image, points)
if valid_box is None:
print("Opps, can not find valid box, using minimal box.")
valid_box = pt.get_minimal_box(points)
# Resize image if needed.
face_image = image[valid_box[1]:valid_box[3], valid_box[0]: valid_box[2]]
if (valid_box[2] - valid_box[0] != TARGET_SIZE) or (valid_box[3] - valid_box[1] != TARGET_SIZE):
face_image = cv2.resize(face_image, (TARGET_SIZE, TARGET_SIZE))
# And update points location.
valid_points = get_valid_points(valid_box, points)
return face_image, valid_points
def main():
"""The main entrance"""
# List all the pts files.
pts_file_list = []
for file_path, _, file_names in os.walk(DATA_DIR):
for file_name in file_names:
if file_name.split(".")[-1] in ["pts"]:
pts_file_list.append(os.path.join(file_path, file_name))
# Extract the image one by one. Use a dict to keep file count.
counter = {'invalid': 0}
for file_name in pts_file_list:
# Read points and image, make sure point importing goes well.
points = pt.read_points(file_name)
assert len(points) == 68, "The landmarks should contain 68 points."
image = read_image(file_name)
# Fast check invalid pts file.
if pt.points_are_valid(points, image) is False:
counter['invalid'] += 1
print("Invalid pts file, ignored:", file_name)
# Extract face image and new points.
face_image, points_normalized = extract_face(image, points)
# Mark the result
# points_restored = []
# for point in points_normalized:
# points_restored.append([point[0] * TARGET_SIZE, point[1] * TARGET_SIZE])
# pt.draw_landmark_point(face_image, points_restored)
# New file to be written.
head, tail = os.path.split(file_name)
subset_name = head.split('/')[-2]
common_file_name = tail.split('.')[-2]
common_url = os.path.join(
TARGET_DIR, "300vw-" + subset_name + "-" + common_file_name)
# Save the Image.
image_url = common_url + ".jpg"
cv2.imwrite(image_url, face_image)
# Save the new point location.
csv_url = common_url + ".json"
points_to_save = np.array(points_normalized).flatten()
with open(csv_url, mode='w') as file:
json.dump(list(points_to_save), file)
print("New file saved:", image_url, csv_url, sep='\n')
# Preive the result
cv2.imshow("Preview", face_image)
# All done, output debug info.
print("All done! Total file: {}, invalid: {}, succeed: {}".format(
len(pts_file_list), counter['invalid'],
len(pts_file_list) - counter['invalid']))
if __name__ == '__main__':