import cv2 import torch import numpy as np import math from super_gradients.training import models from super_gradients.training.processing import (DetectionCenterPadding,StandardizeImage, ImagePermute, ComposeProcessing, DetectionLongestMaxSizeRescale) from deep_sort_pytorch.utils.parser import get_config from deep_sort_pytorch.deep_sort import DeepSort import streamlit as st file_path = 'coco-labels-paper.txt' palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1) names = [] with open(file_path, 'r') as file: for line in file: names.append(line.strip()) st.header(":hand: Welcome To YoLo Nas Object Detection and Tracking : ") st.info(""" This app uses the cutting-edge YOLO Nas algorithm to detect objects in real-time video streams. But that's not all it also employs the powerful DeepSort algorithm to track these objects, providing you with seamless tracking capabilities. Easily upload a video feed, and watch as our app identifies and tracks objects with precision. It's simple, efficient, and ready to help you monitor and analyze moving objects effortlessly! """) with st.sidebar : device_name =st.selectbox("Device : " , ["cpu" , "cuda"]) if device_name == 'cuda' : device = torch.device("cuda:0") else : device = torch.device("cpu") source_name = st.selectbox("select you source feed : " , ["URL"]) conf = st.slider("Select threshold confidence value : " , min_value=0.1 , max_value=1.0 , value=0.25) iou = st.slider("Select Intersection over union (iou) value : " , min_value=0.1 , max_value=1.0 , value=0.5) #model=models.get('yolo_nas_s',num_classes=len(names) , # checkpoint_path="yolo_nas_s_coco.pth").to(device) if source_name == "URL" : source = st.text_input("Input your Url Camera feed and press Entre ex : http://IP:8080/video") cap = cv2.VideoCapture(source) model=models.get('yolo_nas_s', pretrained_weights="coco").to(device) model.set_dataset_processing_params( class_names=names, image_processor=ComposeProcessing( [DetectionLongestMaxSizeRescale(output_shape=(636, 636)), DetectionCenterPadding(output_shape=(640, 640), pad_value=114), StandardizeImage(max_value=255.0), ImagePermute(permutation=(2, 0, 1)),]), iou=iou ,conf=conf) cfg_deep = get_config() cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml") deepsort = DeepSort(cfg_deep.DEEPSORT.REID_CKPT, max_dist=cfg_deep.DEEPSORT.MAX_DIST, min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE, nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE, max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT, nn_budget=cfg_deep.DEEPSORT.NN_BUDGET, use_cuda=False) def compute_color_for_labels(label): """ Simple function that adds fixed color depending on the class """ if label == 0: #person color = (85,45,255) elif label == 2: # Car color = (222,82,175) elif label == 3: # Motobike color = (0, 204, 255) elif label == 5: # Bus color = (0, 149, 255) else: color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette] return tuple(color) def draw_boxes(img, bbox, identities=None, categories=None, names=None, offset=(0,0)): for i, box in enumerate(bbox): x1, y1, x2, y2 = [int(i) for i in box] x1 += offset[0] x2 += offset[0] y1 += offset[0] y2 += offset[0] cat = int(categories[i]) if categories is not None else 0 id = int(identities[i]) if identities is not None else 0 cv2.rectangle(img, (x1, y1), (x2, y2), color= compute_color_for_labels(cat),thickness=2, lineType=cv2.LINE_AA) label = str(id) + ":" + names[cat] (w,h), _ = cv2.getTextSize(str(label), cv2.FONT_HERSHEY_SIMPLEX, fontScale=1/2, thickness=1) t_size=cv2.getTextSize(str(label), cv2.FONT_HERSHEY_SIMPLEX, fontScale=1/2, thickness=1)[0] c2=x1+t_size[0], y1-t_size[1]-3 cv2.rectangle(img, (x1, y1), c2, color=compute_color_for_labels(cat), thickness=-1, lineType=cv2.LINE_AA) cv2.putText(img, str(label), (x1, y1-2), 0, 1/2, [255, 255, 255], thickness=1, lineType=cv2.LINE_AA) return img if st.button("Start detection and Tracking") : frame_window = st.image( [] ) while True: xywh_bboxs = [] confs = [] oids = [] ret, frame = cap.read() if ret: result = list(model.predict(frame))[0] bbox_xyxys = result.prediction.bboxes_xyxy.tolist() confidences = result.prediction.confidence labels = result.prediction.labels.tolist() for (bbox_xyxy, confidence, cls) in zip(bbox_xyxys, confidences, labels): bbox = np.array(bbox_xyxy) x1, y1, x2, y2 = bbox[0], bbox[1], bbox[2], bbox[3] x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) conf = math.ceil((confidence*100))/100 cx, cy = int((x1+x2)/2), int((y1+y2)/2) bbox_width = abs(x1-x2) bbox_height = abs(y1-y2) xcycwh = [cx, cy, bbox_width, bbox_height] xywh_bboxs.append(xcycwh) confs.append(conf) oids.append(int(cls)) xywhs = torch.tensor(xywh_bboxs) confss= torch.tensor(confs) outputs = deepsort.update(xywhs, confss, oids, frame) if len(outputs)>0: bbox_xyxy = outputs[:,:4] identities = outputs[:, -2] object_id = outputs[:, -1] draw_boxes(frame, bbox_xyxy, identities, object_id , names=names) #output.write(frame) #cv2.imshow('Video', frame) #if cv2.waitKey(25) & 0xFF == ord('q'): # break frame = cv2.cvtColor( frame , cv2.COLOR_BGR2RGB) frame_window.image(frame) else: break