mirror of
https://github.com/esimov/pigo.git
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98 lines
3.0 KiB
Python
98 lines
3.0 KiB
Python
from ctypes import *
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import subprocess
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import numpy as np
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import os
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import cv2
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import time
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os.system('go build -o puploc.so -buildmode=c-shared puploc.go')
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pigo = cdll.LoadLibrary('./puploc.so')
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os.system('rm puploc.so')
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MAX_NDETS = 2024
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ARRAY_DIM = 6
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# define class GoPixelSlice to map to:
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# C type struct { void *data; GoInt len; GoInt cap; }
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class GoPixelSlice(Structure):
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_fields_ = [
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("pixels", POINTER(c_ubyte)), ("len", c_longlong), ("cap", c_longlong),
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]
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# Obtain the camera pixels and transfer them to Go trough Ctypes.
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def process_frame(pixs):
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dets = np.zeros(ARRAY_DIM * MAX_NDETS, dtype=np.float32)
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pixels = cast((c_ubyte * len(pixs))(*pixs), POINTER(c_ubyte))
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# call FindFaces
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faces = GoPixelSlice(pixels, len(pixs), len(pixs))
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pigo.FindFaces.argtypes = [GoPixelSlice]
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pigo.FindFaces.restype = c_void_p
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# Call the exported FindFaces function from Go.
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ndets = pigo.FindFaces(faces)
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data_pointer = cast(ndets, POINTER((c_longlong * ARRAY_DIM) * MAX_NDETS))
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if data_pointer :
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buffarr = ((c_longlong * ARRAY_DIM) * MAX_NDETS).from_address(addressof(data_pointer.contents))
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res = np.ndarray(buffer=buffarr, dtype=c_longlong, shape=(MAX_NDETS, 5,))
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# The first value of the buffer aray represents the buffer length.
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dets_len = res[0][0]
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res = np.delete(res, 0, 0) # delete the first element from the array
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# We have to consider the pupil pair added into the list.
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# That's why we are multiplying the detection length with 3.
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dets = list(res.reshape(-1, 5))[0:dets_len*3]
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return dets
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# initialize the camera
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cap = cv2.VideoCapture(0)
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
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# Changing the camera resolution introduce a short delay in the camera initialization.
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# For this reason we should delay the object detection process with a few milliseconds.
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time.sleep(0.4)
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showPupil = True
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showEyes = False
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while(True):
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ret, frame = cap.read()
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pixs = np.ascontiguousarray(frame[:, :, 1].reshape((frame.shape[0], frame.shape[1])))
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pixs = pixs.flatten()
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# Verify if camera is intialized by checking if pixel array is not empty.
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if np.any(pixs):
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dets = process_frame(pixs) # pixs needs to be numpy.uint8 array
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if dets is not None:
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# We know that the detected faces are taking place in the first positions of the multidimensional array.
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for det in dets:
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if det[3] > 50:
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if det[4] == 1: # 1 == face; 0 == pupil
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cv2.circle(frame, (int(det[1]), int(det[0])), int(det[2]/2.0), (0, 0, 255), 2)
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else:
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if showPupil:
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cv2.circle(frame, (int(det[1]), int(det[0])), 4, (0, 0, 255), -1, 8, 0)
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if showEyes:
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cv2.rectangle(frame,
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(int(det[1])-int(det[2]), int(det[0])-int(det[2])),
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(int(det[1])+int(det[2]), int(det[0])+int(det[2])),
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(0, 255, 0), 2
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)
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cv2.imshow('', frame)
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key = cv2.waitKey(1)
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if key & 0xFF == ord('q'):
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break
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elif key & 0xFF == ord('w'):
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showPupil = not showPupil
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elif key & 0xFF == ord('e'):
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showEyes = not showEyes
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cap.release()
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cv2.destroyAllWindows() |