├── README.md
├── consumer
├── consumer.py
└── templates
│ └── index.html
├── data
├── coco.names
└── yolov3-320.cfg
├── producer
└── producer.py
└── requirements.txt
/README.md:
--------------------------------------------------------------------------------
1 | # Object-Detection-using-Spark-Docker-Kafka-Video-Streaming
2 |
3 | ### Please download the CNN model from below docker registry and the model code has deployed inside on the docker images
4 |
5 | docker pull yesdineshkumar/bdpmodel
6 |
7 | docker run --name c1_bdpmodel -it -v /c/Users/esdin/OneDrive/Documents/ModelDocker:/data yesdineshkumar/bdpmodel bash
8 |
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/consumer/consumer.py:
--------------------------------------------------------------------------------
1 |
2 | from flask import Flask, Response, render_template
3 | from kafka import KafkaConsumer
4 |
5 | # Fire up the Kafka Consumer
6 | topic = "kafka-video-topic"
7 |
8 | consumer = KafkaConsumer(
9 | topic,
10 | bootstrap_servers=['192.168.99.100:9092'])
11 |
12 |
13 | # Set the consumer in a Flask App
14 | app = Flask(__name__)
15 |
16 |
17 | @app.route('/')
18 | def index():
19 | return render_template('index.html')
20 |
21 |
22 | @app.route('/read_video', methods=['GET'])
23 | def read_video():
24 | """
25 | This is the heart of our video display. Notice we set the mimetype to
26 | multipart/x-mixed-replace. This tells Flask to replace any old images with
27 | new values streaming through the pipeline.
28 | """
29 | return Response(
30 | get_video_stream(),
31 | mimetype='multipart/x-mixed-replace; boundary=frame')
32 |
33 |
34 | def get_video_stream():
35 | """
36 | Here is where we recieve streamed images from the Kafka Server and convert
37 | them to a Flask-readable format.
38 | """
39 | for msg in consumer:
40 | print("msg ", msg)
41 | yield (b'--frame\r\n'
42 | b'Content-Type: image/jpg\r\n\r\n' + msg.value + b'\r\n\r\n')
43 |
44 |
45 | if __name__ == "__main__":
46 | app.run(host='0.0.0.0', debug=True)
47 |
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/consumer/templates/index.html:
--------------------------------------------------------------------------------
1 |
2 |
3 | BigDatapedia Kafka Video Streaming
4 |
5 |
6 | Object Detection using Kafka Video Streaming
7 |  }})
8 |
9 |
10 |
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/data/coco.names:
--------------------------------------------------------------------------------
1 | person
2 | bicycle
3 | car
4 | motorbike
5 | aeroplane
6 | bus
7 | train
8 | truck
9 | boat
10 | traffic light
11 | fire hydrant
12 | stop sign
13 | parking meter
14 | bench
15 | bird
16 | cat
17 | dog
18 | horse
19 | sheep
20 | cow
21 | elephant
22 | bear
23 | zebra
24 | giraffe
25 | backpack
26 | umbrella
27 | handbag
28 | tie
29 | suitcase
30 | frisbee
31 | skis
32 | snowboard
33 | sports ball
34 | kite
35 | baseball bat
36 | baseball glove
37 | skateboard
38 | surfboard
39 | tennis racket
40 | bottle
41 | wine glass
42 | cup
43 | fork
44 | knife
45 | spoon
46 | bowl
47 | banana
48 | apple
49 | sandwich
50 | orange
51 | broccoli
52 | carrot
53 | hot dog
