adding detection models

IT19142838/realtime_detection2.py

+import cv2
+import torch
+from tracker import *
+import numpy as np
+from shapely.geometry import Polygon
+
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
+# model = torch.hub.load('.', 'custom', path='best.pt', source='local') 
+
+cap=cv2.VideoCapture('sample_video.mp4')
+# cap = cv2.VideoCapture(0)
+
+def POINTS(event, x, y, flags, param):
+    if event == cv2.EVENT_MOUSEMOVE :  
+        colorsBGR = [x, y]
+        print(colorsBGR)
+        
+def get_iou(box1, box2):
+    y11, x11, y21, x21 = box1
+    y12, x12, y22, x22 = box2
+    
+    yi1 = max(y11, y12)
+    xi1 = max(x11, x12)
+    yi2 = min(y21, y22)
+    xi2 = min(x21, x22)
+    inter_area = max(((xi2 - xi1) * (yi2 - yi1)), 0)
+    # Calculate the Union area by using Formula: Union(A,B) = A + B - Inter(A,B)
+    box1_area = (x21 - x11) * (y21 - y11)
+    box2_area = (x22 - x12) * (y22 - y12)
+    print("box1_area: ",box1_area)
+    union_area = box1_area + box2_area - inter_area
+    # compute the IoU
+    iou = inter_area / union_area
+    return iou
+
+cv2.namedWindow('FRAME')
+cv2.setMouseCallback('FRAME', POINTS)
+
+tracker = Tracker()
+countingRegion = [(151,159),(933,173),(933,437), (127,413)]
+
+objectCountSet = set()
+while True:
+    ret,frame=cap.read()
+    frame=cv2.resize(frame,(1020,500))
+
+    cv2.polylines(frame, [np.array(countingRegion,np.int32)], True, (0,255,0), 2)
+    # Make detections 
+    results = model(frame)
+    # a = results.pandas().xyxy[0]
+    # print(a)
+
+    # creating a tracker list
+    trackerList = []
+    iterateCounter, overCrowdCounter = 0, 0
+    for index, row in results.pandas().xyxy[0].iterrows():
+        x1 = int(row['xmin'])
+        y1 = int(row['ymin'])
+        x2 = int(row['xmax'])
+        y2 = int(row['ymax'])
+        objectName = str(row['name'])
+        
+        # print("trackerlist: ",trackerList)
+        # tracking overlapping bounding boxes
+
+        if len(trackerList) == 0:
+            trackerList.append([x1,y1,x2,y2])
+        else:
+            for i in trackerList:
+                value = get_iou([x1,y1,x2,y2], i)
+                if value > 0.8:
+                    print("the iou value: ",value)
+                    trackerList.remove(i)
+                else:
+                    trackerList.append([x1,y1,x2,y2])
+        # creating box id
+        boxIds = tracker.update(trackerList)
+        # print("boxids: ",boxIds)
+        print("Object name: ",objectName)
+        print("points: [",x1,y1,x2,y2,"]")
+        for id in boxIds:
+            x,y,w,h,id = id
+            cv2.rectangle(frame, (x,y), (w,h), (0,0,0),2)
+            cv2.putText(frame, str(row['name']), (x,y), cv2.FONT_HERSHEY_PLAIN,1, (255,255,255))
+            count=cv2.pointPolygonTest(np.array(countingRegion,np.int32), (int(w), int(h)), False)
+            if count > 0:
+                objectCountSet.add(id)
+        # print(len(objectCountSet))
+        cv2.putText(frame, str(objectCountSet), (78,52), cv2.FONT_HERSHEY_PLAIN,2, (255,0,0))
+    if len(objectCountSet) > 5:
+        cv2.putText(frame, "Too many objects", (78,52), cv2.FONT_HERSHEY_PLAIN,2, (255,0,0))
+        print("Too many objects")
+        overCrowdCounter += 1
+        if overCrowdCounter > 5:
+            cv2.putText(frame, "Over Crowded too long", (78,90), cv2.FONT_HERSHEY_PLAIN,2, (255,0,0))
+            print("Over Crowded too long")
+    
+    objectCountSet = set()
+        # cv2.rectangle(frame, (x1,y1), (x2,y2), (0,0,0),2)
+        # cv2.putText(frame, objectName, (x1,y1), cv2.FONT_HERSHEY_PLAIN,1, (255,255,255))
+
+    cv2.imshow('FRAME',frame)
+    if cv2.waitKey(10) & 0xFF == ord('q'):
+        break
+cap.release()
+cv2.destroyAllWindows()
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