Rambikapathy image check

face_recognition/imageCheck.py

@@ -0,0 +1,86 @@
+# Import necessary libraries
+from _future_ import print_function
+import click
+import os
+import re
+import face_recognition.api as face_recognition
+import multiprocessing
+import sys
+import itertools
+from gtts import gTTS
+from playsound import playsound
+
+# Define a function to print the location of a face and speak it out loud
+def print_result(filename, location):
+    top, right, bottom, left = location
+    # Format the location as a string with the format "filename,top,right,bottom,left"
+    result = "{},{},{},{},{}".format(filename, top, right, bottom, left)
+    # Print the location to the console
+    print(result)
+    # Speak the location out loud using Google Text-to-Speech and the playsound library
+    speak(result)
+
+# Define a function to speak text using Google Text-to-Speech and the playsound library
+def speak(text):
+    tts = gTTS(text=text, lang='en')
+    tts.save('output.mp3')
+    playsound('output.mp3')
+    os.remove('output.mp3')
+
+# Define a function to test an image for faces
+def test_image(image_to_check, model, upsample):
+    unknown_image = face_recognition.load_image_file(image_to_check)
+    face_locations = face_recognition.face_locations(unknown_image, number_of_times_to_upsample=upsample, model=model)
+    # For each face location, print the location and speak it out loud
+    for face_location in face_locations:
+        print_result(image_to_check, face_location)
+
+# Define a function to get a list of all image files in a folder
+def image_files_in_folder(folder):
+    return [os.path.join(folder, f) for f in os.listdir(folder) if re.match(r'.*\.(jpg|jpeg|png)', f, flags=re.I)]
+
+# Define a function to process a list of image files using multiple processes
+def process_images_in_process_pool(images_to_check, number_of_cpus, model, upsample):
+    # Set the number of processes to use based on the number of CPU cores available
+    if number_of_cpus == -1:
+        processes = None
+    else:
+        processes = number_of_cpus
+
+    # Create a process pool using the multiprocessing library
+    context = multiprocessing
+    if "forkserver" in multiprocessing.get_all_start_methods():
+        context = multiprocessing.get_context("forkserver")
+    pool = context.Pool(processes=processes)
+
+    # Map the test_image function to each image file in the list using the process pool
+    function_parameters = zip(
+        images_to_check,
+        itertools.repeat(model),
+        itertools.repeat(upsample),
+    )
+    pool.starmap(test_image, function_parameters)
+
+# Define the main function that will be run when the script is executed
+@click.command()
+@click.argument('image_to_check')
+@click.option('--cpus', default=1, help='number of CPU cores to use in parallel. -1 means "use all in system"')
+@click.option('--model', default="hog", help='Which face detection model to use. Options are "hog" or "cnn".')
+@click.option('--upsample', default=0, help='How many times to upsample the image looking for faces. Higher numbers find smaller faces.')
+def main(image_to_check, cpus, model, upsample):
+    # Multi-core processing only supported on Python 3.4 or greater
+    if (sys.version_info < (3, 4)) and cpus != 1:
+        click.echo("WARNING: Multi-processing support requires Python 3.4 or greater. Falling back to single-threaded processing!")
+        cpus = 1
+
+    if os.path.isdir(image_to_check):
+        if cpus == 1:
+            [test_image(image_file, model, upsample) for image_file in image_files_in_folder(image_to_check)]
+        else:
+            process_images_in_process_pool(image_files_in_folder(image_to_check), cpus, model, upsample)
+    else:
+        test_image(image_to_check, model, upsample)
+
+
+if _name_ == "_main_":
+    main()