54 | pizza
55 | donut
56 | cake
57 | chair
58 | sofa
59 | potted plant
60 | bed
61 | dining table
62 | toilet
63 | tvmonitor
64 | laptop
65 | mouse
66 | remote
67 | keyboard
68 | cell phone
69 | microwave
70 | oven
71 | toaster
72 | sink
73 | refrigerator
74 | book
75 | clock
76 | vase
77 | scissors
78 | teddy bear
79 | hair drier
80 | toothbrush
81 |
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/data/yolov3-320.cfg:
--------------------------------------------------------------------------------
1 | [net]
2 | # Testing
3 | # batch=1
4 | # subdivisions=1
5 | # Training
6 | batch=64
7 | subdivisions=16
8 | width=608
9 | height=608
10 | channels=3
11 | momentum=0.9
12 | decay=0.0005
13 | angle=0
14 | saturation = 1.5
15 | exposure = 1.5
16 | hue=.1
17 |
18 | learning_rate=0.001
19 | burn_in=1000
20 | max_batches = 500200
21 | policy=steps
22 | steps=400000,450000
23 | scales=.1,.1
24 |
25 | [convolutional]
26 | batch_normalize=1
27 | filters=32
28 | size=3
29 | stride=1
30 | pad=1
31 | activation=leaky
32 |
33 | # Downsample
34 |
35 | [convolutional]
36 | batch_normalize=1
37 | filters=64
38 | size=3
39 | stride=2
40 | pad=1
41 | activation=leaky
42 |
43 | [convolutional]
44 | batch_normalize=1
45 | filters=32
46 | size=1
47 | stride=1
48 | pad=1
49 | activation=leaky
50 |
51 | [convolutional]
52 | batch_normalize=1
53 | filters=64
54 | size=3
55 | stride=1
56 | pad=1
57 | activation=leaky
58 |
59 | [shortcut]
60 | from=-3
61 | activation=linear
62 |
63 | # Downsample
64 |
65 | [convolutional]
66 | batch_normalize=1
67 | filters=128
68 | size=3
69 | stride=2
70 | pad=1
71 | activation=leaky
72 |
73 | [convolutional]
74 | batch_normalize=1
75 | filters=64
76 | size=1
77 | stride=1
78 | pad=1
79 | activation=leaky
80 |
81 | [convolutional]
82 | batch_normalize=1
83 | filters=128
84 | size=3
85 | stride=1
86 | pad=1
87 | activation=leaky
88 |
89 | [shortcut]
90 | from=-3
91 | activation=linear
92 |
93 | [convolutional]
94 | batch_normalize=1
95 | filters=64
96 | size=1
97 | stride=1
98 | pad=1
99 | activation=leaky
100 |
101 | [convolutional]
102 | batch_normalize=1
103 | filters=128
104 | size=3
105 | stride=1
106 | pad=1
107 | activation=leaky
108 |
109 | [shortcut]
110 | from=-3
111 | activation=linear
112 |
113 | # Downsample
114 |
115 | [convolutional]
116 | batch_normalize=1
117 | filters=256
118 | size=3
119 | stride=2
120 | pad=1
121 | activation=leaky
122 |
123 | [convolutional]
124 | batch_normalize=1
125 | filters=128
126 | size=1
127 | stride=1
128 | pad=1
129 | activation=leaky
130 |
131 | [convolutional]
132 | batch_normalize=1
133 | filters=256
134 | size=3
135 | stride=1
136 | pad=1
137 | activation=leaky
138 |
139 | [shortcut]
140 | from=-3
141 | activation=linear
142 |
143 | [convolutional]
144 | batch_normalize=1
145 | filters=128
146 | size=1
147 | stride=1
148 | pad=1
149 | activation=leaky
150 |
151 | [convolutional]
152 | batch_normalize=1
153 | filters=256
154 | size=3
155 | stride=1
156 | pad=1
157 | activation=leaky
158 |
159 | [shortcut]
160 | from=-3
161 | activation=linear
162 |
163 | [convolutional]
164 | batch_normalize=1
165 | filters=128
166 | size=1
