Python: 将 RAW 图像转换为 PNG
Python: Convert RAW image to PNG
我正在努力使用 Python 处理原始图像以将其转换为 png 格式。
处理这些类型的数据对我来说是新手，我还不太了解其中的逻辑。
图像数据（根据相机文档）是 12 位、小端顺序和拜耳模式。
图像为 2048x1944 像素。
图片可用 here.
我在 中遵循了@Rotem 的回答，但我得到的图像基本上只是噪音。下面的代码。
知道如何处理这个问题吗？
import cv2
import numpy as np
width = 2048
height = 1944
with open('.raw_image.raw', "rb") as rawimg:
# Read the packed 12bits as bytes - each 3 bytes applies 2 pixels
data = np.fromfile(rawimg, np.uint8, width * height * 3//2)
data = data.astype(np.uint16) # Cast the data to uint16 type.
result = np.zeros(data.size*2//3, np.uint16) # Initialize matrix for storing the pixels.
# 12 bits packing: ######## ######## ########
# | 8bits| | 4 | 4 | 8 |
# | lsb | |msb|lsb | msb |
# <-----------><----------->
# 12 bits 12 bits
result[0::2] = ((data[1::3] & 15) << 8) | data[0::3]
result[1::2] = (data[1::3] >> 4) | (data[2::3] << 4)
bayer_im = np.reshape(result, (height, width))
# Apply Demosacing (COLOR_BAYER_BG2BGR gives the best result out of the 4 combinations).
bgr = cv2.cvtColor(bayer_im, cv2.COLOR_BAYER_BG2BGR) # The result is BGR format with 16 bits per pixel and 12 bits range [0, 2^12-1].
# bgr = cv2.cvtColor(bayer_im, cv2.COLOR_BayerGB2BGR) # The result is BGR format with 16 bits per pixel and 12 bits range [0, 2^12-1].
# bgr = cv2.cvtColor(bayer_im, cv2.COLOR_BayerRG2BGR) # The result is BGR format with 16 bits per pixel and 12 bits range [0, 2^12-1].
# bgr = cv2.cvtColor(bayer_im, cv2.COLOR_BayerGR2BGR ) # The result is BGR format with 16 bits per pixel and 12 bits range [0, 2^12-1].
# Show image for testing (multiply by 16 because imshow requires full uint16 range [0, 2^16-1]).
cv2.imshow('bgr', cv2.resize(bgr*16, [width//10, height//10]))
cv2.waitKey()
cv2.destroyAllWindows()
# Convert to uint8 before saving as JPEG (not part of the conversion).
colimg = np.round(bgr.astype(float) * (255/4095))
cv2.imwrite("./test.png", colimg)
您发布的原始图像是每像素 12 位，但没有打包。
12位数据存储在每16位的高12位。
我们从文件的大小可以看出，每个像素有2个字节：
7962624 = 2048*1944*2
我们可以将uint16元素中的12位数据表示如下：
-------------------------------------------------------------------------------
| | | | | | | | | | | | | | | | |
| b11| b10| b9 | b8 | b7 | b6 | b5 | b4 | b3 | b2 | b1 | b0 | 0 | 0 | 0 | 0 |
-------------------------------------------------------------------------------
与 12 位压缩格式相比，这种格式更易于使用...
我们不需要解包数据，我们可以将其视为每个像素 16 位。
代码示例：
import cv2
import numpy as np
width = 2048
height = 1944
with open("raw_image.ims_rgb", "rb") as rawimg:
# Read the raw image as uint16 (two bytes per pixel).
bayer_im = np.fromfile(rawimg, np.uint16, width * height).reshape(height, width)
# The 12 bits of each pixel are stored in the upper 12 bits of every uint16 element.
# The lower 4 bits of the uint16 element are zeros.
# <--- 16 bits -->
# ************0000
# <-12 bits -><4->
# data zeros
# Apply Demosacing.
# It look like COLOR_BAYER_BG2BGR gives the best result, but it hard to tell from the given input.
bgr = cv2.cvtColor(bayer_im, cv2.COLOR_BAYER_BG2BGR) # The result is BGR format with 16 bits per pixel range [0, 2^16-1].
# Apply manual "white balance".
# The result image is greenish - this is normal for most cameras.
# We may fix it by scaling up the red and the blue color channels.
# It look like approximate scaling is about 1.5 for the red and about 1.25 for the blue.
bgr[:, :, 0] = (bgr[:, :, 0].astype(np.float32)*1.25).clip(0, 65535).astype(np.uint16)
bgr[:, :, 2] = (bgr[:, :, 2].astype(np.float32)*1.5).clip(0, 65535).astype(np.uint16)
# Show image for testing (multiply by 16 because imshow requires full uint16 range [0, 2^16-1]).
cv2.imshow('bgr', cv2.resize(bgr, [width//10, height//10]))
cv2.waitKey()
cv2.destroyAllWindows()
# Save the output as tiff with 16 bits per color component.
cv2.imwrite("rgb16.tif", bgr)
我们可以看到，这是一张月亮的照片：
月亮不是最好的选择，因为我们无法验证颜色的正确性...
