The Project

  1. This is a project with minimal scaffolding. Expect to use the the discussion forums to gain insights! It’s not cheating to ask others for opinions or perspectives!
  2. Be inquisitive, try out new things.
  3. Use the previous modules for insights into how to complete the functions! You'll have to combine Pillow, OpenCV, and Pytesseract
  4. There are hints provided in Coursera, feel free to explore the hints if needed. Each hint provide progressively more details on how to solve the issue. This project is intended to be comprehensive and difficult if you do it without the hints.

The Assignment

Take a ZIP file) of images and process them, using a library built into python that you need to learn how to use. A ZIP file takes several different files and compresses them, thus saving space, into one single file. The files in the ZIP file we provide are newspaper images (like you saw in week 3). Your task is to write python code which allows one to search through the images looking for the occurrences of keywords and faces. E.g. if you search for "pizza" it will return a contact sheet of all of the faces which were located on the newspaper page which mentions "pizza". This will test your ability to learn a new (library), your ability to use OpenCV to detect faces, your ability to use tesseract to do optical character recognition, and your ability to use PIL to composite images together into contact sheets.

Each page of the newspapers is saved as a single PNG image in a file called These newspapers are in english, and contain a variety of stories, advertisements and images. Note: This file is fairly large (~200 MB) and may take some time to work with, I would encourage you to use for testing.

Here's an example of the output expected. Using the file, if I search for the string "Christopher" I should see the following image: Christopher Search If I were to use the file and search for "Mark" I should see the following image (note that there are times when there are no faces on a page, but a word is found!): Mark Search

Note: That big file can take some time to process - for me it took nearly ten minutes! Use the small one for testing.

In [13]:
import zipfile

from PIL import Image
import pytesseract
import cv2 as cv
import numpy as np
import io

# loading the face detection classifier
face_cascade = cv.CascadeClassifier('readonly/haarcascade_frontalface_default.xml')

# the rest is up to you!

# specifying the zip file name
file_name = "readonly/"
In [14]:
# create empty list for file info as dictionary
fileList = []
# opening the zip file in READ mode
with zipfile.ZipFile(file_name, 'r') as zip:
    for name in zip.namelist():
        # reading the file data
        data =
        # convert file data to PIL object
        bindata = io.BytesIO(data)
        image =
        # storing file name and PIL object
        fileList.append({'fileName': name, 'PILObject': image})
In [15]:
# OCR and face recognition
for i in range(0, len(fileList)):
    # convert to b/w and OCR
    image_bw = fileList[i]['PILObject'].convert('1')
    text = pytesseract.image_to_string(image_bw)
    # store text from OCR scan
    fileList[i]['textOCR'] = text
    # convert image tot OpenCV object
    image_px = np.array(fileList[i]['PILObject'])
    # convert to b/w and face recognition
    gray = cv.cvtColor(image_px, cv.COLOR_RGB2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5, minSize=(40,40))
    # store rectangles from face recognition
    fileList[i]['facesREC'] = faces
In [18]:
textString = "Mark"

# create contact sheet
for i in range(0, len(fileList)):
    # check if the name is in the textOCR
    if textString in fileList[i]['textOCR']:
        print("Results found in in file {}".format(fileList[i]['fileName']))
        if len(fileList[i]['facesREC']) != 0:
            # create empty contact sheet for thumbnailes
            thumbWidth, thumbHeight = 100, 100
            sheetSize = int(len(fileList[i]['facesREC'])/5)
            if len(fileList[i]['facesREC'])%5 != 0:
                sheetSize += 1
            contact_sheet ='RGB', (thumbWidth * 5, thumbHeight * sheetSize))
            # create image for cropping faces
            cropimage = fileList[i]['PILObject']
            cx, cy = 0, 0
            # loop over the rectangles
            for x, y, w, h in fileList[i]['facesREC']:
                img = cropimage.crop((x, y, x + w, y + h))
                img.thumbnail((thumbWidth, thumbHeight))
                contact_sheet.paste(img, (cx, cy))
                # Next position
                if cx + thumbWidth == contact_sheet.width:
                    cx = 0
                    cy += thumbHeight
                    cx += thumbWidth
            print("But there were no faces in that file!")
Results found in in file a-0.png
Results found in in file a-1.png
Results found in in file a-10.png
But there were no faces in that file!
Results found in in file a-13.png
Results found in in file a-2.png
Results found in in file a-3.png
Results found in in file a-8.png
But there were no faces in that file!
In [ ]: