Pure PyTorch MTCNN

MTCNN end-to-end in PyTorch, as a single module.

Supports batched inputs by adding a column to the bounding box matrix for batch index.

If you use CUDA, everything stays on device through end of inference.

Does NOT include file read/pipelining - implement this how you want.

Pretrained weights are included in mtcnn.pth.

Requirements

Python 3.7+

• numpy>=1.17.1
• torch>=1.3.1
• torchvision>=0.4.2

Installation

git clone https://github.com/galbiati/mtcnn.git && cd mtcnn && pip install -U . && cd ..

Preprocessing requirements

• Images must be dtype float32
• To normalize from uint8 image, do (image - 127.5) / 128
• Inputs must be batched and in (N, C, H, W) format
• Channel order should be RGB (watch out OpenCV users)

Model output structure

Each item in a batch may have a variable number of output bounding boxes, so a tensor maintaining a batch axis cannot be used. Instead, the model MTCNN will output a flattened matrix with the following stucture:

Size: [num_boxes, 20]

Each row is a single bounding box.

Column 0 is batch index.

Columns 1 - 4 are bounding box top left and bottom right coordinates.

Column 5 is score for that box.

Columns 6-10 are offset values

Columns 10-20 are landmark coordinates (same order as output by ONet)

Example

import cv2
import numpy as np
import torch

from mtcnn import MTCNN

# Config
device = torch.device('...')    # Whatever

# Load model - use `pretrained=False` if you did not pip install!
mtcnn = MTCNN(pretrained=True)
mtcnn.to(device)
mtcnn.eval()

# Placeholder data loading pipeline
path_to_test_image = '...'                                  # Whatever
image = cv2.imread(path_to_test_image, cv2.IMREAD_COLOR)    # unit8, BGR, HWC
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)              # Convert to RGB
image = (image.astype(np.float32) - 127.5) / 128            # Convert to float32 and normalize
image = image.transpose(2, 0, 1)                            # Switch to CHW

# Send to device as torch tensor, add batch axis, and run inference
image = torch.as_tensor(image, dtype=torch.float32, device=device).unsqueeze(0)
bounding_boxes = mtcnn(image)

Credit

Adapted from Dan Antoshchenko's implementation.