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Upgrade cv library #3

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Upgrade cv library #3

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7 changes: 4 additions & 3 deletions build_csv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,14 +12,15 @@
if __name__== "__main__":
# output csv file where we save paths to out images
write_file= open("data.csv", "w")
data_dir= "/home/suryateja/Documents/GitHub/FaceRecognizer/output_images"
data_dir= "C:/Users/jaraa/OneDrive/Documentos/GitHub/FaceRecognizer/sorted_output_images"

# folders for each subject in root images folder
subjects= listdir(data_dir)
#print(subjects)
print(subjects)

for i in range(len(subjects)):
# all pictures in a particular folder/subject
# all pictures in a particular folder/subject
print(subjects[i])
photos= listdir(data_dir+ "/"+ subjects[i])
for photo in photos:
out_line= data_dir+ "/"+ subjects[i]+ "/"+ photo+ ";"+ str(i)
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23 changes: 13 additions & 10 deletions detect_save_images.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,11 +7,14 @@
If face(s) is(are) found, it crops and saves them at "output_path".
'''

import cv2
import cv2.cv as cv

import cv2 as cv
from os import listdir
import time




def cropImage(img, box):
[p, q, r, s]= box
# crop and save the image provided with the co-ordinates of bounding box
Expand All @@ -20,28 +23,28 @@ def cropImage(img, box):

# save the cropped image at specified location
def saveCropped(img, name):
cv2.imwrite(output_path+ name+ ".jpg", img)
cv.imwrite(output_path+ name+ ".jpg", img)

if __name__== "__main__":
# paths to input and output images
input_path= "old/input_images/"
output_path= "old/output_images/"
input_path= "input_images/"
output_path= "output_images/"

# load pre-trained frontalface cascade classifier
frontal_face= cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
input_names= listdir("/home/suryateja/Documents/GitHub/FaceRecognizer/"+ input_path)
frontal_face= cv.CascadeClassifier("haarcascade_frontalface_default.xml")
input_names= listdir("C:/Users/jaraa/OneDrive/Documentos/GitHub/FaceRecognizer/"+ input_path)

print("Starting to detect faces in images and save the cropped images to output file...")
sttime= time.clock()
i= 1
for name in input_names:
print(input_path+name)
color_img= cv2.imread(input_path+ name)
color_img= cv.imread(input_path+ name)
# converting color image to grayscale image
gray_img= cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)
gray_img= cv.cvtColor(color_img, cv.COLOR_BGR2GRAY)

# find the bounding boxes around detected faces in images
bBoxes= frontal_face.detectMultiScale(gray_img, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
bBoxes= frontal_face.detectMultiScale(gray_img, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30), flags = cv.CASCADE_SCALE_IMAGE)
#print(bBoxes)

for box in bBoxes:
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7 changes: 3 additions & 4 deletions face_detect_recognition.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,7 @@
Note: Images in here can be full sized images. (no need to crop faces)
'''

import cv2
import cv2.cv as cv
import cv2 as cv2
import numpy as np
from os import listdir
import sys, time
Expand Down Expand Up @@ -67,7 +66,7 @@ def get_images(path, size):
# initializing eigen_model and training
print("Initializing eigen FaceRecognizer and training...")
sttime= time.clock()
eigen_model= cv2.createEigenFaceRecognizer()
eigen_model= cv2.face.EigenFaceRecognizer_create()
eigen_model.train(images, labels)
print("\tSuccessfully completed training in "+ str(time.clock()- sttime)+ " Secs!")

Expand All @@ -90,7 +89,7 @@ def get_images(path, size):
#starting to detect face in given image
frontal_face= cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
#bBoxes= frontal_face.detectMultiScale(pre_image, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
bBoxes= frontal_face.detectMultiScale(pre_image, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
bBoxes= frontal_face.detectMultiScale(pre_image, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags = cv2.CASCADE_SCALE_IMAGE)

for bBox in bBoxes:
(p,q,r,s)= bBox
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