Upload app.py

app.py

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+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
+    #out.release()
+    cap.release()
+    cv2.destroyAllWindows()