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detect_face.py
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detect_face.py
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# -*- coding: UTF-8 -*-
import argparse
import time
from pathlib import Path
import sys
import os
import numpy as np
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import copy
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from models.experimental import attempt_load
from utils.datasets import letterbox, img_formats, vid_formats, LoadImages, LoadStreams
from utils.general import check_img_size, non_max_suppression_face, apply_classifier, scale_coords, xyxy2xywh, \
strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
def load_model(weights, device):
model = attempt_load(weights, map_location=device) # load FP32 model
return model
def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None):
# Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
coords[:, [0, 2, 4, 6, 8]] -= pad[0] # x padding
coords[:, [1, 3, 5, 7, 9]] -= pad[1] # y padding
coords[:, :10] /= gain
#clip_coords(coords, img0_shape)
coords[:, 0].clamp_(0, img0_shape[1]) # x1
coords[:, 1].clamp_(0, img0_shape[0]) # y1
coords[:, 2].clamp_(0, img0_shape[1]) # x2
coords[:, 3].clamp_(0, img0_shape[0]) # y2
coords[:, 4].clamp_(0, img0_shape[1]) # x3
coords[:, 5].clamp_(0, img0_shape[0]) # y3
coords[:, 6].clamp_(0, img0_shape[1]) # x4
coords[:, 7].clamp_(0, img0_shape[0]) # y4
coords[:, 8].clamp_(0, img0_shape[1]) # x5
coords[:, 9].clamp_(0, img0_shape[0]) # y5
return coords
def show_results(img, xyxy, conf, landmarks, class_num):
h,w,c = img.shape
tl = 1 or round(0.002 * (h + w) / 2) + 1 # line/font thickness
x1 = int(xyxy[0])
y1 = int(xyxy[1])
x2 = int(xyxy[2])
y2 = int(xyxy[3])
img = img.copy()
cv2.rectangle(img, (x1,y1), (x2, y2), (0,255,0), thickness=tl, lineType=cv2.LINE_AA)
clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)]
for i in range(5):
point_x = int(landmarks[2 * i])
point_y = int(landmarks[2 * i + 1])
cv2.circle(img, (point_x, point_y), tl+1, clors[i], -1)
tf = max(tl - 1, 1) # font thickness
label = str(conf)[:5]
cv2.putText(img, label, (x1, y1 - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
return img
def detect(
model,
source,
device,
project,
name,
exist_ok,
save_img,
view_img
):
# Load model
img_size = 640
conf_thres = 0.6
iou_thres = 0.5
imgsz=(640, 640)
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
Path(save_dir).mkdir(parents=True, exist_ok=True) # make dir
is_file = Path(source).suffix[1:] in (img_formats + vid_formats)
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
# Dataloader
if webcam:
print('loading streams:', source)
dataset = LoadStreams(source, img_size=imgsz)
bs = 1 # batch_size
else:
print('loading images', source)
dataset = LoadImages(source, img_size=imgsz)
bs = 1 # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs
for path, im, im0s, vid_cap in dataset:
if len(im.shape) == 4:
orgimg = np.squeeze(im.transpose(0, 2, 3, 1), axis= 0)
else:
orgimg = im.transpose(1, 2, 0)
orgimg = cv2.cvtColor(orgimg, cv2.COLOR_BGR2RGB)
img0 = copy.deepcopy(orgimg)
h0, w0 = orgimg.shape[:2] # orig hw
r = img_size / max(h0, w0) # resize image to img_size
if r != 1: # always resize down, only resize up if training with augmentation
interp = cv2.INTER_AREA if r < 1 else cv2.INTER_LINEAR
img0 = cv2.resize(img0, (int(w0 * r), int(h0 * r)), interpolation=interp)
imgsz = check_img_size(img_size, s=model.stride.max()) # check img_size
img = letterbox(img0, new_shape=imgsz)[0]
# Convert from w,h,c to c,w,h
img = img.transpose(2, 0, 1).copy()
img = torch.from_numpy(img).to(device)
img = img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
pred = model(img)[0]
# Apply NMS
pred = non_max_suppression_face(pred, conf_thres, iou_thres)
print(len(pred[0]), 'face' if len(pred[0]) == 1 else 'faces')
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, im0, frame = path[i], im0s[i].copy(), dataset.count
else:
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(Path(save_dir) / p.name) # im.jpg
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
det[:, 5:15] = scale_coords_landmarks(img.shape[2:], det[:, 5:15], im0.shape).round()
for j in range(det.size()[0]):
xyxy = det[j, :4].view(-1).tolist()
conf = det[j, 4].cpu().numpy()
landmarks = det[j, 5:15].view(-1).tolist()
class_num = det[j, 15].cpu().numpy()
im0 = show_results(im0, xyxy, conf, landmarks, class_num)
if view_img:
cv2.imshow('result', im0)
k = cv2.waitKey(1)
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].release() # release previous video writer
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
try:
vid_writer[i].write(im0)
except Exception as e:
print(e)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='runs/train/exp5/weights/last.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='0', help='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--save-img', action='store_true', help='save results')
parser.add_argument('--view-img', action='store_true', help='show results')
opt = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = load_model(opt.weights, device)
detect(model, opt.source, device, opt.project, opt.name, opt.exist_ok, opt.save_img, opt.view_img)