167 | stride=1
168 | pad=1
169 | activation=leaky
170 |
171 | [convolutional]
172 | batch_normalize=1
173 | filters=256
174 | size=3
175 | stride=1
176 | pad=1
177 | activation=leaky
178 |
179 | [shortcut]
180 | from=-3
181 | activation=linear
182 |
183 | [convolutional]
184 | batch_normalize=1
185 | filters=128
186 | size=1
187 | stride=1
188 | pad=1
189 | activation=leaky
190 |
191 | [convolutional]
192 | batch_normalize=1
193 | filters=256
194 | size=3
195 | stride=1
196 | pad=1
197 | activation=leaky
198 |
199 | [shortcut]
200 | from=-3
201 | activation=linear
202 |
203 |
204 | [convolutional]
205 | batch_normalize=1
206 | filters=128
207 | size=1
208 | stride=1
209 | pad=1
210 | activation=leaky
211 |
212 | [convolutional]
213 | batch_normalize=1
214 | filters=256
215 | size=3
216 | stride=1
217 | pad=1
218 | activation=leaky
219 |
220 | [shortcut]
221 | from=-3
222 | activation=linear
223 |
224 | [convolutional]
225 | batch_normalize=1
226 | filters=128
227 | size=1
228 | stride=1
229 | pad=1
230 | activation=leaky
231 |
232 | [convolutional]
233 | batch_normalize=1
234 | filters=256
235 | size=3
236 | stride=1
237 | pad=1
238 | activation=leaky
239 |
240 | [shortcut]
241 | from=-3
242 | activation=linear
243 |
244 | [convolutional]
245 | batch_normalize=1
246 | filters=128
247 | size=1
248 | stride=1
249 | pad=1
250 | activation=leaky
251 |
252 | [convolutional]
253 | batch_normalize=1
254 | filters=256
255 | size=3
256 | stride=1
257 | pad=1
258 | activation=leaky
259 |
260 | [shortcut]
261 | from=-3
262 | activation=linear
263 |
264 | [convolutional]
265 | batch_normalize=1
266 | filters=128
267 | size=1
268 | stride=1
269 | pad=1
270 | activation=leaky
271 |
272 | [convolutional]
273 | batch_normalize=1
274 | filters=256
275 | size=3
276 | stride=1
277 | pad=1
278 | activation=leaky
279 |
280 | [shortcut]
281 | from=-3
282 | activation=linear
283 |
284 | # Downsample
285 |
286 | [convolutional]
287 | batch_normalize=1
288 | filters=512
289 | size=3
290 | stride=2
291 | pad=1
292 | activation=leaky
293 |
294 | [convolutional]
295 | batch_normalize=1
296 | filters=256
297 | size=1
298 | stride=1
299 | pad=1
300 | activation=leaky
301 |
302 | [convolutional]
303 | batch_normalize=1
304 | filters=512
305 | size=3
306 | stride=1
307 | pad=1
308 | activation=leaky
309 |
310 | [shortcut]
311 | from=-3
312 | activation=linear
313 |
314 |
315 | [convolutional]
316 | batch_normalize=1
317 | filters=256
318 | size=1
319 | stride=1
320 | pad=1
321 | activation=leaky
322 |
323 | [convolutional]
324 | batch_normalize=1
325 | filters=512
326 | size=3
327 | stride=1
328 | pad=1
329 | activation=leaky
330 |
331 | [shortcut]
332 | from=-3
333 | activation=linear
334 |
335 |
336 | [convolutional]
337 | batch_normalize=1
338 | filters=256
339 | size=1
340 | stride=1
341 | pad=1
342 | activation=leaky
343 |
344 | [convolutional]
345 | batch_normalize=1
346 | filters=512
347 | size=3
348 | stride=1
349 | pad=1
350 | activation=leaky
351 |
352 | [shortcut]
353 | from=-3
354 | activation=linear
355 |
356 |
357 | [convolutional]
358 | batch_normalize=1
359 | filters=256
360 | size=1
