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google video processor python nmp

# Load a random image from the images folder
file_names = next(os.walk(IMAGE_DIR))[2]
image = skimage.io.imread(os.path.join(IMAGE_DIR, random.choice(file_names)))
  
# Run detection
results = model.detect([image], verbose=1)
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google video processor python nmp

# Load a random image from the images folder
file_names = next(os.walk(IMAGE_DIR))[2]
  
for file_name in file_names:
    image = skimage.io.imread(os.path.join(IMAGE_DIR, file_name))
  
    # Run detection
    results = model.detect([image], verbose=1)
  
    # Visualize results
    r = results[0]
    visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],
                        class_names, r['scores'])
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google video processor python nmp

...
Processing 1 images
image shape: (415, 640, 3) min: 0.00000 max: 255.00000 uint8
molded_images shape: (1, 1024, 1024, 3) min: -123.70000 max: 151.10000 float64
image_metas shape: (1, 93) min: 0.00000 max: 1024.00000 float64
anchors shape: (1, 261888, 4) min: -0.35390 max: 1.29134 float32
Segmentation fault (core dumped)
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google video processor python nmp

os.chdir("/content/drive/My Drive/Colab Notebooks/MRCNN_pure")
sys.path.append("/content/drive/My Drive/Colab Notebooks/MRCNN_pure")
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google video processor python nmp

import cv2
...
VIDEO_STREAM = "/content/drive/My Drive/Colab Notebooks/Millery.avi"
VIDEO_STREAM_OUT = "/content/drive/My Drive/Colab Notebooks/Result.avi"
...
# initialize the video stream and pointer to output video file
vs = cv2.VideoCapture(VIDEO_STREAM)
writer = None
vs.set(cv2.CAP_PROP_POS_FRAMES, 1000);
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google video processor python nmp

fourcc = cv2.VideoWriter_fourcc(*"XVID")
writer = cv2.VideoWriter(VIDEO_STREAM_OUT, fourcc, 30, 
         (masked_frame.shape[1], masked_frame.shape[0]), True)
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google video processor python nmp

ffprobe Result.avi
...
 Duration: N/A, start: 0.000000, bitrate: N/A
    Stream #0:0: Video: mpeg4 (Simple Profile) (XVID / 0x44495658), 
    yuv420p, 640x272 [SAR 1:1 DAR 40:17], 30 fps, 30 tbr, 30 tbn, 30 tbc
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