Add old stuff from DST
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8
.dockerignore
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8
.dockerignore
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**/*.jpg
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**/*.png
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tests
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MotorControl
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Messaging
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Web
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**/*.mdj
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**/*.pdf
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@@ -2,6 +2,7 @@
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from queue import Queue
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import json
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import argparse
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import numpy as np
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import cv2
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@@ -97,7 +98,9 @@ class Instance:
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self.kaleid = False
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parser = argparse.ArgumentParser(description="An instance of CAIDE")
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if __name__ == "__main__":
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instance = Instance()
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instance = Instance(video_file="/Users/piv/Documents/Projects/Experiments/Camera1/video.mp4")
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instance.start()
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73
GestureRecognition/kaleidoscope.py
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73
GestureRecognition/kaleidoscope.py
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import numpy as np
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import cv2
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def make_triangle(start_img):
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h, w, d = start_img.shape
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#crop square
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inset = int((max(w,h) - min(w,h)) / 2)
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# sqrimg = start_img.crop(inset, inset, h-inset, w-inset)
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insetW = inset if w > h else 0
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insetH = inset if h > w else 0
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sqrimg = start_img[insetH:h-insetH, insetW:w-insetW]
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#solve equilateral triangle
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w, h, d = sqrimg.shape
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print((w,h))
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mask = np.zeros((w,h,d))
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t_height = w/2 * np.tan(60)
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pts = np.array([[0,w],[h/2,t_height],[h,w]], np.int32)
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pts = pts.reshape((-1,1,2))
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mask = cv2.fillPoly(mask, [pts], (255,0,0))
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# With mask, get the triangle from the original image.
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sqrimg[:,:,0] = np.where(mask[:,:,0] == 255, sqrimg[:,:,0], 0)
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sqrimg[:,:,1] = np.where(mask[:,:,0] == 255, sqrimg[:,:,1], 0)
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sqrimg[:,:,2] = np.where(mask[:,:,0] == 255, sqrimg[:,:,2], 0)
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return sqrimg
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def rotate(im, rotation):
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M = cv2.getRotationMatrix2D((im.shape[1]/2,im.shape[0]/2),rotation,1)
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im[:,:,0] = cv2.warpAffine(im[:,:,0],M,(im.shape[1],im.shape[0]))
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im[:,:,1] = cv2.warpAffine(im[:,:,1],M,(im.shape[1],im.shape[0]))
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im[:,:,2] = cv2.warpAffine(im[:,:,2],M,(im.shape[1],im.shape[0]))
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return im
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def make_kaleidoscope(img):
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triangle = make_triangle(img)
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def make_trapezoid(triangle, save=False):
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w, h = triangle.size
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can_w, can_h = w*3, h
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output = np.array((can_w, can_h, 3))
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output = Image.new('RGBA', (can_w, can_h), color=255)
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def mirror_paste(last_img, coords):
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mirror = rotate(cv2.flip(last_img, 1), 60)
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output.paste(mirror, (coords), mirror)
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return mirror, coords
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#paste in bottom left corner
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output.paste(triangle,(0, can_h-h), triangle)
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last_img, coords = mirror_paste(triangle, (int(w/4.4), -int(h/2.125)))
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last_img, coords = mirror_paste(rotateIm(last_img, 120), (int(can_w/7.3), -228))
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output = output.crop((0,15, w*2-22, h))
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if save:
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path = 'output/trapezoid_{}'.format(filename.split('/')[1])
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output.save(path)
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return output, path
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return output
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if __name__ == "__main__":
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img = cv2.imread("/Users/piv/Documents/Projects/car/GestureRecognition/IMG_0818.png")
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triangle = make_triangle(img)
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triangle = cv2.resize(triangle, None, fx=0.3, fy=0.3, interpolation = cv2.INTER_AREA)
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triangle = rotate(triangle, 180)
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cv2.imshow("", triangle)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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23
GestureRecognition/opencvtensorflowex.py
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23
GestureRecognition/opencvtensorflowex.py
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import cv2 as cv
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cvNet = cv.dnn.readNetFromTensorflow('frozen_inference_graph.pb', 'graph.pbtxt')
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img = cv.imread('IMG_0825.jpg')
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img = cv.resize(img, None, fx=0.1, fy=0.1, interpolation = cv.INTER_AREA)
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rows = img.shape[0]
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cols = img.shape[1]
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print(str(rows) + " " + str(cols))
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cvNet.setInput(cv.dnn.blobFromImage(img, size=(300, 300), swapRB=True, crop=False))
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cvOut = cvNet.forward()
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for detection in cvOut[0,0,:,:]:
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score = float(detection[2])
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if score > 0.6:
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left = detection[3] * cols
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top = detection[4] * rows
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right = detection[5] * cols
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bottom = detection[6] * rows
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cv.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (23, 230, 210), thickness=2)
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cv.imshow('img', img)
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cv.waitKey()
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5
requirements.txt
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5
requirements.txt
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numpy
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opencv-python
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six
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paho-mqtt
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u-msgpack-python
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@@ -22,9 +22,9 @@ class TestSimpleHandRecogniser(unittest.TestCase):
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img_5 = cv2.resize(img_5, None, fx=0.1, fy=0.1, interpolation = cv2.INTER_AREA)
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self.recogniser_5 = SimpleHandRecogniser(img_5)
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img_s = cv2.imread("/Users/piv/Documents/Projects/car/GestureRecognition/Screen Shot hand.png")
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img_s = cv2.resize(img_s, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_AREA)
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self.recogniser_s = SimpleHandRecogniser(img_s)
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# img_s = cv2.imread("/Users/piv/Documents/Projects/car/GestureRecognition/Screen Shot hand.png")
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# img_s = cv2.resize(img_s, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_AREA)
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# self.recogniser_s = SimpleHandRecogniser(img_s)
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if __name__ == '__main__':
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unittest.main()
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