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import copy
import os
from pathlib import Path
import cv2
import numpy as np
import torch
from torch import nn
from facelib.detection.yolov5face.models.common import Conv
from facelib.detection.yolov5face.models.yolo import Model
from facelib.detection.yolov5face.utils.datasets import letterbox
from facelib.detection.yolov5face.utils.general import (
check_img_size,
non_max_suppression_face,
scale_coords,
scale_coords_landmarks,
)
def isListempty(inList):
if isinstance(inList, list): # Is a list
return all(map(isListempty, inList))
return False # Not a list
class YoloDetector:
def __init__(
self,
config_name,
min_face=10,
target_size=None,
device='cuda',
):
"""
config_name: name of .yaml config with network configuration from models/ folder.
min_face : minimal face size in pixels.
target_size : target size of smaller image axis (choose lower for faster work). e.g. 480, 720, 1080.
None for original resolution.
"""
self._class_path = Path(__file__).parent.absolute()
self.target_size = target_size
self.min_face = min_face
self.detector = Model(cfg=config_name)
self.device = device
def _preprocess(self, imgs):
"""
Preprocessing image before passing through the network. Resize and conversion to torch tensor.
"""
pp_imgs = []
for img in imgs:
h0, w0 = img.shape[:2] # orig hw
if self.target_size:
r = self.target_size / min(h0, w0) # resize image to img_size
if r < 1:
img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_LINEAR)
imgsz = check_img_size(max(img.shape[:2]), s=self.detector.stride.max()) # check img_size
img = letterbox(img, new_shape=imgsz)[0]
pp_imgs.append(img)
pp_imgs = np.array(pp_imgs)
pp_imgs = pp_imgs.transpose(0, 3, 1, 2)
pp_imgs = torch.from_numpy(pp_imgs).to(self.device)
pp_imgs = pp_imgs.float() # uint8 to fp16/32
return pp_imgs / 255.0 # 0 - 255 to 0.0 - 1.0
def _postprocess(self, imgs, origimgs, pred, conf_thres, iou_thres):
"""
Postprocessing of raw pytorch model output.
Returns:
bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2.
points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners).
"""
bboxes = [[] for _ in range(len(origimgs))]
landmarks = [[] for _ in range(len(origimgs))]
pred = non_max_suppression_face(pred, conf_thres, iou_thres)
for image_id, origimg in enumerate(origimgs):
img_shape = origimg.shape
image_height, image_width = img_shape[:2]
gn = torch.tensor(img_shape)[[1, 0, 1, 0]] # normalization gain whwh
gn_lks = torch.tensor(img_shape)[[1, 0, 1, 0, 1, 0, 1, 0, 1, 0]] # normalization gain landmarks
det = pred[image_id].cpu()
scale_coords(imgs[image_id].shape[1:], det[:, :4], img_shape).round()
scale_coords_landmarks(imgs[image_id].shape[1:], det[:, 5:15], img_shape).round()
for j in range(det.size()[0]):
box = (det[j, :4].view(1, 4) / gn).view(-1).tolist()
box = list(
map(int, [box[0] * image_width, box[1] * image_height, box[2] * image_width, box[3] * image_height])
)
if box[3] - box[1] < self.min_face:
continue
lm = (det[j, 5:15].view(1, 10) / gn_lks).view(-1).tolist()
lm = list(map(int, [i * image_width if j % 2 == 0 else i * image_height for j, i in enumerate(lm)]))
lm = [lm[i : i + 2] for i in range(0, len(lm), 2)]
bboxes[image_id].append(box)
landmarks[image_id].append(lm)
return bboxes, landmarks
def detect_faces(self, imgs, conf_thres=0.7, iou_thres=0.5):
"""
Get bbox coordinates and keypoints of faces on original image.
Params:
imgs: image or list of images to detect faces on with BGR order (convert to RGB order for inference)
conf_thres: confidence threshold for each prediction
iou_thres: threshold for NMS (filter of intersecting bboxes)
Returns:
bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2.
points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners).
"""
# Pass input images through face detector
images = imgs if isinstance(imgs, list) else [imgs]
images = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in images]
origimgs = copy.deepcopy(images)
images = self._preprocess(images)
with torch.inference_mode(): # change this with torch.no_grad() for pytorch <1.8 compatibility
pred = self.detector(images)[0]
bboxes, points = self._postprocess(images, origimgs, pred, conf_thres, iou_thres)
# return bboxes, points
if not isListempty(points):
bboxes = np.array(bboxes).reshape(-1,4)
points = np.array(points).reshape(-1,10)
padding = bboxes[:,0].reshape(-1,1)
return np.concatenate((bboxes, padding, points), axis=1)
else:
return None
def __call__(self, *args):
return self.predict(*args)
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