Python how to read png file in python
After decompressing, you get a stream of binary data consisting of height runs, each one with filter_type + width × pixel_data (where width and height are straightforward defined in the chunk, and the exact format of pixel_data – bit depth and alpha – needs to be derived from the Colour Type flag in . Solution: step back — your assumptions are not correct png files don’t have «hexadecimal content» — they have bytes — «hexadecimal» is not what you think, it is just another way of representing a number.
Read bytes of a PNG in python
step back — your assumptions are not correct
png files don’t have «hexadecimal content» — they have bytes — «hexadecimal» is not what you think, it is just another way of representing a number.
Your code is not fixable because it doesn’t make any sense in first place — there is nothing to decode.
Hexadecimal is used by programmers as a convenience because each hex digit can represent 4 bits so it takes only two hexadecimal digits to represent a byte. However this is just representation.
From the comments it seems what you want is to add binary data to the end of the file:
message = 'Hello World!' with open(image, 'ab') as f: # open the file for appending, binary mode f.write(bytes(8)) # write 8 null bytes to the file f.write(message.encode('utf-8')) # add the message, encoded to bytes # by using the utf-8 encoding Then to read again the message:
with open(image, 'rb') as f: # open the file in read binary mode data = f.read() # read the bytes from the file to a variable pos = data.find(bytes(8)) # locate the 8 null bytes message = data[pos + 8:].decode('utf-8') # decode the message Read bytes of a PNG in python, Then to read again the message: with open(image, ‘rb’) as f: # open the file in read binary mode data = f.read() # read the bytes from the file to a variable pos = data.find(bytes(8)) # locate the 8 null bytes message = data[pos + 8:].decode(‘utf-8’) # decode the message
Python: default/common way to read png images
No, there are no modules in the standard library for reading/writing/processing images directly. But the most common library might be PIL (Python Imaging Library). Many projects are not included in the standard library because they are 1) totally optional and 2) cannot be maintained by the few Python core developers.
Coming late to the party, I would strongly suggest one of the Python interfaces to the ImageMagick library (Wand worked well in my testing, I’ll know more soon. )
ImageMagick is a more powerful library and also pretty well a de-facto standard across many languages. Appealing to a wider base, they also have a wider developer base as a result.
THe suggested PIL does not support interlaced PNGs. It can be quite anoying when dealing with lots of PNGs from different origins. It is possible to open them, but can only read headerinformation from them, all other operations fail.
Python — Importing PNG files into Numpy?, Example: import imageio im = imageio.imread (‘my_image.png’) print (im.shape) You can also use imageio to load from fancy sources: im = imageio.imread (‘http://upload.wikimedia.org/wikipedia/commons/d/de/Wikipedia_Logo_1.0.png’) …
Having a hard time reading a text from png file using python
I think the problem is that the text CHUBB is too large for the picture. If we decrease the size a little bit or paste it into a larger canvas, then pytesseract will work fine
from PIL import Image img = Image.open('test.png') # load image new_img = Image.new('RGB', (400, 400), color = 'white') # create a larger canvas new_img.paste(im=img, box=(100,100), mask=img) # paste original CHUBB in the large image text = pytesseract.image_to_string(new_img, lang='eng', config='--psm 12') # OCR print(text) # CHUBB for i in range(1,14): try: text = pytesseract.image_to_string(new_img, lang='eng',config=f"--psm ") # OCR print('psm',i, text) except: pass psm 1 CHUBB psm 3 CHUBB psm 4 CHUBB psm 5 0 u J I U psm 6 CHUBB psm 7 CHUBB psm 8 7 psm 9 CHUBB psm 10 CHUBB psm 11 CHUBB psm 12 CHUBB psm 13 7 Python — Open a PNG in Jupyter Notebook, Im trying this code to open an image in a jupyter notebook with python. This is the code: import matplotlib.image as mpimg import matplotlib.pyplot as plt # Read Images img = mpimg.imread(‘C:
Read the IDAT of a PNG file with Python
Ok, my bad. i think i forgot the zlib compression. This block look like better, but i sill have 273 pixels insted of 100.
elif chunkType == "IDAT": print(chunkType) chunkDataBin=zlib.decompress(bytes.fromhex(chunkDataHex)) while cursor_1 < len(chunkDataBin): colorIndex= chunkDataBin[cursor_1] cursor_1 += 1 print("colorIndex: "+str(colorIndex)) So, the data I have are not out of range anymore. Each line look like begin with a 0 index.
