EMOTION ANALYSIS#
 Run on Google Colab
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 View source on GitHub
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 Download notebook
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Start EVA Server#
We are reusing the start server notebook for launching the EVA server
!wget -nc "https://raw.githubusercontent.com/georgia-tech-db/eva/master/tutorials/00-start-eva-server.ipynb"
%run 00-start-eva-server.ipynb
cursor = connect_to_server()
File ‘00-start-eva-server.ipynb’ already there; not retrieving.
nohup eva_server > eva.log 2>&1 &
Note: you may need to restart the kernel to use updated packages.
Video Files#
getting some video files to test
# A video of a happy person
!wget -nc https://www.dropbox.com/s/gzfhwmib7u804zy/defhappy.mp4
# Adding Emotion detection
!wget -nc https://raw.githubusercontent.com/georgia-tech-db/eva/master/eva/udfs/emotion_detector.py
# Adding Face Detector
!wget -nc https://raw.githubusercontent.com/georgia-tech-db/eva/master/eva/udfs/face_detector.py
File ‘defhappy.mp4’ already there; not retrieving.
File ‘emotion_detector.py’ already there; not retrieving.
File ‘face_detector.py’ already there; not retrieving.
Adding the video file to EVADB for analysis#
cursor.execute('DROP TABLE HAPPY')
response = cursor.fetch_all()
print(response)
cursor.execute('LOAD VIDEO "defhappy.mp4" INTO HAPPY')
response = cursor.fetch_all()
print(response)
@status: ResponseStatus.SUCCESS
@batch:
0
0 Table Successfully dropped: HAPPY
@query_time: 0.039770226925611496
@status: ResponseStatus.SUCCESS
@batch:
0
0 Number of loaded VIDEO: 1
@query_time: 0.0870923399925232
Visualize Video#
from IPython.display import Video
Video("defhappy.mp4", height=450, width=800, embed=True)
Create an user-defined function(UDF) for analyzing the frames#
cursor.execute("""CREATE UDF IF NOT EXISTS EmotionDetector
INPUT (frame NDARRAY UINT8(3, ANYDIM, ANYDIM))
OUTPUT (labels NDARRAY STR(ANYDIM), scores NDARRAY FLOAT32(ANYDIM))
TYPE Classification IMPL 'emotion_detector.py';
""")
response = cursor.fetch_all()
print(response)
cursor.execute("""CREATE UDF IF NOT EXISTS FaceDetector
INPUT (frame NDARRAY UINT8(3, ANYDIM, ANYDIM))
OUTPUT (bboxes NDARRAY FLOAT32(ANYDIM, 4),
scores NDARRAY FLOAT32(ANYDIM))
TYPE FaceDetection
IMPL 'face_detector.py';
""")
response = cursor.fetch_all()
print(response)
@status: ResponseStatus.SUCCESS
@batch:
0
0 UDF EmotionDetector already exists, nothing added.
@query_time: 0.0088406961876899
@status: ResponseStatus.SUCCESS
@batch:
0
0 UDF FaceDetector already exists, nothing added.
@query_time: 0.0053917099721729755
Run the Face Detection UDF on video#
cursor.execute("""SELECT id, FaceDetector(data)
FROM HAPPY WHERE id<10""")
response = cursor.fetch_all()
print(response)
@status: ResponseStatus.SUCCESS
@batch:
happy.id facedetector.bboxes facedetector.scores
0 0 [[493.4729 89.1079 769.5677 441.7005]] [0.9997701]
1 1 [[501.57224 89.75874 773.5567 442.99564]] [0.99984527]
2 2 [[503.33987 92.72798 773.9184 444.89667]] [0.9998871]
3 3 [[506.64032 91.81204 774.7923 446.76813]] [0.9994814]
4 4 [[508.46338 93.29222 777.00275 448.40146]] [0.99958366]
5 5 [[506.592 99.09416 772.56396 445.32654]] [0.99950814]
6 6 [[508.85898 98.99975 774.42487 450.29272]] [0.999731]
7 7 [[512.3384 98.99459 781.2488 451.23007]] [0.9997571]
8 8 [[513.3356 97.60012 783.34937 451.96744]] [0.99983895]
9 9 [[514.25696 98.21023 784.0434 452.2896 ]] [0.9998286]
@query_time: 0.7126878660637885
Run the Emotion Detection UDF on the outputs of the Face Detection UDF#
cursor.execute("""SELECT id, bbox, EmotionDetector(Crop(data, bbox))
FROM HAPPY JOIN LATERAL UNNEST(FaceDetector(data)) AS Face(bbox, conf)
WHERE id < 15;""")
response = cursor.fetch_all()
print(response)
@status: ResponseStatus.SUCCESS
@batch:
happy.id Face.bbox \
0 0 [493.4729, 89.1079, 769.5677, 441.7005]
1 1 [501.57224, 89.75874, 773.5567, 442.99564]
2 2 [503.33987, 92.728, 773.9184, 444.89667]
3 3 [506.64032, 91.81204, 774.7923, 446.76813]
4 4 [508.46338, 93.29221, 777.00275, 448.40146]
5 5 [506.592, 99.094154, 772.56396, 445.32654]
6 6 [508.85898, 98.99975, 774.42487, 450.29272]
7 7 [512.3384, 98.99459, 781.2488, 451.23007]
8 8 [513.3356, 97.60013, 783.34937, 451.96744]
9 9 [514.25696, 98.21022, 784.0434, 452.2896]
10 10 [515.7314, 97.13689, 786.78296, 452.3648]
11 10 [50.10141, 524.19183, 120.75999, 599.16064]
12 11 [513.3706, 96.37443, 784.8802, 452.24167]
13 12 [512.84, 95.54421, 785.3812, 453.33618]
14 13 [512.6082, 94.14093, 785.8945, 452.27637]
15 14 [512.9006, 93.912674, 786.7881, 453.10126]
emotiondetector.labels emotiondetector.scores
0 happy 0.999640
1 happy 0.999674
2 happy 0.999696
3 happy 0.999686
4 happy 0.999690
5 happy 0.999709
6 happy 0.999732
7 happy 0.999721
8 happy 0.999709
9 happy 0.999718
10 happy 0.999701
11 neutral 0.998291
12 happy 0.999687
13 happy 0.999676
14 happy 0.999639
15 happy 0.999649
@query_time: 1.23868982703425
import cv2
from pprint import pprint
from matplotlib import pyplot as plt
def annotate_video(detections, input_video_path, output_video_path):
color1=(207, 248, 64)
color2=(255, 49, 49)
thickness=4
vcap = cv2.VideoCapture(input_video_path)
width = int(vcap.get(3))
height = int(vcap.get(4))
fps = vcap.get(5)
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v') #codec
video=cv2.VideoWriter(output_video_path, fourcc, fps, (width,height))
frame_id = 0
# Capture frame-by-frame
# ret = 1 if the video is captured; frame is the image
ret, frame = vcap.read()
while ret:
df = detections
df = df[['Face.bbox', 'emotiondetector.labels', 'emotiondetector.scores']][df.index == frame_id]
if df.size:
x1, y1, x2, y2 = df['Face.bbox'].values[0]
label = df['emotiondetector.labels'].values[0]
score = df['emotiondetector.scores'].values[0]
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
# object bbox
frame=cv2.rectangle(frame, (x1, y1), (x2, y2), color1, thickness)
# object label
cv2.putText(frame, label, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color1, thickness)
# object score
cv2.putText(frame, str(round(score, 5)), (x1+120, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color1, thickness)
# frame label
cv2.putText(frame, 'Frame ID: ' + str(frame_id), (700, 500), cv2.FONT_HERSHEY_SIMPLEX, 1.2, color2, thickness)
video.write(frame)
# Show every fifth frame
if frame_id % 5 == 0:
plt.imshow(frame)
plt.show()
frame_id+=1
ret, frame = vcap.read()
video.release()
vcap.release()
from ipywidgets import Video, Image
input_path = 'defhappy.mp4'
output_path = 'video.mp4'
dataframe = response.batch.frames
annotate_video(dataframe, input_path, output_path)