361 | stride=1
362 | pad=1
363 | activation=leaky
364 |
365 | [convolutional]
366 | batch_normalize=1
367 | filters=512
368 | size=3
369 | stride=1
370 | pad=1
371 | activation=leaky
372 |
373 | [shortcut]
374 | from=-3
375 | activation=linear
376 |
377 | [convolutional]
378 | batch_normalize=1
379 | filters=256
380 | size=1
381 | stride=1
382 | pad=1
383 | activation=leaky
384 |
385 | [convolutional]
386 | batch_normalize=1
387 | filters=512
388 | size=3
389 | stride=1
390 | pad=1
391 | activation=leaky
392 |
393 | [shortcut]
394 | from=-3
395 | activation=linear
396 |
397 |
398 | [convolutional]
399 | batch_normalize=1
400 | filters=256
401 | size=1
402 | stride=1
403 | pad=1
404 | activation=leaky
405 |
406 | [convolutional]
407 | batch_normalize=1
408 | filters=512
409 | size=3
410 | stride=1
411 | pad=1
412 | activation=leaky
413 |
414 | [shortcut]
415 | from=-3
416 | activation=linear
417 |
418 |
419 | [convolutional]
420 | batch_normalize=1
421 | filters=256
422 | size=1
423 | stride=1
424 | pad=1
425 | activation=leaky
426 |
427 | [convolutional]
428 | batch_normalize=1
429 | filters=512
430 | size=3
431 | stride=1
432 | pad=1
433 | activation=leaky
434 |
435 | [shortcut]
436 | from=-3
437 | activation=linear
438 |
439 | [convolutional]
440 | batch_normalize=1
441 | filters=256
442 | size=1
443 | stride=1
444 | pad=1
445 | activation=leaky
446 |
447 | [convolutional]
448 | batch_normalize=1
449 | filters=512
450 | size=3
451 | stride=1
452 | pad=1
453 | activation=leaky
454 |
455 | [shortcut]
456 | from=-3
457 | activation=linear
458 |
459 | # Downsample
460 |
461 | [convolutional]
462 | batch_normalize=1
463 | filters=1024
464 | size=3
465 | stride=2
466 | pad=1
467 | activation=leaky
468 |
469 | [convolutional]
470 | batch_normalize=1
471 | filters=512
472 | size=1
473 | stride=1
474 | pad=1
475 | activation=leaky
476 |
477 | [convolutional]
478 | batch_normalize=1
479 | filters=1024
480 | size=3
481 | stride=1
482 | pad=1
483 | activation=leaky
484 |
485 | [shortcut]
486 | from=-3
487 | activation=linear
488 |
489 | [convolutional]
490 | batch_normalize=1
491 | filters=512
492 | size=1
493 | stride=1
494 | pad=1
495 | activation=leaky
496 |
497 | [convolutional]
498 | batch_normalize=1
499 | filters=1024
500 | size=3
501 | stride=1
502 | pad=1
503 | activation=leaky
504 |
505 | [shortcut]
506 | from=-3
507 | activation=linear
508 |
509 | [convolutional]
510 | batch_normalize=1
511 | filters=512
512 | size=1
513 | stride=1
514 | pad=1
515 | activation=leaky
516 |
517 | [convolutional]
518 | batch_normalize=1
519 | filters=1024
520 | size=3
521 | stride=1
522 | pad=1
523 | activation=leaky
524 |
525 | [shortcut]
526 | from=-3
527 | activation=linear
528 |
529 | [convolutional]
530 | batch_normalize=1
531 | filters=512
532 | size=1
533 | stride=1
534 | pad=1
535 | activation=leaky
536 |
537 | [convolutional]
538 | batch_normalize=1
539 | filters=1024
540 | size=3
541 | stride=1
542 | pad=1
543 | activation=leaky
544 |
545 | [shortcut]
546 | from=-3
547 | activation=linear
548 |
549 | ######################
550 |
551 | [convolutional]
552 | batch_normalize=1
553 | filters=512
