Initial commit.

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README.md

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+## Crop eye pair from face image using HAAR cascade and/or MTCNN
+-------------
+
+### How to run?
+1. Clone this repository.
+2. Install requirements: `pip install -r requirements.txt`
+3. If input face images are in directory `example/input/dir` and output direcotry `example/output/dir` then run the following command which will preserve input directory structure for further tasks.
+
+    ```
+    python crop_eye_pair.py --input_dir 'example/input/dir' --output_dir 'example/output/dir' --method 'mtcnn' --device 'cuda'
+    ```
+
+4. Supported `method` options: `haar-cascade` and `mtcnn`
+5. Supported `device` options: `cpu` and `cuda`. 
+
+Notes: Setting device to `cuda` has no effect while using `haar-cascade` method.
+
+
+## Credits
+
+1. MTCNN code borrowed from https://github.com/ipazc/mtcnn
+2. HAAR Cascade code inspired from https://github.com/zekeriyafince/EyePair

crop_eye_pair.py

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+import numpy as np
+import cv2
+import glob
+import argparse
+import os
+import re
+import logging
+import tqdm
+
+# global variable for mtcnn detector
+mtcnn_detector = None
+
+# global variables for cascade detector
+face_cascade = None
+eye_pair_cascade = None
+
+def get_eye_pair_mtcnn(image):
+
+	image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+	result = mtcnn_detector.detect_faces(image_rgb)
+
+	if not result: return (False, False)
+
+	bounding_box = result[0]['box']
+	keypoints = result[0]['keypoints']
+
+	left_eye = keypoints['left_eye']
+	right_eye = keypoints['right_eye']
+
+	y_start = min(left_eye[1], right_eye[1])
+	y_end = max(left_eye[1], right_eye[1])
+
+	x_start = min(left_eye[0], right_eye[0])
+	x_end = max(left_eye[0], right_eye[0])
+
+	image = image[y_start-15:y_end+15, x_start-10:x_end+10]
+
+	return (True, image)
+
+def get_eye_pair_opencv(image):
+
+	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+	faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+	for x, y, w, h in faces: # face points
+		roi_gray = gray[y:y + h, x:x + w]
+		roi_color = image[y:y + h, x:x + w]
+		eye_pairs = eye_pair_cascade.detectMultiScale(roi_gray)
+
+		for (ex, ey, ew, eh) in eye_pairs: # eye_pair points
+			roi_eyes = roi_color[ex:min(roi_color.shape[0], ex+ew*2), max(0, ey-eh//2):min(roi_color.shape[1], ey+eh)]
+			return (True, roi_eyes)
+
+	return (False, False)
+
+def save_eye_pair(input_path, output_path, method='mtcnn'):
+	image = cv2.imread(input_path)
+	if method == 'mtcnn':
+		found, pair = get_eye_pair_mtcnn(image)
+	else:
+		found, pair = get_eye_pair_opencv(image)
+
+	if found:
+		os.makedirs(os.path.split(output_path)[0], exist_ok = True)
+		cv2.imwrite(output_path, pair)
+
+def main(args):
+
+	if args.device == 'cuda':
+		os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+	else:
+		os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+	if args.method == 'mtcnn':
+		global mtcnn_detector
+		os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # ERROR
+		logging.getLogger('tensorflow').setLevel(logging.ERROR)
+		from mtcnn import MTCNN
+		mtcnn_detector = MTCNN()
+	else:
+		global face_cascade, eye_pair_cascade
+		face_cascade = cv2.CascadeClassifier('./haar-cascade/haarcascade_frontalface_default.xml') # face 
+		eye_pair_cascade = cv2.CascadeClassifier('./haar-cascade/haarcascades_haarcascade_mcs_eyepair_big.xml') #eye_pair
+
+	for input_path in tqdm.tqdm(glob.glob(args.input_dir + "/**/*.jpg", recursive=True)):
+		save_eye_pair(input_path, os.path.join(args.output_dir, re.sub('^' + args.input_dir, '', input_path)), method=args.method)
+		break
+
+
+# python crop_eye_pair.py --input_dir images/ --output_dir eye_pair_images/ --method mtcnn
+if __name__ == '__main__':
+	parser = argparse.ArgumentParser(description='crop eye pair from face image')
+	parser.add_argument('--input_dir', default='', type=str, help='')
+	parser.add_argument('--output_dir', default='', type=str, help='')
+	parser.add_argument('--method', default='mtcnn', type=str, help='mtcnn or cascade')
+	parser.add_argument('--device', default='cpu', type=str, help='cuda or cpu')
+	args = parser.parse_args()
+
+	main(args)