This picture have 16px Width on 16px Height (not 10px Width on 10px Height like I said).
I was still working with a hexadecimal number instead of a decimal one:
#Width (4 octets) start = cursor_1 stop = cursor_1+(4*2) cursor_1 = stop width = int(chunkDataHex[start:stop],16) print("Width: "+str(width)) #Height (4 octets) start = cursor_1 stop = cursor_1+(4*2) cursor_1 = stop height = int(chunkDataHex[start:stop],16) print("Height: "+str(height)) #Bit Depth (1 octets) start = cursor_1 stop = cursor_1+(1*2) cursor_1 = stop bitDepth = int(chunkDataHex[start:stop],16) print("Bit Depth: "+str(bitDepth)) #Color Type (1 octets) start = cursor_1 stop = cursor_1+(1*2) cursor_1 = stop colorType = int(chunkDataHex[start:stop],16) print("ColorType: "+str(colorType)) #Compression Method (1 octets) start = cursor_1 stop = cursor_1+(1*2) cursor_1 = stop compressionMethod = int(chunkDataHex[start:stop],16) print("Compression Method: "+str(compressionMethod)) #Filter Method (1 octets) start = cursor_1 stop = cursor_1+(1*2) cursor_1 = stop filterMethod = int(chunkDataHex[start:stop],16) print("Filter Method: "+str(filterMethod)) #Interlace Method (1 octets) start = cursor_1 stop = cursor_1+(1*2) cursor_1 = stop interlaceMethod = int(chunkDataHex[start:stop],16) print("Interlace Method: "+str(interlaceMethod)) If you plan to 'manually' extract PNG data, it's a good idea to keep your browser open at the official specifications. The pixel data of PNG images is compressed, as explained in 10. Compression. After decompressing, you get a stream of binary data consisting of height runs, each one with filter_type + width × pixel_data (where width and height are straightforward defined in the IHDR chunk, and the exact format of pixel_data – bit depth and alpha – needs to be derived from the Colour Type flag in IHDR .
So step 1 is to use zlib to decompress the binary chunk. After decompressing, you end up with 16 × (1 + 16) = 272 bytes.
The next step is to iterate over the rows and apply the filter method of each row, to (finally!) end up with a list of actual values in the format defined by the Colour Type.
Each of the filter types uses the previously decoded row as input (row number -1 is considered to be all zeroes), on which a function is applied with the new bytes. Fortunately, your sample image only uses Row Filter #0: None, which is the easiest to implement as it only replaces the old value with the new one. For a more complete PNG decoder, you will need to implement all 4 filters.
This leads to the following:
elif chunkType == "IDAT": print(chunkType) chunkDataBin=zlib.decompress(bytes.fromhex(chunkDataHex)) print (len(chunkDataBin)) # create the initial (empty) row processRow = [0] * width for y in range(height): rowFilter = chunkDataBin[y*(width+1)] print ("Row Filter: %d; pixel data " % rowFilter, end='') for x in range(width): colorIndex = chunkDataBin[y*(width+1)+1+x] # process filters # Filter: None if rowFilter == 0: processRow[x] = colorIndex else: # raise an error for not implemented filters raise ValueError("Filter type %d is not implemented" % rowFilter) for x in range(width): print("%02X " % processRow[x], end='') print () Processing the IDAT chunk as soon as you come across it works for your test file, but according to the specifications, there can be multiple IDAT chunks. The proper way to handle this is to keep a global object to which you concatenate all IDAT chunks you encounter (they should be consecutive as well). Only after you concatenated all of them into a single stream, you can use zlib to decompress this.
Read the IDAT of a PNG file with Python, So step 1 is to use zlib to decompress the binary chunk. After decompressing, you end up with 16 × (1 + 16) = 272 bytes. The next step is to iterate over the rows and apply the filter method of each row, to (finally!) end up with a list of actual values in the format defined by the Colour Type.