1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
### AWS Face and Emotion recognition ###

library(paws)  # used for AWS configuration
library(magick)  # used for image functions
library(tidyverse)

aws_access_key_id = "################"
aws_secret_access_key = "################"

svc <- rekognition()

### Create an AWS collection (server-side containers) ###
# Create a library of faces used for determining the identity of a new photo

svc$create_collection(CollectionId = "photos-r")
#svc$delete_collection(CollectionId = "photos-r")

# photos stored in directory within folders containing the person name
# i.e. all "Danny" photos are in folder named "Danny"

# Get the list of files
path = "~/Desktop/face_detection/photos"
filenames <- list.files(path, recursive=TRUE)

# Loop through the files in the specified folder, add and index them in the collection
for(f in filenames) {
  imgFile = paste(path,f,sep="/")
  # Get the person name, which is embedded in the last file path folder name
  imgName = unlist(strsplit(f,split="/"))[[1]]
  # Add the photos and the name to the AWS collection
  svc$index_faces(CollectionId="photos-r", Image=list(Bytes=imgFile), ExternalImageId=imgName, DetectionAttributes=list())
}

svc$list_faces(CollectionId = "photos-r")

### Label and identify the face of a new photo ###

# Grab a new photo with multiple faces
group_photo = "~/Desktop/face_detection/img1.JPG"
group_file_name = unlist(strsplit(group_photo,split="/"))[[4]]  # used for writing out annotated file

# Read the photo using magick
img = image_read(group_photo)

# Get basic info about the photo to be used for annotation
inf = image_info(img)

# Detect the faces in the image and pull all attributes associated with faces
o = svc$detect_faces(Image=list(Bytes=group_photo), Attributes="ALL")

# Get the face details
all_faces = o$FaceDetails
length(all_faces)


### For each face in photo, draw a rectange with the name and emotions ###
new.img = img  # Duplicate the original image
people_df <- NULL

for(face in all_faces) {

  # Grab emotions from AWS rekognition model
  emo_label = ""
  for(emo in face$Emotions) {
    emo_label = paste(emo_label, emo$Type, " = ", round(emo$Confidence, 2), "\n", sep="")
  }
  
  # Grab ages from AWS rekognition
  age_label = ""
  for(age in list(face$AgeRange)) {
    age_label = paste(age_label, "AGE ESTIMATE: = ", (age$Low+age$High)/2, "\n", sep="")
  }
  
  # Grab genders from AWS rekognition
  #gender_label = ""
  #for(gndr in list(face$Gender)) {
  #  gender_label = paste(gender_label, gndr$Value, " = ", round(gndr$Confidence, 2), "\n", sep="")
  #}
  
  # Append all lists together
  final_label = ""
  final_label <- rbind(emo_label, age_label)
  final_label <- paste(final_label, collapse = '')
  
  # Identify the coordinates of the face. Note that AWS returns percentage values of the total image size. This is
  # why the image info object above is needed
  box = face$BoundingBox
  image_width=inf$width
  image_height=inf$height
  x1 = box$Left*image_width
  y1 = box$Top*image_height
  x2 = x1 + box$Width*image_width
  y2 = y1 + box$Height*image_height  
  
  # Create a subset image in memory that is just cropped around the focal face
  img.crop = image_crop(img, paste(box$Width*image_width,"x",box$Height*image_height,"+",x1,"+",y1, sep=""))
  img.crop = image_write(img.crop, path = NULL, format = "png")
  
  # Search in a specified collection to see if we can label the identity of the face is in this crop
  o = svc$search_faces_by_image(CollectionId="photos-r",Image=list(Bytes=img.crop), FaceMatchThreshold=70)
  
  # Create a graphics device version of the larger photo that we can annotate
  new.img = image_draw(new.img)
  
  # If the face matches something in the collection, then add the name to the image
  if(length(o$FaceMatches) > 0) {
    faceName = o$FaceMatches[[1]]$Face$ExternalImageId
    faceConfidence = round(o$FaceMatches[[1]]$Face$Confidence,3)
    print(paste("Detected: ", faceName, sep=""))
    
    # Annotate with the name of the person
    text(x=x1+(box$Width*image_width)/2, y=y1-20, faceName, adj=0.5, cex=3, col="green")
  }
  
  # Draw a rectangle around the face
  rect(x1,y1,x2,y2, border="red", lty="dashed", lwd=5)   
  
  # Annotate the photo with the emotions information
  text(x=x1+(box$Width*image_width)/2, y=y1+50, final_label, pos=1, cex=1.5, col="red")     
  
  # Create a dataframe of individual data appended together
  individual_emotion_df <- do.call(rbind.data.frame, face$Emotions)
  
  individual_emotion_df <- individual_emotion_df %>% 
    spread(Type, Confidence) %>%
    add_column(faceName)
  individual_emotion_df$image <- strsplit(group_file_name, ".JPG")
  
  individual_emotion_df <- individual_emotion_df%>%
    select(faceName, image, everything()) # move faceName to beginning
  
  individual_age_df <- data.frame(face$AgeRange)
  colnames(individual_age_df) <- c("age_low", "age_high")
  
  individual_df <- cbind(individual_emotion_df, individual_age_df)
  
  people_df <- rbind(individual_df, people_df)
  
}
dev.off()

people_df$age_est <- (people_df$age_low + people_df$age_high)/2
names(people_df) <- tolower(names(people_df))
head(people_df)

# Write the image out to file 
image_write(new.img, path=paste0("~/Desktop/face_detection/annotated/annotated_", group_file_name))