image to chars + google slides script

.gitignore

@@ -1,4 +1,6 @@
 *.pyc
 tests/
 .vscode/
-.idea/
+.idea/
+
+secrets/

other_tutorials/image_to_chars.py → image_to_chars/image_to_chars.py

@@ -1,41 +1,42 @@
-#%% [markdown]
 # # Image to chars
 # We want to represent images with chars to get a cool matrix effect
 #
-# All data that we are working on can be found here: https://github.com/YoniChechik/AI_is_Math/tree/master/other_tutorials
+# All data that we are working on can be found here: https://github.com/YoniChechik/AI_is_Math
 
-#%%
-import numpy as np
 import cv2
 import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
 
-#%%
-im_bgr = cv2.imread("matrix.jpg")
+im_bgr = cv2.imread("image_to_chars/matrix.jpg")
 im_rgb = cv2.cvtColor(im_bgr, cv2.COLOR_BGR2RGB)
 
 plt.imshow(im_rgb)
 plt.title("Wanted result style")
 plt.show()
 
-
-#%% [markdown]
 # Let's read an image to start and experiment on
-im_bgr = cv2.imread("person.jpg")
+im_bgr = cv2.imread("image_to_chars/person.jpg")
 im_rgb = cv2.cvtColor(im_bgr, cv2.COLOR_BGR2RGB)
 
 plt.imshow(im_rgb)
 plt.title("original image")
 plt.show()
 
-
-#%% [markdown]
 # Start with 2 functions to convert chars to pixel array representation
 # You don't really need to understand it, we simply copy it from the web...
-#%%
-from PIL import Image
-from PIL import ImageFont
-from PIL import ImageDraw
-import string
+
+
+def max_char_window_size(char_list, font_path, fontsize):
+    font = ImageFont.truetype(font_path, fontsize)
+    max_w = max_h = 0
+    for c in char_list:
+        _, _, w, h = font.getbbox(c)
+        if w > max_w:
+            max_w = w
+        if h > max_h:
+            max_h = h
+    return max_w, max_h
 
 
 def char_to_pixels(text, window_size_wh, font_path, fontsize):
@@ -52,34 +53,19 @@ def char_to_pixels(text, window_size_wh, font_path, fontsize):
     return arr
 
 
-def max_char_window_size(font_path, fontsize):
-    font = ImageFont.truetype(font_path, fontsize)
-    max_w = max_h = 0
-    for c in string.printable[:-5]:
-        w, h = font.getsize(c)
-        if w > max_w:
-            max_w = w
-        if h > max_h:
-            max_h = h
-    return max_w, max_h
-
-
-#%% [markdown]
 # Next we will build the chars we want to use for the image representation.
-#%%
 # download the font from here: https://github.com/YoniChechik/AI_is_Math/tree/master/other_tutorials
-FONT_PATH = "whitrabt.ttf"
+FONT_PATH = "image_to_chars/whitrabt.ttf"
 FONT_SIZE = 14
-REPRESENTATION_CHARS = [" ", ".", ":", "!", "+", "*", "e", "$", "@"]
+REPRESENTATION_CHARS = [" ", ".", ":", "+", "*", "e", "$", "@"]
 
-
-window_size_wh = max_char_window_size(FONT_PATH, FONT_SIZE)
+window_size_wh = max_char_window_size(REPRESENTATION_CHARS, FONT_PATH, FONT_SIZE)
 
 char_pix_list = []
 for c in REPRESENTATION_CHARS:
     char_pix_list.append(char_to_pixels(c, window_size_wh, FONT_PATH, FONT_SIZE))
 
-#%%
+
 def display(arr):
     result = np.where(arr, "#", " ")
     print("\n".join(["".join(row) for row in result]))
@@ -89,29 +75,23 @@ def display(arr):
     print(c)
     display(c_pix)
 
-#%% [markdown]
 # This is the tricky part...
 # Each small block of the image should be represented as the closest char from our list.
 # By doing decimation on the graylevel image we can get the *mean intensity of the block*,
 # and then we can convert it to a char that corresponds to this intensity.
-#%%
 w = window_size_wh[0]
 h = window_size_wh[1]
 
-
 im_gray = cv2.cvtColor(im_bgr, cv2.COLOR_BGR2GRAY)
 im_resize = cv2.resize(im_gray, (im_gray.shape[1] // w, im_gray.shape[0] // h))
 
-
 plt.imshow(im_resize)
 plt.colorbar()
 plt.title("resized gray image")
 plt.show()
 
-#%% [markdown]
 # Each block now has a corresponding number to our `REPRESENTATION_CHARS`
 # LUT = look-up table
-#%%
 quantization_step = 256 / len(REPRESENTATION_CHARS)
 im_lut = np.floor(im_resize / quantization_step).astype(int)
 
@@ -120,24 +100,22 @@ def display(arr):
 plt.title("LUT representation of the image")
 plt.show()
 
-#%% [markdown]
 # Now simply build a new image block by block from our `im_lut`
-#%%
 res = np.zeros((im_lut.shape[0] * h, im_lut.shape[1] * w))
 
 for col in range(im_lut.shape[0]):
     for row in range(im_lut.shape[1]):
-
-        res[col * h : col * h + h, row * w : row * w + w] = char_pix_list[im_lut[col, row]]
+        res[col * h : col * h + h, row * w : row * w + w] = char_pix_list[
+            im_lut[col, row]
+        ]
 
 plt.imshow(res)
 plt.title("Final result")
 plt.show()
 
