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Upload mask_the_face.py
Browse files- mask_the_face.py +132 -0
mask_the_face.py
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# Author: aqeelanwar
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# Created: 27 April,2020, 10:22 PM
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# Email: [email protected]
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import argparse
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import dlib
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from aux_functions import *
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import numpy as np
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def maskThisImages(myImg):
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# Command-line input setup
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parser = argparse.ArgumentParser(
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description="MaskTheFace - Python code to mask faces dataset"
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)
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parser.add_argument(
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"--path",
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type=str,
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default="",
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help="Path to either the folder containing images or the image itself",
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)
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parser.add_argument(
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"--mask_type",
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type=str,
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default="surgical",
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choices=["surgical", "N95", "KN95", "cloth", "gas", "inpaint", "random", "all"],
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help="Type of the mask to be applied. Available options: all, surgical_blue, surgical_green, N95, cloth",
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)
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parser.add_argument(
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"--pattern",
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type=str,
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default="",
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help="Type of the pattern. Available options in masks/textures",
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)
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parser.add_argument(
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"--pattern_weight",
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type=float,
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default=0.5,
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help="Weight of the pattern. Must be between 0 and 1",
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)
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parser.add_argument(
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"--color",
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type=str,
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default="cyan",
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help="Hex color value that need to be overlayed to the mask",
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)
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parser.add_argument(
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"--color_weight",
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type=float,
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default=0.5,
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help="Weight of the color intensity. Must be between 0 and 1",
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)
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parser.add_argument(
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"--code",
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type=str,
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# default="cloth-masks/textures/check/check_4.jpg, cloth-#e54294, cloth-#ff0000, cloth, cloth-masks/textures/others/heart_1.png, cloth-masks/textures/fruits/pineapple.png, N95, surgical_blue, surgical_green",
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default="",
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help="Generate specific formats",
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)
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parser.add_argument(
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"--verbose", dest="verbose", action="store_true", help="Turn verbosity on"
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)
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parser.add_argument(
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"--write_original_image",
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dest="write_original_image",
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action="store_true",
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help="If true, original image is also stored in the masked folder",
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)
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parser.set_defaults(feature=False)
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args, unknown = parser.parse_known_args()
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args.write_path = args.path + "_masked"
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# Set up dlib face detector and predictor
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args.detector = dlib.get_frontal_face_detector()
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path_to_dlib_model = "shape_predictor_68_face_landmarks.dat"
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if not os.path.exists(path_to_dlib_model):
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download_dlib_model()
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args.predictor = dlib.shape_predictor(path_to_dlib_model)
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# Extract data from code
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mask_code = "".join(args.code.split()).split(",")
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args.code_count = np.zeros(len(mask_code))
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args.mask_dict_of_dict = {}
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for i, entry in enumerate(mask_code):
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print
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mask_dict = {}
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mask_color = ""
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mask_texture = ""
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mask_type = entry.split("-")[0]
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if len(entry.split("-")) == 2:
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mask_variation = entry.split("-")[1]
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if "#" in mask_variation:
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mask_color = mask_variation
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else:
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mask_texture = mask_variation
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mask_dict["type"] = mask_type
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mask_dict["color"] = mask_color
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mask_dict["texture"] = mask_texture
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args.mask_dict_of_dict[i] = mask_dict
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# Check if path is file or directory or none
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is_file=True
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# Process if the path was a file
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if is_file:
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print("Masking image file")
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image_path = args.path
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write_path = args.path.rsplit(".")[0]
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if True:
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# Proceed if file is image
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# masked_images, mask, mask_binary_array, original_image
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masked_image, mask, mask_binary_array, original_image = mask_image(
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myImg, args
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)
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if len(masked_image)==0:
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return masked_image
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else:
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img = masked_image[i]
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return img
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else:
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print("Path is neither a valid file or a valid directory")
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print("Processing Done")
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