MTCNN-OpenCV
MTCNN Face Detector using OpenCV, no reqiurement for tensorflow/pytorch.
INSTALLATION
pip3 install opencv-pythonorpip3 install opencv-python-headlesspip3 install mtcnn-opencv
USAGE
import cv2
from mtcnn_cv2 import MTCNN
detector = MTCNN()
test_pic = "t.jpg"
image = cv2.cvtColor(cv2.imread(test_pic), cv2.COLOR_BGR2RGB)
result = detector.detect_faces(image)
# Result is an array with all the bounding boxes detected. Show the first.
print(result)
if len(result) > 0:
keypoints = result[0]['keypoints']
cv2.rectangle(image,
(bounding_box[0], bounding_box[1]),
(bounding_box[0]+bounding_box[2], bounding_box[1] + bounding_box[3]),
(0,155,255),
2)
cv2.circle(image,(keypoints['left_eye']), 2, (0,155,255), 2)
cv2.circle(image,(keypoints['right_eye']), 2, (0,155,255), 2)
cv2.circle(image,(keypoints['nose']), 2, (0,155,255), 2)
cv2.circle(image,(keypoints['mouth_left']), 2, (0,155,255), 2)
cv2.circle(image,(keypoints['mouth_right']), 2, (0,155,255), 2)
cv2.imwrite("result.jpg", cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
# 生成标记了的人脸的图片
with open(test_pic, "rb") as fp:
marked_data = detector.mark_faces(fp.read())
with open("marked.jpg", "wb") as fp:
fp.write(marked_data)
Metadata
Release files for mtcnn-opencv 1.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mtcnn_opencv-1.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Release files / mtcnn_opencv-1.0.2-py3-none-any.whl
| Download URL | mtcnn_opencv-1.0.2-py3-none-any.whl |
|---|---|
| Size | 1.9 MB |
| Tags | Python 3 |
|
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