Averaging pixels in image ROI

I have the following image and I would like to apply averaging on each masked region (not the bounding box) in the image.

enter image description here

As you can see, right now, the regions have changing values inside them on the heat map. Some pixels are yellowish, some are purplish. I want this to be not the case inside the masks.

So what I need to do is (I guess):

to find which coordinates correspond to the masks

average the pixels within these coordinates

Here is how the masks are found:

file_names = glob(os.path.join(IMAGE_DIR, "*.jpg"))

masks_prediction = np.zeros((521, 768, len(file_names)))

for i in range(len(file_names)):

    print(i)

    image = skimage.io.imread(file_names[i])

    predictions = model.detect([image],  verbose=1)

    p = predictions[0]

    masks = p['masks']

    merged_mask = np.zeros((masks.shape[0], masks.shape[1]))

    for j in range(masks.shape[2]):

        merged_mask[masks[:,:,j]==True] = True

        masks_prediction[:,:,i] = merged_mask

Here is the function which applies the masks:

def apply_mask(image, mask, color, alpha=0.5):

    """Apply the given mask to the image.

    """

    for c in range(3):

        image[:, :, c] = np.where(mask == 1,

                                  image[:, :, c] *

                                  (1 - alpha) + alpha * color[c] * 255,

                                  image[:, :, c])

    return image

and in the main file, here is how it is used:

mask = masks[:, :, i]

if show_mask:

     masked_image = apply_mask(masked_image, mask, color)

So I need to make a modification in somewhere here but I don't know where exactly.

asked Nov 23 '18 at 8:09

Schütze

55527

add a comment |

I have the following image and I would like to apply averaging on each masked region (not the bounding box) in the image.

enter image description here

As you can see, right now, the regions have changing values inside them on the heat map. Some pixels are yellowish, some are purplish. I want this to be not the case inside the masks.

So what I need to do is (I guess):

to find which coordinates correspond to the masks

average the pixels within these coordinates

Here is how the masks are found:

file_names = glob(os.path.join(IMAGE_DIR, "*.jpg"))

masks_prediction = np.zeros((521, 768, len(file_names)))

for i in range(len(file_names)):

    print(i)

    image = skimage.io.imread(file_names[i])

    predictions = model.detect([image],  verbose=1)

    p = predictions[0]

    masks = p['masks']

    merged_mask = np.zeros((masks.shape[0], masks.shape[1]))

    for j in range(masks.shape[2]):

        merged_mask[masks[:,:,j]==True] = True

        masks_prediction[:,:,i] = merged_mask

Here is the function which applies the masks:

def apply_mask(image, mask, color, alpha=0.5):

    """Apply the given mask to the image.

    """

    for c in range(3):

        image[:, :, c] = np.where(mask == 1,

                                  image[:, :, c] *

                                  (1 - alpha) + alpha * color[c] * 255,

                                  image[:, :, c])

    return image

and in the main file, here is how it is used:

mask = masks[:, :, i]

if show_mask:

     masked_image = apply_mask(masked_image, mask, color)

So I need to make a modification in somewhere here but I don't know where exactly.

asked Nov 23 '18 at 8:09

Schütze

55527

add a comment |

I have the following image and I would like to apply averaging on each masked region (not the bounding box) in the image.

enter image description here

As you can see, right now, the regions have changing values inside them on the heat map. Some pixels are yellowish, some are purplish. I want this to be not the case inside the masks.

So what I need to do is (I guess):

to find which coordinates correspond to the masks

average the pixels within these coordinates

Here is how the masks are found:

file_names = glob(os.path.join(IMAGE_DIR, "*.jpg"))

masks_prediction = np.zeros((521, 768, len(file_names)))

for i in range(len(file_names)):

    print(i)

    image = skimage.io.imread(file_names[i])

    predictions = model.detect([image],  verbose=1)

    p = predictions[0]

    masks = p['masks']

    merged_mask = np.zeros((masks.shape[0], masks.shape[1]))

    for j in range(masks.shape[2]):

        merged_mask[masks[:,:,j]==True] = True

        masks_prediction[:,:,i] = merged_mask

Here is the function which applies the masks:

def apply_mask(image, mask, color, alpha=0.5):

    """Apply the given mask to the image.

    """

    for c in range(3):

        image[:, :, c] = np.where(mask == 1,

                                  image[:, :, c] *

                                  (1 - alpha) + alpha * color[c] * 255,

                                  image[:, :, c])

    return image

and in the main file, here is how it is used:

mask = masks[:, :, i]

if show_mask:

     masked_image = apply_mask(masked_image, mask, color)

So I need to make a modification in somewhere here but I don't know where exactly.

asked Nov 23 '18 at 8:09

Schütze

55527

I have the following image and I would like to apply averaging on each masked region (not the bounding box) in the image.

enter image description here

As you can see, right now, the regions have changing values inside them on the heat map. Some pixels are yellowish, some are purplish. I want this to be not the case inside the masks.

