Delete inference_video.py

inference_video.py

@@ -1,198 +0,0 @@
-import cv2
-import numpy as np
-import glob
-from os.path import isfile, join
-import subprocess
-import os
-import shutil
-from io import BytesIO
-import io
-from RealESRGAN import RealESRGAN
-import torch
-from PIL import Image
-import numpy as np
-
-
-IMAGE_FORMATS = ('.png', '.jpg', '.jpeg', '.tiff', '.bmp', '.gif')
-
-
-def inference_image(image, size):
-    global model2
-    global model4
-    global model8
-    if image is None:
-        raise gr.Error("Image not uploaded")
-
-    width, height = image.size
-    if width >= 5000 or height >= 5000:
-        raise gr.Error("The image is too large.")
-
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-
-    if size == '2x':
-        try:
-            result = model2.predict(image.convert('RGB'))
-        except torch.cuda.OutOfMemoryError as e:
-            print(e)
-            model2 = RealESRGAN(device, scale=2)
-            model2.load_weights('weights/RealESRGAN_x2.pth', download=False)
-            result = model2.predict(image.convert('RGB'))
-    elif size == '4x':
-        try:
-            result = model4.predict(image.convert('RGB'))
-        except torch.cuda.OutOfMemoryError as e:
-            print(e)
-            model4 = RealESRGAN(device, scale=4)
-            model4.load_weights('weights/RealESRGAN_x4.pth', download=False)
-            result = model2.predict(image.convert('RGB'))
-    else:
-        try:
-            result = model8.predict(image.convert('RGB'))
-        except torch.cuda.OutOfMemoryError as e:
-            print(e)
-            model8 = RealESRGAN(device, scale=8)
-            model8.load_weights('weights/RealESRGAN_x8.pth', download=False)
-            result = model2.predict(image.convert('RGB'))
-
-    print(f"Frame of the Video size ({device}): {size} ... OK")
-    return result
-
-
-
-# assign directory
-directory = 'videos' #PATH_WITH_INPUT_VIDEOS
-zee = 0
-
-def convert_frames_to_video(pathIn,pathOut,fps):
-    global INPUT_DIR
-    cap = cv2.VideoCapture(f'/{INPUT_DIR}/videos/input.mp4')
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    frame_array = []
-    files = [f for f in os.listdir(pathIn) if isfile(join(pathIn, f))]
-    #for sorting the file names properly
-    files.sort(key = lambda x: int(x[5:-4]))
-    size2 = (0,0)
-
-    for i in range(len(files)):
-        filename=pathIn + files[i]
-        #reading each files
-        img = cv2.imread(filename)
-        height, width, layers = img.shape
-        size = (width,height)
-        size2 = size
-        print(filename)
-        #inserting the frames into an image array
-        frame_array.append(img)
-    out = cv2.VideoWriter(pathOut,cv2.VideoWriter_fourcc(*'DIVX'), fps, size2)
-    for i in range(len(frame_array)):
-        # writing to a image array
-        out.write(frame_array[i])
-    out.release()
-
-
-for filename in os.listdir(directory):
-
-    f = os.path.join(directory, filename)
-    # checking if it is a file
-    if os.path.isfile(f):
-
-
-      print("PROCESSING :"+str(f)+"\n")
-      # Read the video from specified path
-
-      #video to frames
-      cam = cv2.VideoCapture(str(f))
-
-      try:
-
-          # PATH TO STORE VIDEO FRAMES
-          if not os.path.exists(f'/{INPUT_DIR}/upload/'):
-              os.makedirs(f'/{INPUT_DIR}/upload/')
-
-      # if not created then raise error
-      except OSError:
-          print ('Error: Creating directory of data')
-
-      # frame
-      currentframe = 0
-
-
-      while(True):
-
-          # reading from frame
-          ret,frame = cam.read()
-
-          if ret:
-              # if video is still left continue creating images
-              name = f'/{INPUT_DIR}/upload/frame' + str(currentframe) + '.jpg'
-
-              # writing the extracted images
-              cv2.imwrite(name, frame)
-
-
-                # increasing counter so that it will
-                # show how many frames are created
-              currentframe += 1
-              print(currentframe)
-          else:
-              #deletes all the videos you uploaded for upscaling
-              #for f in os.listdir(video_folder):
-              #  os.remove(os.path.join(video_folder, f))
-
-              break
-
-        # Release all space and windows once done
-      cam.release()
-      cv2.destroyAllWindows()
-
-      #apply super-resolution on all frames of a video
-
-      # Specify the directory path
-      all_frames_path = f"/{INPUT_DIR}/upload/"
-
-      # Get a list of all files in the directory
-      file_names = os.listdir(all_frames_path)
-
-      # process the files
-      for file_name in file_names:
-        inference_image(f"/{INPUT_DIR}/upload/{file_name}")
-
-
-      #convert super res frames to .avi
-      pathIn = f'/{INPUT_DIR}/results/restored_imgs/'
-
-      zee = zee+1
-      fName = "video"+str(zee)
-      filenameVid = f"{fName}.avi"
-
-      pathOut = f"/{INPUT_DIR}/results_videos/"+filenameVid
-
-      convert_frames_to_video(pathIn, pathOut, fps)
-
-
-      #convert .avi to .mp4
-      src = f'/{INPUT_DIR}/results_videos/'
-      dst = f'/{INPUT_DIR}/results_mp4_videos/'
-
-      for root, dirs, filenames in os.walk(src, topdown=False):
-          #print(filenames)
-          for filename in filenames:
-              print('[INFO] 1',filename)
-              try:
-                  _format = ''
-                  if ".flv" in filename.lower():
-                      _format=".flv"
-                  if ".mp4" in filename.lower():
-                      _format=".mp4"
-                  if ".avi" in filename.lower():
-                      _format=".avi"
-                  if ".mov" in filename.lower():
-                      _format=".mov"
-
-                  inputfile = os.path.join(root, filename)
-                  print('[INFO] 1',inputfile)
-                  outputfile = os.path.join(dst, filename.lower().replace(_format, ".mp4"))
-                  subprocess.call(['ffmpeg', '-i', inputfile, outputfile])
-              except:
-                  print("An exception occurred")