ph_chann = 3
fb0 = fb(shrink=args.shrink)
if True:
fbB = fb(shrink=1)
assert os.path.exists(args.background)
background = cv2.imread(args.background)
os.system('clear')
if os.system('which clear') == 0: os.system('clear')
fbB.imshow('back', background)
os.system("banner HST")
if os.system('which setterm') == 0:
os.system('setterm -blank 0;echo setterm -blank 0')
fbB = fb(shrink=1)
fbB.close()
print("virtual_size:", fb0.vw, fb0.vh)
devmem_image = devmem(0xe018c000, ph_height * ph_width * ph_chann)
devmem_start = devmem(0xe0c00004, 4)
devmem_stat = devmem(0xe0c00008, 0x4)
devmem_pred = devmem(0xe0000000, 0xc15c)
n_classes = 20
grid_h = 9
grid_w = 11
box_coord = 4
n_b_boxes = 5
n_info_per_grid = box_coord + 1 + n_classes
classes = [
"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
"chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
"pottedplant", "sheep", "sofa", "train", "tvmonitor"
def main():
# Definition of the paths
# weights_path = './yolov2-tiny-voc_352_288_final.weights'
input_img_path = './dog.jpg'
input_img_path = './horses.jpg'
output_image_path = './result.jpg'
# If you do not have the checkpoint yet keep it like this! When you will run test.py for the first time it will be created automatically
# ckpt_folder_path = './ckpt/'
# Definition of the parameters
ph_height = 288 # placeholder height
ph_width = 352 # placeholder width
score_threshold = 0.3
iou_threshold = 0.3
# Definition of the session
# sess = tf.InteractiveSession()
# tf.global_variables_initializer().run()
# Check for an existing checkpoint and load the weights (if it exists) or do it from binary file
# print('Looking for a checkpoint...')
# saver = tf.train.Saver()
# _ = weights_loader.load(sess,weights_path,ckpt_folder_path,saver)
verbose = True
# Preprocess the input image
print('Preprocessing...')
preprocessed_nchwRGB = preprocessing(input_img_path, ph_height, ph_width)
cnt = 0
with open('preprocessed_nchwRGB.txt', 'w') as f:
for i in preprocessed_nchwRGB.reshape(-1):
cnt += 1
f.write("%2x\n" % i)
print('dump preprocessed_nchwRGB.txt:', cnt)
d = preprocessed_nchwRGB.reshape(-1).astype(np.uint8).tostring()
devmem(0xe018c000, len(d), verbose=verbose).write(d).close()
print("start FPGA accelerator")
s = np.asarray([0x1], dtype=np.uint32).tostring()
devmem(0xe0c00004, len(s)).write(s).close()
#sleep(1)
for i in range(10000):
mem = devmem(0xe0c00008, 0x4)
status = mem.read(np.uint32)
mem.close()
if status[0] == 0x2000: break
print("fpga status:0x%08x" % (status[0]))
print("preprocessing to NCHW-RGB", preprocessed_nchwRGB.shape)
# Compute the predictions on the input image
print('Computing predictions...')
# predictions = inference(sess,preprocessed_image)
if True:
v = True
v = False
mem = devmem(0xe0000000, 0xc15c, v)
predictions = mem.read(np.float32)
mem.close()
assert predictions[0] == predictions[
0], "invalid mem values:{}".format(predictions[:8])
print("inference from FPGA", predictions.shape)
else:
filename = 'featuremap_8.txt'
with open(filename) as f:
txt_v = f.read().strip().split()
predictions = np.asarray(
[np.float(re.sub(',', '', i)) for i in txt_v])
print("inference dummy", predictions.shape, filename)
# _predictions________________________________________________________
# | 4 entries |1 entry | 20 entries |
# | x..x | y..y | w..w | h..h | c .. c | p0 - p19 .. p0 - p19| x 5(==num)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# entiry size == grid_w x grid_h
dets = []
for i in range(5):
entries = []
off = grid_h * grid_w * n_info_per_grid * i
for j in range(n_info_per_grid):
off2 = off + j * grid_h * grid_w * 1
entry = predictions[off2:off2 + grid_h * grid_w * 1].reshape(
grid_h, grid_w, 1)
entries.append(entry)
dets.append(np.concatenate(entries, axis=2))
predictions = np.stack(dets, axis=2)
# _predictions_________________________________________
# | 25 float32 words |
# grid_h, grid_w, num, | x | y | w | h | c | p0..p19 |
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# predictions.shape=( 9,11,5,25)
print("predictions.shape", predictions.shape)
# Postprocess the predictions and save the output image
print('Postprocessing...')
output_image = postprocessing(predictions, input_img_path, score_threshold,
iou_threshold, ph_height, ph_width)
cv2.imwrite(output_image_path, output_image)
def main():
# Definition of the paths
input_img_path = './dog.jpg'
input_img_path = './horses.jpg'
input_img_path = './person.jpg'
output_image_path = './result.jpg'
# Definition of the parameters
ph_height = 288 # placeholder height
ph_width = 352 # placeholder width
score_threshold = 0.3
iou_threshold = 0.3
verbose = True
# Preprocess the input image
print('Preprocessing...')
