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detect_face.py
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# -*- coding: UTF-8 -*-
import argparse
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import copy
from models.experimental import attempt_load
from utils.datasets import letterbox
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
from cv2 import getTickCount, getTickFrequency
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])
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)]
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_one(model, image_path, device):
# Load model
img_size = 800
conf_thres = 0.3
iou_thres = 0.5
orgimg = cv2.imread(image_path) # BGR
img0 = copy.deepcopy(orgimg)
assert orgimg is not None, 'Image Not Found ' + image_path
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
img = img[:, :, ::-1].transpose(2, 0, 1).copy() # BGR to RGB, to 3x416x416
# Run inference
t0 = time.time()
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
t1 = time_synchronized()
pred = model(img)[0]
# Apply NMS
pred = non_max_suppression_face(pred, conf_thres, iou_thres)
print('img.shape: ', img.shape)
print('orgimg.shape: ', orgimg.shape)
return pred, img, orgimg
def detect_video(model, frame, device):
# Load model
img_size = 800
conf_thres = 0.3
iou_thres = 0.5
# orgimg = cv2.imread(frame) # BGR
orgimg = frame
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
img = img[:, :, ::-1].transpose(2, 0, 1).copy() # BGR to RGB, to 3x416x416
# Run inference
t0 = time.time()
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
t1 = time_synchronized()
pred = model(img)[0]
# Apply NMS
pred = non_max_suppression_face(pred, conf_thres, iou_thres)
return pred, img, orgimg
def detector(weights, detect_type):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('device', device)
model = load_model(weights, device)
if detect_type == 'video':
cap=cv2.VideoCapture('./demo03.m4v')
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
count_frame = cap.get(cv2.CAP_PROP_FRAME_COUNT)
fps = cap.get(cv2.CAP_PROP_FPS) #获取视频的帧率
print('height and width', height, width)
print('frame num', count_frame)
print('fps', fps)
# fourcc = int(cap.get(cv2.CAP_PROP_FOURCC))
fourcc = cv2.VideoWriter_fourcc('M','J','P','G')
#定义视频对象输出
writer = cv2.VideoWriter("./video_result.mp4", fourcc, fps, (width, height))
while cap.isOpened():
loop_start = getTickCount()
ret, frame = cap.read()
if ret:
srcimg = detect_video(model, frame, device)
key = cv2.waitKey(24)
loop_time = getTickCount() - loop_start
total_time = loop_time / (getTickFrequency())
fps = 1 / total_time
print('fps', fps)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(srcimg, 'fps'+str(int(fps)), (50, 50), font, 1, (0, 255, 0), 2)
writer.write(srcimg)
else:
break
if key == ord('q'):
break
cap.release()
writer.release()
cv2.destroyAllWindows()