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detect.py
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import argparse
import time
from pathlib import Path
import os
import sys
import matplotlib
# matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import cv2
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import yaml
# from models.experimental import attempt_load
from models.yolo import get_model
from models.finn_models import QuantC2f
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box, plot_images, output_to_target
from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
def detect(save_img=False):
source, weights, cfg, view_img, save_txt, imgsz, trace, mot_format, classes = opt.source, opt.weights, opt.cfg, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace, opt.mot_format, opt.classes
save_img = not opt.nosave and not source.endswith('.txt') # save inference images
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://', 'https://'))
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Initialize
set_logging()
device = select_device(opt.device)
# half = device.type != 'cpu' # half precision only supported on CUDA
half = False
# Load model
# model = attempt_load(weights, map_location=device) # load FP32 model
model, ckpt, _ = get_model(cfg, weights, num_classes=data_dict['nc'], device=device, load_ema=True)
model.eval()
for m in model.modules():
# print(type(m))
if isinstance(m, QuantC2f):
m.forward = m.forward_split
stride = int(model.stride.max()) # model stride
# imgsz = check_img_size(imgsz, s=stride) # check img_size
if trace:
model = TracedModel(model, device, opt.img_size)
if half:
model.half() # to FP16
# Second-stage classifier
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz, stride=stride)
# Get names and colors
# names = model.module.names if hasattr(model, 'module') else model.names
names = data_dict['names']
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
if len(imgsz) == 2:
old_img_h, old_img_w = imgsz
elif len(imgsz) == 1:
imgsz = imgsz[0]
old_img_h = old_img_w = imgsz
# Run inference
if device.type != 'cpu':
model(torch.zeros(1, 3, old_img_h, old_img_w).to(device).type_as(next(model.parameters()))) # run once
old_img_b = 1
if mot_format:
seqnames = os.listdir(source)
# seqnames = ["MVI_40701", "MVI_40771", "MVI_40863"]
for seqname in seqnames:
# if 'FRCNN' not in seqname:
# continue
seq_save_dir = save_dir / seqname
mot_txt_path = seq_save_dir / 'det' / 'det.txt'
savedimg_path = seq_save_dir / 'img'
os.makedirs(seq_save_dir / 'det')
if save_img:
os.makedirs(seq_save_dir / 'img')
dataset = LoadImages(os.path.join(source, seqname, 'img1'), img_size=imgsz, stride=stride)
t0 = time.time()
nms = non_max_suppression
last_layer = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]
if hasattr(last_layer, 'dedicated_nms'):
nms = last_layer.dedicated_nms
key = None
for frame_idx, (path, img, im0s, vid_cap) in enumerate(dataset):
print('img:', img.shape)
exp_dir = '/home/vision/danilowi/serious_mot/finn/notebooks/experiments/yolov8'
test_input_np = img.copy()
# cv2.imshow('img', img.transpose(1, 2, 0))
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Warmup
if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
old_img_b = img.shape[0]
old_img_h = img.shape[2]
old_img_w = img.shape[3]
for i in range(3):
model(img, augment=opt.augment)[0]
# Inference
t1 = time_synchronized()
with torch.no_grad(): # Calculating gradients would cause a GPU memory leak
pred, x = model(img, augment=opt.augment)
t2 = time_synchronized()
print('PRED:', pred.shape)
print(pred)
# Apply NMS
pred = nms(pred, opt.conf_thres, opt.iou_thres, agnostic=opt.agnostic_nms)
t3 = time_synchronized()
# plot_images(img, output_to_target(pred), [path], save_dir / "result.jpg", names)
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
else:
p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(savedimg_path / p.name) # img.jpg
# txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
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
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if mot_format:
xywh = [xyxy[0], xyxy[1], xyxy[2]-xyxy[0], xyxy[3]-xyxy[1]]
line = (frame_idx+1, -1, *xywh, conf, -1, -1, -1)
if not len(classes) or cls in classes:
with open(mot_txt_path, 'a') as f:
f.write(('%g,' * len(line)).rstrip(',') % line + '\n')
if save_img or view_img: # Add bbox to image
label = f'{names[int(cls)]} {conf:.2f}'
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
# Print time (inference + NMS)
# print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
# Stream results
if view_img:
# plt.imshow(im0)
# plt.show()
cv2.imshow('demo', im0)
key = cv2.waitKey(0)
if key == ord('v'):
np.save(exp_dir + '/test_input_{}x{}.npy'.format(test_input_np.shape[1], test_input_np.shape[2]), np.expand_dims(test_input_np, 0))
print("IMAGE SAVED")
if model.saved_features:
for i, f in enumerate(model.saved_features):
np.save(exp_dir + '/{}_f{}.npy'.format(opt.name, i), f.detach().cpu())
for out_idx, out in enumerate(x):
np.save(exp_dir + '/{}_trainout{}.npy'.format(opt.name, out_idx), out.detach().cpu())
print('FEATURES SAVED')
key = cv2.waitKey(0)
# Save results (image with detections)
if save_img:
cv2.imwrite(save_path, im0)
print(save_path, 'saved')
# if dataset.mode == 'image':
# cv2.imwrite(save_path, im0)
# # print(f" The image with the result is saved in: {save_path}")
# else: # 'video' or 'stream'
# if vid_path != save_path: # new video
# vid_path = save_path
# if isinstance(vid_writer, cv2.VideoWriter):
# vid_writer.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 += '.mp4'
# vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
# vid_writer.write(im0)
if key == ord('s') or key == ord('q'):
break
if key == ord('q'):
break
# if save_txt or save_img:
# s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
# #print(f"Results saved to {save_dir}{s}")
print(f'Done. ({time.time() - t0:.3f}s)')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)')
parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path (for classnames)')
parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', nargs='+', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
parser.add_argument('--classes', nargs='+', type=int, default=[], help='filter by class: --classes 0, or --classes 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default='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('--no-trace', action='store_true', help='don`t trace model')
parser.add_argument('--mot-format', action='store_true', help='output detections to txt file in mot format')
opt = parser.parse_args()
print(opt)
#check_requirements(exclude=('pycocotools', 'thop'))
with torch.no_grad():
if opt.update: # update all models (to fix SourceChangeWarning)
for opt.weights in ['yolov7.pt']:
detect()
strip_optimizer(opt.weights)
else:
detect()