554 | size=1
555 | stride=1
556 | pad=1
557 | activation=leaky
558 |
559 | [convolutional]
560 | batch_normalize=1
561 | size=3
562 | stride=1
563 | pad=1
564 | filters=1024
565 | activation=leaky
566 |
567 | [convolutional]
568 | batch_normalize=1
569 | filters=512
570 | size=1
571 | stride=1
572 | pad=1
573 | activation=leaky
574 |
575 | [convolutional]
576 | batch_normalize=1
577 | size=3
578 | stride=1
579 | pad=1
580 | filters=1024
581 | activation=leaky
582 |
583 | [convolutional]
584 | batch_normalize=1
585 | filters=512
586 | size=1
587 | stride=1
588 | pad=1
589 | activation=leaky
590 |
591 | [convolutional]
592 | batch_normalize=1
593 | size=3
594 | stride=1
595 | pad=1
596 | filters=1024
597 | activation=leaky
598 |
599 | [convolutional]
600 | size=1
601 | stride=1
602 | pad=1
603 | filters=255
604 | activation=linear
605 |
606 |
607 | [yolo]
608 | mask = 6,7,8
609 | anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
610 | classes=80
611 | num=9
612 | jitter=.3
613 | ignore_thresh = .7
614 | truth_thresh = 1
615 | random=1
616 |
617 |
618 | [route]
619 | layers = -4
620 |
621 | [convolutional]
622 | batch_normalize=1
623 | filters=256
624 | size=1
625 | stride=1
626 | pad=1
627 | activation=leaky
628 |
629 | [upsample]
630 | stride=2
631 |
632 | [route]
633 | layers = -1, 61
634 |
635 |
636 |
637 | [convolutional]
638 | batch_normalize=1
639 | filters=256
640 | size=1
641 | stride=1
642 | pad=1
643 | activation=leaky
644 |
645 | [convolutional]
646 | batch_normalize=1
647 | size=3
648 | stride=1
649 | pad=1
650 | filters=512
651 | activation=leaky
652 |
653 | [convolutional]
654 | batch_normalize=1
655 | filters=256
656 | size=1
657 | stride=1
658 | pad=1
659 | activation=leaky
660 |
661 | [convolutional]
662 | batch_normalize=1
663 | size=3
664 | stride=1
665 | pad=1
666 | filters=512
667 | activation=leaky
668 |
669 | [convolutional]
670 | batch_normalize=1
671 | filters=256
672 | size=1
673 | stride=1
674 | pad=1
675 | activation=leaky
676 |
677 | [convolutional]
678 | batch_normalize=1
679 | size=3
680 | stride=1
681 | pad=1
682 | filters=512
683 | activation=leaky
684 |
685 | [convolutional]
686 | size=1
687 | stride=1
688 | pad=1
689 | filters=255
690 | activation=linear
691 |
692 |
693 | [yolo]
694 | mask = 3,4,5
695 | anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
696 | classes=80
697 | num=9
698 | jitter=.3
699 | ignore_thresh = .7
700 | truth_thresh = 1
701 | random=1
702 |
703 |
704 |
705 | [route]
706 | layers = -4
707 |
708 | [convolutional]
709 | batch_normalize=1
710 | filters=128
711 | size=1
712 | stride=1
713 | pad=1
714 | activation=leaky
715 |
716 | [upsample]
717 | stride=2
718 |
719 | [route]
720 | layers = -1, 36
721 |
722 |
723 |
724 | [convolutional]
725 | batch_normalize=1
726 | filters=128
727 | size=1
728 | stride=1
729 | pad=1
730 | activation=leaky
731 |
732 | [convolutional]
733 | batch_normalize=1
734 | size=3
735 | stride=1
736 | pad=1
737 | filters=256
738 | activation=leaky
739 |
740 | [convolutional]
741 | batch_normalize=1
742 | filters=128
743 | size=1
744 | stride=1
745 | pad=1
746 | activation=leaky
747 |
748 | [convolutional]
