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This commit contain nine code examples of using deepstack AI sever.
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‎README.md‎

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‎python/.ipynb_checkpoints/Readme-checkpoint.ipynb‎

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‎python/.vscode/settings.json‎

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{
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"python.linting.pylintEnabled": true
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}

‎python/face_detection_api_from_camera_to_display.py‎

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import numpy as np
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import cv2
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import requests
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cap = cv2.VideoCapture(0)
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progress_tracker = 0
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prediction_json = {}
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skip_frame = 20
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while(cap.isOpened()):
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valid, frame = cap.read()
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if valid == True:
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progress_tracker += 1
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if(progress_tracker % skip_frame == 0):
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retval, new_frame = cv2.imencode('.jpg', frame)
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response = requests.post("http://localhost:80/v1/vision/face",
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files={"image":new_frame}).json()
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prediction_json = response['predictions']
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print(prediction_json)
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num_prediction_json = len(prediction_json)
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for i in range(num_prediction_json):
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red, green, blue = 200, 100, 200
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frame = cv2.rectangle(frame, (prediction_json[i]['x_min'], prediction_json[i]['y_min']),
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(prediction_json[i]['x_max'], prediction_json[i]['y_max']), (red, green, blue), 1)
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cv2.imshow('Image Viewer', frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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else:
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break
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cap.release()
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cv2.destroyAllWindows()

‎python/face_detection_api_from_camera_to_file.py‎

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import numpy as np
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import cv2
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import requests
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cap = cv2.VideoCapture(0)
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out = cv2.VideoWriter("face_detection_from_camera_to_file.avi", cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
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,24,((int(cap.get(3)), int(cap.get(4)))))
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progress_tracker = 0
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prediction_json = {}
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skip_frame = 20
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print('<============================= Press contrl + c to break ===============================>')
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while(cap.isOpened()):
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valid, frame = cap.read()
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if valid == True:
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progress_tracker += 1
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if(progress_tracker % skip_frame == 0):
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retval, new_frame = cv2.imencode('.jpg', frame)
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response = requests.post("http://localhost:80/v1/vision/face",
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files={"image":new_frame}).json()
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prediction_json = response['predictions']
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print(prediction_json)
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num_prediction_json = len(prediction_json)
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for i in range(num_prediction_json):
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red, green, blue = 100, 50, 200
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frame = cv2.rectangle(frame, (prediction_json[i]['x_min'], prediction_json[i]['y_min']),
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(prediction_json[i]['x_max'], prediction_json[i]['y_max']), (red, green, blue), 1)
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out.write(frame)
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else:
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break
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cap.release()
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out.release()

‎python/face_detection_api_from_video_to_display.py‎

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import numpy as np
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import cv2
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import requests
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cap = cv2.VideoCapture('furious.mp4')
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progress_tracker = 0
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prediction_json = {}
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skip_frame = 20
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while(cap.isOpened()):
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valid, frame = cap.read()
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if valid == True:
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progress_tracker += 1
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if(progress_tracker % skip_frame == 0):
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retval, new_frame = cv2.imencode('.jpg', frame)
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response = requests.post("http://localhost:80/v1/vision/face",
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files={"image":new_frame}).json()
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prediction_json = response['predictions']
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print(prediction_json)
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num_prediction_json = len(prediction_json)
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for i in range(num_prediction_json):
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red, green, blue = 100, 50, 200
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frame = cv2.rectangle(frame, (prediction_json[i]['x_min'], prediction_json[i]['y_min']),
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(prediction_json[i]['x_max'], prediction_json[i]['y_max']), (red, green, blue), 1)
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cv2.imshow('Image Viewer', frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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else:
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break
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cap.release()
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cv2.destroyAllWindows()

‎python/face_detection_api_from_video_to_file.py‎

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import numpy as np
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import cv2
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import requests
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cap = cv2.VideoCapture('furious.mp4')
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out = cv2.VideoWriter("face_detection_from_video_to_file.avi", cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),
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24,((int(cap.get(3)), int(cap.get(4)))))
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progress_tracker = 0
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prediction_json = {}
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skip_frame = 20
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while(cap.isOpened()):
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valid, frame = cap.read()
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if valid == True:
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progress_tracker += 1
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if(progress_tracker % skip_frame == 0):
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retval, new_frame = cv2.imencode('.jpg', frame)
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response = requests.post("http://localhost:80/v1/vision/face",
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files={"image":new_frame}).json()
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prediction_json = response['predictions']
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print(prediction_json)
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num_prediction_json = len(prediction_json)
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for i in range(num_prediction_json):
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red, green, blue = 100, 50, 200
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frame = cv2.rectangle(frame, (prediction_json[i]['x_min'], prediction_json[i]['y_min']),
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(prediction_json[i]['x_max'], prediction_json[i]['y_max']), (red, green, blue), 1)
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out.write(frame)
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else:
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break
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print('<==============Video file as been full written with face bounding boxes============>')
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cap.release()
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out.release()

‎python/furious.mp4‎

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‎python/object_detection_api_from_camera_to_display.py‎

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import numpy as np
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import cv2
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import requests
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cap = cv2.VideoCapture(0)
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progress_tracker = 0
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prediction_json = {}
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skip_frame = 20
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while(cap.isOpened()):
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valid, frame = cap.read()
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if valid == True:
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progress_tracker += 1
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if(progress_tracker % skip_frame == 0):
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retval, new_frame = cv2.imencode('.jpg', frame)
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response = requests.post("http://localhost:80/v1/vision/detection",
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files={"image":new_frame}).json()
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prediction_json = response['predictions']
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print(prediction_json)
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num_prediction_json = len(prediction_json)
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for i in range(num_prediction_json):
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color_space_values = np.random.randint(50, 255, size=(3,))
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red, green, blue = color_space_values
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red, green, blue = int(red), int(green), int(blue)
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frame = cv2.rectangle(frame, (prediction_json[i]['x_min'], prediction_json[i]['y_min']),
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(prediction_json[i]['x_max'], prediction_json[i]['y_max']), (red, green, blue), 1)
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cv2.imshow('Image Viewer', frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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else:
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break
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cap.release()
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cv2.destroyAllWindows()

‎python/object_detection_api_from_camera_to_file.py‎

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import numpy as np
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import cv2
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import requests
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cap = cv2.VideoCapture(0)
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out = cv2.VideoWriter("object_detection_from_camera_to_file.avi", cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
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,24,((int(cap.get(3)), int(cap.get(4)))))
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progress_tracker = 0
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prediction_json = {}
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skip_frame = 20
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print('<============================= Press contrl + c to break ===============================>')
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while(cap.isOpened()):
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valid, frame = cap.read()
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if valid == True:
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progress_tracker += 1
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if(progress_tracker % skip_frame == 0):
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retval, new_frame = cv2.imencode('.jpg', frame)
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response = requests.post("http://localhost:80/v1/vision/detection",
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files={"image":new_frame}).json()
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prediction_json = response['predictions']
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print(prediction_json)
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num_prediction_json = len(prediction_json)
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for i in range(num_prediction_json):
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color_space_values = np.random.randint(50, 255, size=(3,))
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red, green, blue = color_space_values
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red, green, blue = int(red), int(green), int(blue)
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frame = cv2.rectangle(frame, (prediction_json[i]['x_min'], prediction_json[i]['y_min']),
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(prediction_json[i]['x_max'], prediction_json[i]['y_max']), (red, green, blue), 1)
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out.write(frame)
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else:
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break
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cap.release()
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out.release()

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