Answers for "google video processor python nmp"

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")

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

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'])

google video processor python nmp

def display_instances(image, boxes, masks, ids, names, scores):
    """
        take the image and results and apply the mask, box, and Label
    """
    n_instances = boxes.shape[0]
    colors = visualize.random_colors(n_instances)
  
    if not n_instances:
        print('NO INSTANCES TO DISPLAY')
    else:
        assert boxes.shape[0] == masks.shape[-1] == ids.shape[0]
  
    for i, color in enumerate(colors):
        if not np.any(boxes[i]):
            continue
  
        y1, x1, y2, x2 = boxes[i]
        label = names[ids[i]]
        score = scores[i] if scores is not None else None
        caption = '{} {:.2f}'.format(label, score) if score else label
        mask = masks[:, :, i]
  
        image = visualize.apply_mask(image, mask, color)
        image = cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
        image = cv2.putText(
            image, caption, (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 0.7, color, 2
        )
  
    return image

google video processor python nmp

import os, sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt
  
os.chdir("/content/drive/My Drive/Colab Notebooks/MRCNN_pure")
sys.path.append("/content/drive/My Drive/Colab Notebooks/MRCNN_pure")
  
# Root directory of the project
ROOT_DIR = os.path.abspath(".")
  
# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
# Import COCO config
sys.path.append(os.path.join(ROOT_DIR, "samples/coco/"))  # To find local version
import coco
  
# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
  
# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)
  
# Directory of images to run detection on
IMAGE_DIR = os.path.join(ROOT_DIR, "images")
  
class InferenceConfig(coco.CocoConfig):
    # Set batch size to 1 since we'll be running inference on
    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1
  
config = InferenceConfig()
config.display()
  
# Create model object in inference mode.
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
  
# Load weights trained on MS-COCO
model.load_weights(COCO_MODEL_PATH, by_name=True)
  
# COCO Class names
# Index of the class in the list is its ID. For example, to get ID of
# the teddy bear class, use: class_names.index('teddy bear')
class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
               'bus', 'train', 'truck', 'boat', 'traffic light',
               'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
               'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
               'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
               'kite', 'baseball bat', 'baseball glove', 'skateboard',
               'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
               'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
               'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
               'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
               'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
               'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
               'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
               'teddy bear', 'hair drier', 'toothbrush']
  
# 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'])

google video processor python nmp

from google.colab import drive
drive.mount('/content/drive')

import os, sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt
import cv2

from matplotlib.patches import Polygon

os.chdir("/content/drive/My Drive/Colab Notebooks/MRCNN_pure")
sys.path.append("/content/drive/My Drive/Colab Notebooks/MRCNN_pure")
VIDEO_STREAM = "/content/drive/My Drive/Colab Notebooks/Millery.avi"
VIDEO_STREAM_OUT = "/content/drive/My Drive/Colab Notebooks/Result.avi"

# Root directory of the project
ROOT_DIR = os.path.abspath(".")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
# Import COCO config
sys.path.append(os.path.join(ROOT_DIR, "samples/coco/"))  # To find local version
import coco

# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)

# Directory of images to run detection on
IMAGE_DIR = os.path.join(ROOT_DIR, "images")

class InferenceConfig(coco.CocoConfig):
    # Set batch size to 1 since we'll be running inference on
    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1
     

def display_instances(image, boxes, masks, ids, names, scores):
    """
        take the image and results and apply the mask, box, and Label
    """
    n_instances = boxes.shape[0]
    colors = visualize.random_colors(n_instances)

    if not n_instances:
        print('NO INSTANCES TO DISPLAY')
    else:
        assert boxes.shape[0] == masks.shape[-1] == ids.shape[0]

    for i, color in enumerate(colors):
        if not np.any(boxes[i]):
            continue

        y1, x1, y2, x2 = boxes[i]
        label = names[ids[i]]
        score = scores[i] if scores is not None else None
        caption = '{} {:.2f}'.format(label, score) if score else label
        mask = masks[:, :, i]

        image = visualize.apply_mask(image, mask, color)
        image = cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
        image = cv2.putText(
            image, caption, (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 0.7, color, 2
        )

    return image 

config = InferenceConfig()
config.display()

# Create model object in inference mode.
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)

# Load weights trained on MS-COCO
model.load_weights(COCO_MODEL_PATH, by_name=True)

# COCO Class names
# Index of the class in the list is its ID. For example, to get ID of
# the teddy bear class, use: class_names.index('teddy bear')
class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
               'bus', 'train', 'truck', 'boat', 'traffic light',
               'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
               'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
               'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
               'kite', 'baseball bat', 'baseball glove', 'skateboard',
               'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
               'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
               'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
               'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
               'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
               'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
               'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
               'teddy bear', 'hair drier', 'toothbrush']

# 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);

i = 0
while i < 20000:
  # read the next frame from the file
  (grabbed, frame) = vs.read()
  i += 1

  # If the frame was not grabbed, then we have reached the end
  # of the stream
  if not grabbed:
    print ("Not grabbed.")
    break;

  # Run detection
  results = model.detect([frame], verbose=1)

  # Visualize results
  r = results[0]
  masked_frame = display_instances(frame, r['rois'], r['masks'], r['class_ids'],
                            class_names, r['scores'])

  # Check if the video writer is None
  if writer is None:
    # Initialize our video writer
    fourcc = cv2.VideoWriter_fourcc(*"XVID")
    writer = cv2.VideoWriter(VIDEO_STREAM_OUT, fourcc, 30,
      (masked_frame.shape[1], masked_frame.shape[0]), True)

  # Write the output frame to disk
  writer.write(masked_frame)

# Release the file pointers
print("[INFO] cleaning up...")
writer.release()

google video processor python nmp

import os, sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt
  
os.chdir("/content/drive/My Drive/Colab Notebooks/MRCNN_pure")
sys.path.append("/content/drive/My Drive/Colab Notebooks/MRCNN_pure")
  
# Root directory of the project
ROOT_DIR = os.path.abspath(".")
  
# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
# Import COCO config
sys.path.append(os.path.join(ROOT_DIR, "samples/coco/"))  # To find local version
import coco
  
# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
  
# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)
  
# Directory of images to run detection on
IMAGE_DIR = os.path.join(ROOT_DIR, "images")
  
class InferenceConfig(coco.CocoConfig):
    # Set batch size to 1 since we'll be running inference on
    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1
  
config = InferenceConfig()
config.display()
  
# Create model object in inference mode.
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
  
# Load weights trained on MS-COCO
model.load_weights(COCO_MODEL_PATH, by_name=True)
  
# COCO Class names
# Index of the class in the list is its ID. For example, to get ID of
# the teddy bear class, use: class_names.index('teddy bear')
class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
               'bus', 'train', 'truck', 'boat', 'traffic light',
               'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
               'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
               'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
               'kite', 'baseball bat', 'baseball glove', 'skateboard',
               'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
               'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
               'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
               'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
               'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
               'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
               'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
               'teddy bear', 'hair drier', 'toothbrush']
  
# 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)
  
# Visualize results
r = results[0]
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],
                            class_names, r['scores'])

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)

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)

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);