Parse non-GenDC binary data

In this tutorial, we learn how to user GenDC separator library.

Prerequisite

• json
• OpenCV
• numpy

pip3 install -U pip
pip3 install opencv-python
pip3 install numpy

Tutorial

In the previous tutorial, we learned how to save image data into a binary file. Now, we will load the data, parse the whole data, and retrieve images.

Binary file structure

The structure of binary data saved in the previous tutorial is as follows:

Size in Byte	Content
4	framecount
w * h * d * c	image
4	framecount
w * h * d * c	4
4	framecount
...	...
w * h * d * c	image

Framecount is 4 byte-length, and imagedata size is width height byte-depth * number of channel.

The value of width and height is in <prefix>-config.json with binary file saved by the pipeline in the previous tutorial.

The byte-depth and the number of channel can be calculated from PixelFormat, which is also noted in <prefix>-config.json.

Config file is saved with the binary files under tutorial_save_image_bin_XXXXXXXXXXXXXXXXXX and prefix is image0-.

# image info from image0-config.json
f = open(os.path.join(directory_name, prefix + "config.json"))
config = json.loads(f.read())
f.close()

w = config["width"]
h = config["height"]
d = 2 if config["pfnc_pixelformat"] == Mono10 or config["pfnc_pixelformat"] == Mono12 \
    else 1
c = 3 if config["pfnc_pixelformat"] == RGB8 or config["pfnc_pixelformat"] == BGR8 \
    else 1
framesize = w * h * d * c

Find Binary file.

If you use the binary file saved in the previous tutorial, the name of the directory should be tutorial_save_image_bin_XXXXXXXXXXXXXXXXXX and binary file prefix is image0-.

directory_name = "tutorial_save_image_bin_XXXXXXXXXXXXXXXXXX"
prefix = "gendc0-"

The following snippet attempts to retrieve all binary files starting with a specified prefix from a directory. It then reorders all the found binaries according to their recorded order.

bin_files = [f for f in os.listdir(directory_name) if f.startswith(prefix) and f.endswith(".bin")]
bin_files = sorted(bin_files, key=lambda s: int(s.split('-')[-1].split('.')[0]))

Now, we go through all ordered binary files in bf with for loop.

for bf in bin_files:

Open and load Binary file.

In the for loop, our target (single) binary file is bf. We open this binary file as ifs and read the whole content of itf.

bin_file = os.path.join(directory_name, bf)

with open(bin_file, mode='rb') as ifs:
    filecontent = ifs.read()

Parse binary file

Finally, we parse each binary file.

Since we know the size of framecount is -byte, which is the size of 32-bit integer. We copy the 4-byte data from filecontent to framecount.

cursor = 0
while cursor < len(filecontent):
    framecount = struct.unpack('I', filecontent[cursor:cursor+4])[0]
    print(framecount)
    ...

Image data is following framecount, so the offse is +4 and the data size is width * height * byte-depth * num-color-channel.

np_dtype = np.uint8 if d == 1 else np.uint16
while cursor < len(filecontent):
    image = np.frombuffer(filecontent[cursor+4:cursor+4+framesize], dtype=np_dtype).reshape((h, w))

    ...
    cv2.imshow("First available image component", image)
    cv2.waitKey(1)

Now OpenCV's imshow can display the image-preview.

To move on to the next framecount and image data, do not forget to shift the cursor.

cursor = cursor + 4 + framesize

Complete code

Complete code used in the tutorial is here.