-#%% [markdown]
+
 # Building our entire code as a function and running it on a video stream.
 # (This will not work in google colab, only in your own computer with a camera)
-#%%
 def im_to_chars(im_bgr, char_pix_list, window_size_wh):
     w = window_size_wh[0]
     h = window_size_wh[1]
@@ -152,20 +130,19 @@ def im_to_chars(im_bgr, char_pix_list, window_size_wh):
 
     for col in range(im_lut.shape[0]):
         for row in range(im_lut.shape[1]):
-
-            res[col * h : col * h + h, row * w : row * w + w] = char_pix_list[im_lut[col, row]]
+            res[col * h : col * h + h, row * w : row * w + w] = char_pix_list[
+                im_lut[col, row]
+            ]
 
     return res
 
 
-# %%
 cap = cv2.VideoCapture(0)
 
 # Check if camera was opened correctly
 if not (cap.isOpened()):
     print("Could not open video device")
 
-
 # Set the resolution
 cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
 cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
@@ -187,7 +164,3 @@ def im_to_chars(im_bgr, char_pix_list, window_size_wh):
 # When everything done, release the capture
 cap.release()
 cv2.destroyAllWindows()
-
-#%% [markdown]
-# ## BONUS
-# Try at home to add color from the original image to the char representation

slides.py

@@ -0,0 +1,126 @@
+"""
+Create or Select a Project in Google Cloud Console:
+
+Go to the Google Cloud Console.
+If you haven't already created a project, you will need to create one. Otherwise, select an existing project.
+
+Enable the Google Drive API:
+In the dashboard of your project, navigate to the "APIs & Services > Library".
+Search for "Google Drive API" and select it.
+Click "Enable" to enable the Google Drive API for your project.
+
+Create Credentials:
+After enabling the API, go to "APIs & Services > Credentials".
+Click "Create Credentials" at the top of the page.
+Choose “OAuth client ID”.
+
+Configure the OAuth consent screen:
+You'll be prompted to configure the OAuth consent screen before creating credentials. This is the screen that users will see when they authenticate with your application.
+Make sure to add a test user that is your mail.
+
+Create OAuth 2.0 Client ID:
+After configuring the consent screen, you’ll be taken back to the "Create credentials" screen.
+For "Application type", select "Web application" or another type relevant to your application.
+Set a name for the OAuth 2.0 client.
+Under "Authorized redirect URIs", add http://localhost:8080/.
+Click “Create”.
+Download the Credentials:
+
+credentials.json
+After creating the client ID, you'll see a confirmation screen showing your client ID and client secret.
+Click the download button (it looks like a download icon) to download the JSON file containing your credentials.
+Rename this file to credentials.json and place it in the directory of your Python script.
+
+Install Required Libraries:
+If you haven’t installed the required libraries for the Google API client and OAuth, you can install them via pip:
+
+pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib
+"""
+
+
+import io
+import os
+
+from google.auth.transport.requests import Request
+from google.oauth2.credentials import Credentials
+from google_auth_oauthlib.flow import InstalledAppFlow
+from googleapiclient.discovery import build
+from googleapiclient.http import MediaIoBaseDownload
+
+
+def get_credentials():
+    scopes = ["https://www.googleapis.com/auth/drive"]
+
+    creds = None
+    # The file token.json stores the user's access and refresh tokens.
+    if os.path.exists("secrets/token.json"):
+        creds = Credentials.from_authorized_user_file("secrets/token.json", scopes)
+    # If there are no (valid) credentials available, let the user log in.
+    if not creds or not creds.valid:
+        if creds and creds.expired and creds.refresh_token:
+            creds.refresh(Request())
+        else:
+            assert os.path.isfile(
+                "secrets/credentials.json"
+            ), "You need secrets/credentials.json. Download it from client secrets in: https://console.cloud.google.com/apis/credentials/oauthclient/385659825496-0123vosqnmabsha0bkdfahktrhqg5d8v.apps.googleusercontent.com?project=slides-to-pdf-412609"
+            flow = InstalledAppFlow.from_client_secrets_file(
+                "secrets/credentials.json", scopes
+            )
+            creds = flow.run_local_server(port=8080)
+        # Save the credentials for the next run
+        with open("secrets/token.json", "w") as token:
+            token.write(creds.to_json())
+    return creds
+
+
+def download_as_pdf(service, file_id, output_filename):
+    request = service.files().export_media(fileId=file_id, mimeType="application/pdf")
+    fh = io.BytesIO()
+    downloader = MediaIoBaseDownload(fh, request)
+    done = False
+    while done is False:
+        status, done = downloader.next_chunk()
+        print("Download %d%%." % int(status.progress() * 100))
+
+    with open(output_filename, "wb") as f:
+        f.write(fh.getbuffer())
+
+
+# Load credentials
+creds = Credentials.from_authorized_user_file("secrets/token.json")
+service = build("drive", "v3", credentials=creds)
+
+
+def find_file_id_by_path(service, path):
+    folder_id = "root"  # start from the root
+    for name in path.split("/"):
+        if not name:
+            continue
+        query = f"name='{name}' and '{folder_id}' in parents"
+        response = (
+            service.files()
+            .list(
+                q=query, spaces="drive", fields="files(id, name, mimeType)", pageSize=10
+            )
+            .execute()
+        )
+
+        files = response.get("files", [])
+
+        if not files:
+            raise Exception(f"No such file/dir named {name} in path {path}")
+
+        # Assuming the first found file/folder is the correct one
+        folder_id = files[0]["id"]
+
+    return folder_id
+
+
+if __name__ == "__main__":
+    path = "CV Course/slides/Intro to Computer Vision"
+
+    creds = get_credentials()
+    service = build("drive", "v3", credentials=creds)
+
+    file_id = find_file_id_by_path(service, path)
+    download_as_pdf(service, file_id, "output2.pdf")