EMOTION ANALYSIS

Run on Google Colab

View source on GitHub

Download notebook

Connect to EvaDB

%pip install --quiet "evadb[vision,notebook]"
%pip install --quiet facenet_pytorch
import evadb
cursor = evadb.connect().cursor()

Note: you may need to restart the kernel to use updated packages.

Note: you may need to restart the kernel to use updated packages.

Download Video

# A video of a happy person
!wget -nc "https://www.dropbox.com/s/gzfhwmib7u804zy/defhappy.mp4?raw=1" -O defhappy.mp4

# Adding Emotion detection
!wget -nc https://raw.githubusercontent.com/georgia-tech-db/eva/master/evadb/udfs/emotion_detector.py

# Adding Face Detector
!wget -nc https://raw.githubusercontent.com/georgia-tech-db/eva/master/evadb/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.

Load video for analysis

response = cursor.query("DROP TABLE IF EXISTS HAPPY;").df()
print(response)
cursor.load(file_regex="defhappy.mp4", table_name="HAPPY", format="VIDEO").df()

                             0
0  Table: HAPPY does not exist

	0
0	Number of loaded VIDEO: 1

Create a function for analyzing the frames

cursor.query("""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';
        """).df()

cursor.query("""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';
        """).df()

	0
0	UDF FaceDetector already exists, nothing added.

Run the Face Detection UDF on video

query = cursor.table("HAPPY")
query = query.filter("id < 10")
query = query.select("id, FaceDetector(data)")

query.df()

	happy.id	facedetector.bboxes	facedetector.scores
0	0	[[502, 94, 762, 435], [238, 296, 325, 398]]	[0.99990165, 0.79820246]
1	1	[[501, 96, 763, 435]]	[0.999918]
2	2	[[504, 97, 766, 437]]	[0.9999138]
3	3	[[498, 90, 776, 446]]	[0.99996686]
4	4	[[496, 99, 767, 444]]	[0.9999982]
5	5	[[499, 87, 777, 448], [236, 305, 324, 407]]	[0.9999136, 0.8369736]
6	6	[[500, 89, 778, 449]]	[0.9999131]
7	7	[[501, 89, 781, 452]]	[0.9999124]
8	8	[[503, 90, 783, 450]]	[0.99994683]
9	9	[[508, 87, 786, 447]]	[0.999949]

Run the Emotion Detection UDF on the outputs of the Face Detection UDF

query = cursor.table("HAPPY")
query = query.cross_apply("UNNEST(FaceDetector(data))", "Face(bbox, conf)")
query = query.filter("id < 15")
query = query.select("id, bbox, EmotionDetector(Crop(data, bbox))")

response = query.df()

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'

annotate_video(response, input_path, output_path)

Dropping an User-Defined Function (UDF)

cursor.drop_udf("EmotionDetector").df()
cursor.drop_udf("FaceDetector").df()

	0
0	UDF FaceDetector successfully dropped