749 | batch_normalize=1
750 | size=3
751 | stride=1
752 | pad=1
753 | filters=256
754 | activation=leaky
755 |
756 | [convolutional]
757 | batch_normalize=1
758 | filters=128
759 | size=1
760 | stride=1
761 | pad=1
762 | activation=leaky
763 |
764 | [convolutional]
765 | batch_normalize=1
766 | size=3
767 | stride=1
768 | pad=1
769 | filters=256
770 | activation=leaky
771 |
772 | [convolutional]
773 | size=1
774 | stride=1
775 | pad=1
776 | filters=255
777 | activation=linear
778 |
779 |
780 | [yolo]
781 | mask = 0,1,2
782 | anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
783 | classes=80
784 | num=9
785 | jitter=.3
786 | ignore_thresh = .7
787 | truth_thresh = 1
788 | random=1
789 |
790 |
--------------------------------------------------------------------------------
/producer/producer.py:
--------------------------------------------------------------------------------
1 | import sys
2 | import time
3 | import cv2
4 | from kafka import KafkaProducer
5 |
6 | topic = "kafka-video-topic"
7 |
8 |
9 | def publish_video(video_file):
10 | """
11 | Publish given video file to a specified Kafka topic.
12 | Kafka Server is expected to be running on the localhost. Not partitioned.
13 |
14 | :param video_file: path to video file
15 | """
16 | # Start up producer
17 | producer = KafkaProducer(bootstrap_servers='192.168.99.100:9092')
18 |
19 | # Open file
20 | video = cv2.VideoCapture(video_file)
21 |
22 | print('publishing video...')
23 |
24 | while(video.isOpened()):
25 | success, frame = video.read()
26 |
27 | # Ensure file was read successfully
28 | if not success:
29 | print("bad read!")
30 | break
31 |
32 | # Convert image to png
33 | ret, buffer = cv2.imencode('.jpg', frame)
34 |
35 | print("ret, buffer", ret, buffer)
36 | # Convert to bytes and send to kafka
37 | producer.send(topic, buffer.tobytes())
38 |
39 | time.sleep(0.2)
40 | video.release()
41 | print('publish complete')
42 |
43 |
44 | def publish_camera():
45 | """
46 | Publish camera video stream to specified Kafka topic.
47 | Kafka Server is expected to be running on the localhost. Not partitioned.
48 | """
49 |
50 | # Start up producer
51 | producer = KafkaProducer(bootstrap_servers='192.168.99.100:9092')
52 |
53 | camera = cv2.VideoCapture(0)
54 | try:
55 | while(True):
56 | success, frame = camera.read()
57 |
58 | ret, buffer = cv2.imencode('.jpg', frame)
59 | producer.send(topic, buffer.tobytes())
60 |
61 | # Choppier stream, reduced load on processor
62 | time.sleep(0.2)
63 |
64 | except:
65 | print("\nExiting.")
66 | sys.exit(1)
67 |
68 |
69 | camera.release()
70 |
71 |
72 | if __name__ == '__main__':
73 | """
74 | Producer will publish to Kafka Server a video file given as a system arg.
75 | Otherwise it will default by streaming webcam feed.
76 | """
77 | # sys.argv = ["hi", r"D:\00STUDY\00ANALYTICS\01DataScience_ML\02Study\12OpenCV\Kafka_VDO\KafkaVideoStreaming\videos\Countdown1.mp4"]
78 | sys.argv = ["hi", r'C:\Users\esdin\Music\Paris Evening Walk and Bike Ride.mp4']
79 | if(len(sys.argv) > 1):
80 | video_path = sys.argv[1]
81 | publish_video(video_path)
82 | else:
83 | print("publishing feed!")