So what I need to do is (I guess):

to find which coordinates correspond to the masks

average the pixels within these coordinates

Here is how the masks are found:

file_names = glob(os.path.join(IMAGE_DIR, "*.jpg"))

masks_prediction = np.zeros((521, 768, len(file_names)))

for i in range(len(file_names)):

    print(i)

    image = skimage.io.imread(file_names[i])

    predictions = model.detect([image],  verbose=1)

    p = predictions[0]

    masks = p['masks']

    merged_mask = np.zeros((masks.shape[0], masks.shape[1]))

    for j in range(masks.shape[2]):

        merged_mask[masks[:,:,j]==True] = True

        masks_prediction[:,:,i] = merged_mask

Here is the function which applies the masks:

def apply_mask(image, mask, color, alpha=0.5):

    """Apply the given mask to the image.

    """

    for c in range(3):

        image[:, :, c] = np.where(mask == 1,

                                  image[:, :, c] *

                                  (1 - alpha) + alpha * color[c] * 255,

                                  image[:, :, c])

    return image

and in the main file, here is how it is used:

mask = masks[:, :, i]

if show_mask:

     masked_image = apply_mask(masked_image, mask, color)

So I need to make a modification in somewhere here but I don't know where exactly.

python image opencv tensorflow image-processing

asked Nov 23 '18 at 8:09

Schütze

55527

asked Nov 23 '18 at 8:09

Schütze

55527

asked Nov 23 '18 at 8:09

Schütze

55527

asked Nov 23 '18 at 8:09

Schütze

55527

asked Nov 23 '18 at 8:09

Schütze

55527

add a comment |

1 Answer
1

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oldest

votes

I think the mask you are looking for is provided by the code :

mask = masks[:, :, i]

where i refers to the number of mask you have.

You can obtain the average values of the mask region from the original image using the openCV function mean

Here is how your code should look like:

mask = masks[:, :, i]

avg_masked_value = cv2.mean(original_image,mask)

where original_image is the original image you loaded and avg_masked_value will contain 3x1 array of averaged value.

answered Nov 23 '18 at 13:19

yapws87

1,023213

add a comment |

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1 Answer
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active

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1 Answer
1

active

oldest

votes

I think the mask you are looking for is provided by the code :

mask = masks[:, :, i]

where i refers to the number of mask you have.

You can obtain the average values of the mask region from the original image using the openCV function mean

Here is how your code should look like:

mask = masks[:, :, i]

avg_masked_value = cv2.mean(original_image,mask)

where original_image is the original image you loaded and avg_masked_value will contain 3x1 array of averaged value.

answered Nov 23 '18 at 13:19

yapws87

1,023213

add a comment |

I think the mask you are looking for is provided by the code :

mask = masks[:, :, i]

where i refers to the number of mask you have.

You can obtain the average values of the mask region from the original image using the openCV function mean

Here is how your code should look like:

mask = masks[:, :, i]

avg_masked_value = cv2.mean(original_image,mask)

where original_image is the original image you loaded and avg_masked_value will contain 3x1 array of averaged value.

answered Nov 23 '18 at 13:19

yapws87

1,023213

add a comment |

I think the mask you are looking for is provided by the code :

mask = masks[:, :, i]

where i refers to the number of mask you have.

You can obtain the average values of the mask region from the original image using the openCV function mean

Here is how your code should look like:

mask = masks[:, :, i]

avg_masked_value = cv2.mean(original_image,mask)

where original_image is the original image you loaded and avg_masked_value will contain 3x1 array of averaged value.

answered Nov 23 '18 at 13:19

yapws87

1,023213

I think the mask you are looking for is provided by the code :

mask = masks[:, :, i]

where i refers to the number of mask you have.

You can obtain the average values of the mask region from the original image using the openCV function mean

Here is how your code should look like:

mask = masks[:, :, i]

avg_masked_value = cv2.mean(original_image,mask)

where original_image is the original image you loaded and avg_masked_value will contain 3x1 array of averaged value.

answered Nov 23 '18 at 13:19

yapws87

1,023213

answered Nov 23 '18 at 13:19

yapws87

1,023213

answered Nov 23 '18 at 13:19

yapws87

1,023213

answered Nov 23 '18 at 13:19

yapws87

1,023213

add a comment |

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