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Build a Python script that detects faces in an image using OpenCV, and saves the headshots of the detected faces to a specified directory. The script should take as input a file path to an image, a directory path to save the headshots, and output the number of faces detected in the image. Instructions:

• Use OpenCV's Haar Cascade classifier for face detection
• The script should be written in Python and use the following libraries: OpenCV, Numpy, and PIL (Python Imaging Library)
• The script should be well commented and easy to understand
• The script should be able to handle a variety of image types (e.g. jpeg, png, etc.)
• The script should be able to handle images with multiple faces
• The script should save the headshots in the specified directory with the file name in the format "face_1.jpg", "face_2.jpg", etc.

Move all image files from one S3 bucket to another S3 bucket, but only if the image has no transparent pixels.

Objective: Write a Python script that uses the Boto3 library to accomplish the following:

• List all the image files in a given S3 bucket
• Check if each image file has transparent pixels
• If an image file has no transparent pixels, copy it to a different S3 bucket
• If an image file has transparent pixels, log it in a separate file

Guidelines:

• Your script should take the name of the source and destination buckets as input
• You should use the Boto3 library to interact with S3
• You should use the Pillow library to check for transparent pixels in an image
• Your script should handle any errors that may occur during the opening of image file, copy process and anywhere else you deem necessary
• Your script should be well commented and easy to understand
• Your script should be executed from the command line