Create app.py

app.py

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+import cv2
+import gradio as gr
+from google.colab.patches import cv2_imshow
+thres = 0.45 # Threshold to detect object
+
+
+def Detection(filename):
+  cap = cv2.VideoCapture(filename)
+
+
+  cap.set(3,1280)
+  cap.set(4,720)
+  cap.set(10,70)
+
+  error="NoneType' object has no attribute"
+  classNames= []
+  FinalItems=[]
+  classFile = 'coco.names'
+  with open(classFile,'rt') as f:
+    #classNames = f.read().rstrip('n').split('n')
+    classNames = f.readlines()
+
+
+  # remove new line characters
+  classNames = [x.strip() for x in classNames]
+  print(classNames)
+  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()
+      try:
+          classIds, confs, bbox = net.detect(img,confThreshold=thres)
+      except:
+          pass
+      print(classIds,bbox)
+      try:
+        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)
+                if FinalItems.count(classNames[classId-1]) == 0:
+                  FinalItems.append(classNames[classId-1])
+              
+        
+        cv2_imshow(img)
+        cv2.waitKey(10)
+      except  Exception as err:
+        print(err)
+        t=str(err)
+        if t.__contains__(error):
+          break
+      
+
+  print(FinalItems)
+  return str(FinalItems)
+
+
+interface = gr.Interface(fn=Detection, 
+                        inputs=["video"],
+                         outputs="text", 
+                        title='Object Detection in Video')
+interface.launch()