input_image = cv2.imread(input_img_path) # HWC BGR
preprocessed_nchwRGB = preprocessing(input_image, ph_height, ph_width)
cnt = 0
d = preprocessed_nchwRGB.reshape(-1).astype(np.uint8).tostring()
devmem(0xe018c000, len(d), verbose=verbose).write(d).close()
print("start FPGA accelerator")
s = np.asarray([0x1], dtype=np.uint32).tostring()
devmem(0xe0c00004, len(s)).write(s).close()
#sleep(1)
for i in range(10000):
mem = devmem(0xe0c00008, 0x4)
status = mem.read(np.uint32)
mem.close()
if status[0] == 0x2000: break
print("fpga status:0x%08x" % (status[0]))
print("preprocessing to NCHW-RGB", preprocessed_nchwRGB.shape)
# Compute the predictions on the input image
print('Computing predictions...')
mem = devmem(0xe0000000, 0xc15c)
predictions = mem.read(np.float32)
mem.close()
assert predictions[0] == predictions[0], "invalid mem values:{}".format(
predictions[:8])
print("inference from FPGA", predictions.shape)
# _predictions________________________________________________________
# | 4 entries |1 entry | 20 entries |
# | x..x | y..y | w..w | h..h | c .. c | p0 - p19 .. p0 - p19| x 5(==num)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# entiry size == grid_w x grid_h
print("predictions.shape", predictions.shape)
# Postprocess the predictions and save the output image
print('Postprocessing...')
im_h, im_w = input_image.shape[:2]
res = dn.postprocessing(predictions, im_w, im_h, 0.5, 0.5)
# res: object_name, confidence, ( centerX, centerY, boxW, boxH )
for r in res:
name, conf, bbox = r[:3]
obj_col = colors[classes.index(r[0])]
rect = box2rect(bbox)
print("{}".format(bbox))
print("{}".format(rect))
cv2.rectangle(
input_image,
(rect[0], rect[1]),
(rect[2], rect[3]),
#(255,255,255)
obj_col)
cv2.putText(
input_image,
name,
(int(bbox[0]), int(bbox[1])),
cv2.FONT_HERSHEY_SIMPLEX,
1,
#(255,255,255)
obj_col,
2)
cv2.imwrite('result.jpg', input_image)
print("virtual_size:",fb0.vw,fb0.vh)
print("camra :",args.cam_w, args.cam_h, "shrink:1/%d"%args.shrink, "thread:", args.thread, "DMA Mode:", args.dma)
fb0 = fb(shrink=args.shrink)
backgrounder(args.background)
if os.system('which setterm') == 0: os.system('setterm -blank 0;echo setterm -blank 0')
image_area_addr = 0xe018c000
if args.dma and os.path.exists(args.phys_addr):
with open(args.phys_addr) as f:
cmd = "image_area_addr = %s"%(f.read().strip())
exec(cmd)
else:
args.dma=False
print("image_area_addr:%x"%image_area_addr)
devmem_image = devmem(image_area_addr, ph_height*ph_width*ph_chann)
devmem_start = devmem(0xe0c00004,4)
devmem_stat = devmem(0xe0c00008,0x4)
devmem_pred = devmem(0xe0000000,0xc15c)
devmem_dmac = devmem(0xe0c00018,4)
devmem_pfmc = devmem(0xe0c00020,4)
if args.dma:
print("DMA-Mode:On")
c = np.asarray([0x00000000],dtype=np.uint32).tostring()
b = np.asarray([image_area_addr],dtype=np.uint32).tostring()
devmem_dmab = devmem(0xe0c00010,4)
devmem_dmab.write(b)
devmem_dmab.close()
else:
print("DMA-Mode:Off")
c = np.asarray([0x80000000],dtype=np.uint32).tostring()
import os, sys, re
import numpy as np
from devmemX import devmem
import cv2
from time import sleep, time
from pdb import *
devmem_image = devmem(0xe018c000, 352 * 288 * 3)
devmem_start = devmem(0xe0c00004, 4)
devmem_stat = devmem(0xe0c00008, 0x4)
devmem_pred = devmem(0xe0000000, 0xc15c)
n_classes = 20
grid_h = 9
grid_w = 11
box_coord = 4
n_b_boxes = 5
n_info_per_grid = box_coord + 1 + n_classes
classes = [
"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
"chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
"pottedplant", "sheep", "sofa", "train", "tvmonitor"
]
colors = [(254.0, 254.0, 254), (239.8, 211.6, 127), (225.7, 169.3, 0),
(211.6, 127.0, 254), (197.5, 84.6, 127), (183.4, 42.3, 0),
(169.3, 0.0, 254), (155.2, -42.3, 127), (141.1, -84.6, 0),
(127.0, 254.0, 254), (112.8, 211.6, 127), (98.7, 169.3, 0),
(84.6, 127.0, 254), (70.5, 84.6, 127), (56.4, 42.3, 0),
(42.3, 0.0, 254), (28.2, -42.3, 127), (14.1, -84.6, 0),
(0.0, 254.0, 254), (-14.1, 211.6, 127)]