注：
我“手动”放大了红色和蓝色通道，使月亮变成灰色（而不是绿色）。
我们可以将缩放称为手动 White Balance.
我正在努力使用 Python 处理原始图像以将其转换为 png 格式。 处理这些类型的数据对我来说是新手，我还不太了解其中的逻辑。
图像数据（根据相机文档）是 12 位、小端顺序和拜耳模式。 图像为 2048x1944 像素。 图片可用 here.
我在
知道如何处理这个问题吗？
import cv2
import numpy as np
width = 2048
height = 1944
with open('.raw_image.raw', "rb") as rawimg:
# Read the packed 12bits as bytes - each 3 bytes applies 2 pixels
data = np.fromfile(rawimg, np.uint8, width * height * 3//2)
data = data.astype(np.uint16) # Cast the data to uint16 type.
result = np.zeros(data.size*2//3, np.uint16) # Initialize matrix for storing the pixels.
# 12 bits packing: ######## ######## ########
# | 8bits| | 4 | 4 | 8 |
# | lsb | |msb|lsb | msb |
# <-----------><----------->
# 12 bits 12 bits
result[0::2] = ((data[1::3] & 15) << 8) | data[0::3]
result[1::2] = (data[1::3] >> 4) | (data[2::3] << 4)
bayer_im = np.reshape(result, (height, width))
# Apply Demosacing (COLOR_BAYER_BG2BGR gives the best result out of the 4 combinations).
bgr = cv2.cvtColor(bayer_im, cv2.COLOR_BAYER_BG2BGR) # The result is BGR format with 16 bits per pixel and 12 bits range [0, 2^12-1].
# bgr = cv2.cvtColor(bayer_im, cv2.COLOR_BayerGB2BGR) # The result is BGR format with 16 bits per pixel and 12 bits range [0, 2^12-1].
# bgr = cv2.cvtColor(bayer_im, cv2.COLOR_BayerRG2BGR) # The result is BGR format with 16 bits per pixel and 12 bits range [0, 2^12-1].
# bgr = cv2.cvtColor(bayer_im, cv2.COLOR_BayerGR2BGR ) # The result is BGR format with 16 bits per pixel and 12 bits range [0, 2^12-1].
# Show image for testing (multiply by 16 because imshow requires full uint16 range [0, 2^16-1]).
cv2.imshow('bgr', cv2.resize(bgr*16, [width//10, height//10]))
cv2.waitKey()
cv2.destroyAllWindows()
# Convert to uint8 before saving as JPEG (not part of the conversion).
colimg = np.round(bgr.astype(float) * (255/4095))
cv2.imwrite("./test.png", colimg)
您发布的原始图像是每像素 12 位，但没有打包。
12位数据存储在每16位的高12位。
我们从文件的大小可以看出，每个像素有2个字节：
7962624 = 2048*1944*2
我们可以将uint16元素中的12位数据表示如下：
-------------------------------------------------------------------------------
| | | | | | | | | | | | | | | | |
| b11| b10| b9 | b8 | b7 | b6 | b5 | b4 | b3 | b2 | b1 | b0 | 0 | 0 | 0 | 0 |
-------------------------------------------------------------------------------
与 12 位压缩格式相比，这种格式更易于使用...
我们不需要解包数据，我们可以将其视为每个像素 16 位。
代码示例：
import cv2
import numpy as np
width = 2048
height = 1944
with open("raw_image.ims_rgb", "rb") as rawimg:
# Read the raw image as uint16 (two bytes per pixel).
bayer_im = np.fromfile(rawimg, np.uint16, width * height).reshape(height, width)
# The 12 bits of each pixel are stored in the upper 12 bits of every uint16 element.
# The lower 4 bits of the uint16 element are zeros.
# <--- 16 bits -->
# ************0000
# <-12 bits -><4->
# data zeros
# Apply Demosacing.
# It look like COLOR_BAYER_BG2BGR gives the best result, but it hard to tell from the given input.
bgr = cv2.cvtColor(bayer_im, cv2.COLOR_BAYER_BG2BGR) # The result is BGR format with 16 bits per pixel range [0, 2^16-1].
# Apply manual "white balance".
# The result image is greenish - this is normal for most cameras.
# We may fix it by scaling up the red and the blue color channels.
# It look like approximate scaling is about 1.5 for the red and about 1.25 for the blue.
bgr[:, :, 0] = (bgr[:, :, 0].astype(np.float32)*1.25).clip(0, 65535).astype(np.uint16)
bgr[:, :, 2] = (bgr[:, :, 2].astype(np.float32)*1.5).clip(0, 65535).astype(np.uint16)
# Show image for testing (multiply by 16 because imshow requires full uint16 range [0, 2^16-1]).
cv2.imshow('bgr', cv2.resize(bgr, [width//10, height//10]))
cv2.waitKey()
cv2.destroyAllWindows()
# Save the output as tiff with 16 bits per color component.
cv2.imwrite("rgb16.tif", bgr)
我们可以看到，这是一张月亮的照片：
月亮不是最好的选择，因为我们无法验证颜色的正确性...
注：
我“手动”放大了红色和蓝色通道，使月亮变成灰色（而不是绿色）。
我们可以将缩放称为手动 White Balance.