84 | publish_camera()
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | absl-py==0.12.0
2 | asgiref==3.2.3
3 | astor==0.8.1
4 | astunparse==1.6.3
5 | attrs==19.3.0
6 | backcall==0.1.0
7 | cachetools==4.2.1
8 | certifi==2019.11.28
9 | chardet==4.0.0
10 | click==8.0.0
11 | colorama==0.4.4
12 | cycler==0.10.0
13 | decorator==4.4.1
14 | defusedxml==0.6.0
15 | Django==3.0.3
16 | easydict==1.9
17 | entrypoints==0.3
18 | enum34==1.1.6
19 | Flask==2.0.0
20 | gast==0.3.3
21 | google-auth==1.28.0
22 | google-auth-oauthlib==0.4.3
23 | google-pasta==0.2.0
24 | grpcio==1.36.1
25 | h5py==2.10.0
26 | idna==2.10
27 | image==1.5.28
28 | imageio==2.9.0
29 | implicits==1.0.2
30 | importlib-metadata==1.5.0
31 | ipykernel==5.1.4
32 | ipython==7.12.0
33 | ipython-genutils==0.2.0
34 | ipywidgets==7.5.1
35 | itsdangerous==2.0.0
36 | jedi==0.16.0
37 | Jinja2==3.0.0
38 | joblib==0.14.1
39 | jsonschema==3.2.0
40 | jupyter==1.0.0
41 | jupyter-console==6.1.0
42 | jupyter-core==4.6.3
43 | kafka-python==2.0.2
44 | Keras==2.1.5
45 | Keras-Applications==1.0.8
46 | Keras-Preprocessing==1.1.0
47 | kiwisolver==1.2.0
48 | Markdown==3.2.1
49 | MarkupSafe==2.0.0
50 | matplotlib==3.4.1
51 | mistune==0.8.4
52 | nbconvert==5.6.1
53 | nbformat==5.0.4
54 | networkx==2.5.1
55 | notebook==6.0.3
56 | numpy==1.17.4
57 | oauthlib==3.1.0
58 | opencv-python==4.5.1.48
59 | opt-einsum==3.3.0
60 | pandocfilters==1.4.2
61 | parso==0.6.1
62 | pexpect==4.8.0
63 | pickleshare==0.7.5
64 | Pillow==6.2.1
65 | pip-date==1.0.3
66 | portpicker==1.2.0
67 | prometheus-client==0.7.1
68 | prompt-toolkit==3.0.3
69 | protobuf==3.11.3
70 | ptyprocess==0.6.0
71 | py4j==0.10.9
72 | pyasn1==0.4.8
73 | pyasn1-modules==0.2.8
74 | pycocotools==2.0.2
75 | pydot==1.4.2
76 | Pygments==2.5.2
77 | pyparsing==2.4.7
78 | pyrsistent==0.15.7
79 | pyspark==3.0.1
80 | pytesseract==0.3.7
81 | python-dateutil==2.8.1
82 | pytz==2019.3
83 | PyWavelets==1.1.1
84 | pywin32==300
85 | pywinpty==0.5.7
86 | PyYAML==5.3
87 | pyzmq==18.1.1
88 | qtconsole==4.6.0
89 | requests==2.25.1
90 | requests-oauthlib==1.3.0
91 | rsa==4.7.2
92 | scikit-image==0.16.2
93 | scikit-learn==0.22.1
94 | scipy==1.4.1
95 | Send2Trash==1.5.0
96 | six==1.14.0
97 | sklearn==0.0
98 | sqlparse==0.3.0
99 | tensorboard==2.2.2
100 | tensorboard-plugin-wit==1.8.0
101 | tensorflow-gpu-estimator==2.2.0
102 | termcolor==1.1.0
103 | terminado==0.8.3
104 | testpath==0.4.4
105 | tf-utils==1.0.4
106 | traitlets==4.3.3
107 | urllib3==1.26.4
108 | wcwidth==0.1.8
109 | Werkzeug==2.0.0
110 | widgetsnbextension==3.5.1
111 | wincertstore==0.2
112 | wrapt==1.12.1
113 | zipp==3.0.0
114 |
--------------------------------------------------------------------------------