How to convert video to gif using python opencv

 

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import cv2 from sklearn.metrics import mean_squared_error from math import sqrt import numpy as np import traceback as tb import images_to_gif as ig from PIL import Image cap = cv2.VideoCapture('..\\test\\videoplayback.mp4') # Check if camera opened successfully if (cap.isOpened()== False): print("Error opening video file") farmes_list = list() while(cap.isOpened()): ret, frame = cap.read() ret, frame = cap.read() if ret == True: # Display the resulting frame cv2.imshow('Frame', frame) farmes_list.append(frame) else: break # Press Q on keyboard to exit if cv2.waitKey(25) & 0xFF == ord('q'): break print(f'length of the frame list is= {len(farmes_list)}') i = 0 new_frame = list() for img in farmes_list: try: frame = img # Open image in bwDir - The searched image searchedImageBw = np.array(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)) # Open image to be compared inx = i if inx != len(farmes_list): cmpImage = np.array(cv2.cvtColor(farmes_list[inx+1], cv2.COLOR_BGR2GRAY)) rms = sqrt(mean_squared_error(searchedImageBw, cmpImage)) print(f'rms= {rms}') if rms>3: #farmes_list.remove(frame) new_frame.append(frame) except Exception as e: print(e) tb.print_exc() pass i = i+1 print(f'length of the frame list is= {len(new_frame)}') pil_frame = [ Image.fromarray(img) for img in new_frame] bytesio_object = ig.frame_gif(pil_frame) ig.save(bytesio_object, path = "videotogif.gif") cap.release() cv2.destroyAllWindows()

Fire off function without waiting for answer (Python)

Here is sample code for thread based method invocation additionally desired threading.stack_size can be added to boost the performance. Also its important to invoke Garbage collector if the number of threaded invocation is greater in number.

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import threading import requests import gc #The stack size set by threading.stack_size is the amount of memory to allocate for the call stack in threads. threading.stack_size(524288) def alpha_gun(url, json, headers): #r=requests.post(url, data=json, headers=headers) r=requests.get(url) print(r.text) def trigger(url, json, headers): threading.Thread(target=alpha_gun, args=(url, json, headers)).start() url = "https://raw.githubusercontent.com/jyotiprakash-work/Live_Video_steaming/master/README.md" payload="{}" headers = { 'Content-Type': 'application/json' } for i in range(10): print(i) #for condition if i==5: trigger(url=url, json =payload, headers=headers) gc.collect() print('invoked')

Face Recognition With python and face-net model

 Here i have added face recognition code in a flask app. The Face app contains registration, training model and recognition where each having a separate end point.

To access the endpoints please follow cUrls-

For Respiration- 

curl -X POST \

  http://127.0.0.1:5000/upload \

  -H 'cache-control: no-cache' \

  -H 'content-type: multipart/form-data; boundary=----WebKitFormBoundary7MA4YWxkTrZu0gW' \

  -H 'postman-token: 2bf86477-f928-9ff2-d677-9b42a802e381' \

  -F file=@WIN_20220123_00_56_49_Pro.jpg \

  -F id=jp

For training-

curl -X POST \

  http://127.0.0.1:5000/train \

  -H 'cache-control: no-cache' \

  -H 'postman-token: 374826ca-15b5-7508-5052-f3ec43b1ca07'

For Recognition-

curl -X POST \

  http://127.0.0.1:5000/recognize \

  -H 'cache-control: no-cache' \

  -H 'content-type: multipart/form-data; boundary=----WebKitFormBoundary7MA4YWxkTrZu0gW' \

  -H 'postman-token: 4183a550-5278-a7fe-c618-f100d164c7f8' \

  -F file=@WIN_20220123_00_57_28_Pro.jpg