Machine learning - Yolo v7 Lobster orientation

import argparse

import time

from pathlib import Path

import math

import cv2

import torch

import torch.backends.cudnn as cudnn

from numpy import random

from models.experimental import attempt_load

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

from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel

def detect(save_img=False):

source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace

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://'))

# Directories

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

# Load model

model = attempt_load(weights, map_location=device) # load FP32 model

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

classify = False

if classify:

modelc = load_classifier(name='resnet101', n=2) # initialize

modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()

# 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)

else:

dataset = LoadImages(source, img_size=imgsz, stride=stride)

# Get names and colors

names = model.module.names if hasattr(model, 'module') else model.names

colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

# Run inference

if device.type != 'cpu':

model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once

old_img_w = old_img_h = imgsz

old_img_b = 1

t0 = time.time()

for path, img, im0s, vid_cap in dataset:

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 = model(img, augment=opt.augment)[0]

t2 = time_synchronized()

# Apply NMS

pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)

t3 = time_synchronized()

# Apply Classifier

if classify:

pred = apply_classifier(pred, modelc, img, im0s)

# 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(save_dir / 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

prev_conf_body = 0

prev_conf_head = 0

# Write results

for *xyxy, conf, cls in reversed(det):

if save_txt: # Write to file

xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh

line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format

with open(txt_path + '.txt', '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(label)

if names[int(cls)]=='Body' and prev_conf_body<conf.detach().cpu().numpy(): #getting the data of the body with maximum confidence

prev_conf_body = conf.detach().cpu().numpy()

label_body = f'{names[int(cls)]} {conf:.2f}'

center_x_body = int((xyxy[0]+xyxy[2])/2)

center_y_body = int((xyxy[1]+xyxy[3])/2)

if names[int(cls)]=='Head' and prev_conf_head<conf.detach().cpu().numpy(): #getting the data of the head with maximum confidence

prev_conf_head = conf.detach().cpu().numpy()

label_head = f'{names[int(cls)]} {conf:.2f}'

center_x_head = int((xyxy[0]+xyxy[2])/2)

center_y_head = int((xyxy[1]+xyxy[3])/2)

# print(xyxy[0],xyxy[1],xyxy[2],xyxy[3],center_x)

if prev_conf_body>0 and prev_conf_head>0:

orientation = math.atan2((center_y_head - center_y_body), (center_x_head - center_x_body)) #calculating the angle

degree = -orientation*(180/math.pi) #converting to degrees

cv2.arrowedLine(im0, (center_x_body ,center_y_body), (center_x_head ,center_y_head), (0,255,255), 5) #Draw the arrow

tl = round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1

cv2.putText(im0, str("%.2f" % round(degree, 2) + " degrees"), (center_x_body ,center_y_body), 0, tl/3, [0,255,255], thickness=1, lineType=cv2.LINE_AA) #write the degrees on image

# 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:

cv2.imshow(str(p), im0)

cv2.waitKey(1) # 1 millisecond

# Save results (image with detections)

if save_img:

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 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('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam

parser.add_argument('--img-size', 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, help='filter by class: --class 0, or --class 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')

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()

for *xyxy, conf, cls in reversed(det):

if save_txt: # Write to file

xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh

line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format

with open(txt_path + '.txt', '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(label)

if names[int(cls)]=='Body' and prev_conf_body<conf.detach().cpu().numpy(): #getting the data of the body with maximum confidence

prev_conf_body = conf.detach().cpu().numpy()

label_body = f'{names[int(cls)]} {conf:.2f}'

center_x_body = int((xyxy[0]+xyxy[2])/2)

center_y_body = int((xyxy[1]+xyxy[3])/2)

if names[int(cls)]=='Head' and prev_conf_head<conf.detach().cpu().numpy(): #getting the data of the head with maximum confidence

prev_conf_head = conf.detach().cpu().numpy()

label_head = f'{names[int(cls)]} {conf:.2f}'

center_x_head = int((xyxy[0]+xyxy[2])/2)

center_y_head = int((xyxy[1]+xyxy[3])/2)

# print(xyxy[0],xyxy[1],xyxy[2],xyxy[3],center_x)

if prev_conf_body>0 and prev_conf_head>0:

orientation = math.atan2((center_y_head - center_y_body), (center_x_head - center_x_body)) #calculating the angle

degree = -orientation*(180/math.pi) #converting to degrees

cv2.arrowedLine(im0, (center_x_body ,center_y_body), (center_x_head ,center_y_head), (0,255,255), 5) #Draw the arrow

tl = round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1

cv2.putText(im0, str("%.2f" % round(degree, 2) + " degrees"), (center_x_body ,center_y_body), 0, tl/3, [0,255,255], thickness=1, lineType=cv2.LINE_AA) #write the degrees on image