animal detection

animalDetection/code.py

+import cv2
+thres = 0.45
+
+cap = cv2.VideoCapture('test.mp4')
+cap.set(3,1280)
+cap.set(4,720)
+cap.set(10,70)
+
+classNames= []
+classFile = 'coco.names'
+with open(classFile,'rt') as f:
+    classNames = f.read().rstrip('n').split('n')
+
+configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
+weightsPath = 'frozen_inference_graph.pb'
+
+net = cv2.dnn_DetectionModel(weightsPath,configPath)
+net.setInputSize(320,320)
+net.setInputScale(1.0/ 127.5)
+net.setInputMean((127.5, 127.5, 127.5))
+net.setInputSwapRB(True)
+
+while True:
+    success,img = cap.read()
+    classIds, confs, bbox = net.detect(img,confThreshold=thres)
+    print(classIds,bbox)
+
+    if len(classIds) != 0:
+        for classId, confidence,box in zip(classIds.flatten(),confs.flatten(),bbox):
+            cv2.rectangle(img,box,color=(0,255,0),thickness=2)
+            # cv2.putText(img,classNames[classId-1].upper(),(box[0]+10,box[1]+30),
+            # cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
+            # cv2.putText(img,str(round(confidence*100,2)),(box[0]+200,box[1]+30),
+            # cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)
+
+    cv2.imshow('Output',img)
+    cv2.waitKey(1)
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