debugging app.py cctv

app.py

@@ -354,88 +354,88 @@ def check_database(ima):
 
 ###########################
 def video(vid):
-  print(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):  
@@ -601,7 +601,8 @@ home""")
       with gr.Column():
         fil1 = gr.File(type="file")
       with gr.Column():
-        vid2 = gr.Video()
+        # vid2 = gr.Video()
+          video_name = gr.Text(label="Video Upload")
     hbtn = gr.Button("Video")
-    hbtn.click(video, inputs=fil1, outputs=vid2)
+    hbtn.click(video, inputs=fil1, outputs=video_name)
 demo.launch()