Testing cctv video

app.py

@@ -354,88 +354,88 @@ def check_database(ima):
 
 ###########################
 def video(vid):
-  return f'Uploaded video name: {vid.name}'  
-#   file = '../../../../..'+vid.name
-#   print(f'file: {file}')
-#   # file = vid
-#   video = cv2.VideoCapture(file)
-#   # video.set(cv2.CAP_PROP_FPS, 10)
-#   if (video.isOpened() == False):
-#     print("Error reading video file")
-#   frame_width = int(video.get(3))
-#   frame_height = int(video.get(4))
-#   size = (frame_width, frame_height)
-
-# # # Below VideoWriter object will create
-# # # a frame of above defined The output
-# # # is stored in 'filename.avi' file.
-#   result = cv2.VideoWriter('filename.mp4',
-#               cv2.VideoWriter_fourcc(*'mp4v'),
-#               10, size)
+  # return f'Uploaded video name: {vid.name}'  
+  file = vid.name
+  print(f'file: {file}')
+  # file = vid
+  video = cv2.VideoCapture(file)
+  # video.set(cv2.CAP_PROP_FPS, 10)
+  if (video.isOpened() == False):
+    print("Error reading video file")
+  frame_width = int(video.get(3))
+  frame_height = int(video.get(4))
+  size = (frame_width, frame_height)
+
+# # Below VideoWriter object will create
+# # a frame of above defined The output
+# # is stored in 'filename.avi' file.
+  result = cv2.VideoWriter('filename.mp4',
+              cv2.VideoWriter_fourcc(*'mp4v'),
+              10, size)
     
-#   while(True):
-#     ret, frame = video.read()
-#     if ret == True:
-
-#       rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-#       faces = faceCascade.detectMultiScale(rgb,
-#                                           scaleFactor=1.1,
-#                                           minNeighbors=5,
-#                                           minSize=(60, 60),
-#                                           flags=cv2.CASCADE_SCALE_IMAGE)
+  while(True):
+    ret, frame = video.read()
+    if ret == True:
+
+      rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+      faces = faceCascade.detectMultiScale(rgb,
+                                          scaleFactor=1.1,
+                                          minNeighbors=5,
+                                          minSize=(60, 60),
+                                          flags=cv2.CASCADE_SCALE_IMAGE)
   
-#       # convert the input frame from BGR to RGB 
+      # convert the input frame from BGR to RGB 
       
-#       # the facial embeddings for face in input
-#       encodings = face_recognition.face_encodings(rgb)
-#       names = []
-#       # loop over the facial embeddings incase
-#       # we have multiple embeddings for multiple fcaes
-#       for encoding in encodings:
-#       #Compare encodings with encodings in data["encodings"]
-#       #Matches contain array with boolean values and True for the embeddings it matches closely
-#       #and False for rest
-#           matches = face_recognition.compare_faces(data["encodings"],
-#           encoding)
-#           #set name =inknown if no encoding matches
-#           name = "Unknown"
-#           # check to see if we have found a match
-#           if True in matches:
-#               #Find positions at which we get True and store them
-#               matchedIdxs = [i for (i, b) in enumerate(matches) if b]
-#               counts = {}
-#               # loop over the matched indexes and maintain a count for
-#               # each recognized face face
-#               for i in matchedIdxs:
-#                   #Check the names at respective indexes we stored in matchedIdxs
-#                   name = data["names"][i]
-#                   #increase count for the name we got
-#                   counts[name] = counts.get(name, 0) + 1
-#               #set name which has highest count
-#               name = max(counts, key=counts.get)
+      # the facial embeddings for face in input
+      encodings = face_recognition.face_encodings(rgb)
+      names = []
+      # loop over the facial embeddings incase
+      # we have multiple embeddings for multiple fcaes
+      for encoding in encodings:
+      #Compare encodings with encodings in data["encodings"]
+      #Matches contain array with boolean values and True for the embeddings it matches closely
+      #and False for rest
+          matches = face_recognition.compare_faces(data["encodings"],
+          encoding)
+          #set name =inknown if no encoding matches
+          name = "Unknown"
+          # check to see if we have found a match
+          if True in matches:
+              #Find positions at which we get True and store them
+              matchedIdxs = [i for (i, b) in enumerate(matches) if b]
+              counts = {}
+              # loop over the matched indexes and maintain a count for
+              # each recognized face face
+              for i in matchedIdxs:
+                  #Check the names at respective indexes we stored in matchedIdxs
+                  name = data["names"][i]
+                  #increase count for the name we got
+                  counts[name] = counts.get(name, 0) + 1
+              #set name which has highest count
+              name = max(counts, key=counts.get)
   
   
-#           # update the list of names
-#           names.append(name)
-#           # loop over the recognized faces
-#           for ((x, y, w, h), name) in zip(faces, names):
-#               # rescale the face coordinates
-#               # draw the predicted face name on the image
-#               cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-#               cv2.putText(frame, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX,
-#               0.75, (0, 255, 0), 2)
-#       result.write(frame)
-#       # cv2_imshow(frame)
-#       if cv2.waitKey(1) & 0xFF == ord('q'):
-#           break
-
-#     # Break the loop
-#     else:
-#       break
-
-
-#   # print("The video was successfully saved")
-#   return 'filename.mp4'
+          # update the list of names
+          names.append(name)
+          # loop over the recognized faces
+          for ((x, y, w, h), name) in zip(faces, names):
+              # rescale the face coordinates
+              # draw the predicted face name on the image
+              cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+              cv2.putText(frame, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX,
+              0.75, (0, 255, 0), 2)
+      result.write(frame)
+      # cv2_imshow(frame)
+      if cv2.waitKey(1) & 0xFF == ord('q'):
+          break
+
+    # Break the loop
+    else:
+      break
+
+
+  # print("The video was successfully saved")
+  return 'filename.mp4'
 
 #################
 def generate_prompt(AG,facftop,facfmid,facfbot):  
@@ -601,8 +601,8 @@ home""")
       with gr.Column():
         fil1 = gr.File(type="file")
       with gr.Column():
-        # vid2 = gr.Video()
-          video_name = gr.Text(label="Video Upload")
+        vid2 = gr.Video()
+          # video_name = gr.Text(label="Video Upload")
     hbtn = gr.Button("Video")
-    hbtn.click(video, inputs=fil1, outputs=video_name)
+    hbtn.click(video, inputs=fil1, outputs=vid2)
 demo.launch()