def rgb2gray(rgbFram):
#grayFram = rgbFram
#sensor.set_pixformat(sensor.GRAYSCALE)
img_gray = sensor.snapshot()
for x in range(0, rgbFram.width()):
for y in range(0, rgbFram.height()):
rgb = rgbFram.get_pixel(x, y)
gray = image.rgb_to_grayscale(rgb)
img_gray.set_pixel(x, y, gray)
#grayFram[x,y] = gray
a = img_gray.get_pixel(10, 10)
print(a)
return img_gray
img.draw_rectangle(blob.rect())
for blob in img.find_blobs([yellow_thresholds], area_threshold=150, merge=False):
if(max(blob.w(), blob.h())/min(blob.w()+blob.w(), blob.h())<1.5 and blob.cx()+(blob.w()/2)<ImageX and blob.cx()-(blob.w()/2)>0 and blob.cy()-(blob.h()/2)>0 and blob.cy()+(blob.h()/2)<ImageY and math.sqrt(((blob.cx()-(ImageX/2))*(blob.cx()-(ImageX/2)))+((blob.cy()-(ImageY/2))*(blob.cy()-(ImageY/2))))<(ImageY/3) and img.get_statistics(roi=blob.rect()).stdev()<30):
yellow_blobfound=True
img.draw_rectangle(blob.rect())
for blob in img.find_blobs([green_thresholds], area_threshold=150, merge=False):
if(max(blob.w(), blob.h())/min(blob.w()+blob.w(), blob.h())<1.5 and blob.cx()+(blob.w()/2)<ImageX and blob.cx()-(blob.w()/2)>0 and blob.cy()-(blob.h()/2)>0 and blob.cy()+(blob.h()/2)<ImageY and math.sqrt(((blob.cx()-(ImageX/2))*(blob.cx()-(ImageX/2)))+((blob.cy()-(ImageY/2))*(blob.cy()-(ImageY/2))))<(ImageY/3) and img.get_statistics(roi=blob.rect()).stdev()<30):
green_blobfound=True
img.draw_rectangle(blob.rect())
img.cartoon(size=1, seed_threshold=1)
img.binary([letter_thresholds], zero=True)
letter_blobfound=False
for blob in img.find_blobs([letter_thresholds], area_threshold=150, merge=False):
if(max(blob.w(), blob.h())/min(blob.w()+blob.w(), blob.h())<1.5 and blob.cx()+(blob.w()/2)<ImageX and blob.cx()-(blob.w()/2)>0 and blob.cy()-(blob.h()/2)>0 and blob.cy()+(blob.h()/2)<ImageY and math.sqrt(((blob.cx()-(ImageX/2))*(blob.cx()-(ImageX/2)))+((blob.cy()-(ImageY/2))*(blob.cy()-(ImageY/2))))<(ImageY/3) and img.get_statistics(roi=blob.rect()).stdev()>30):
letter_blobfound=True
SimilarityH = 255-((abs(image.rgb_to_grayscale(img.get_pixel(blob.cx(), blob.y()))-blobHtop)+abs(image.rgb_to_grayscale(img.get_pixel(blob.x(), blob.cy()))-blobHleft)+abs(image.rgb_to_grayscale(img.get_pixel(blob.x(), blob.y()))-blobHtopleft)+abs(image.rgb_to_grayscale(img.get_pixel(blob.cx(), blob.cy()))-blobHcenter))/4)
SimilarityS = 255-((abs(image.rgb_to_grayscale(img.get_pixel(blob.cx(), blob.y()))-blobStop)+abs(image.rgb_to_grayscale(img.get_pixel(blob.x(), blob.cy()))-blobSleft)+abs(image.rgb_to_grayscale(img.get_pixel(blob.x(), blob.y()))-blobStopleft)+abs(image.rgb_to_grayscale(img.get_pixel(blob.cx(), blob.cy()))-blobScenter))/4)
SimilarityU = 255-((abs(image.rgb_to_grayscale(img.get_pixel(blob.cx(), blob.y()))-blobUtop)+abs(image.rgb_to_grayscale(img.get_pixel(blob.x(), blob.cy()))-blobUleft)+abs(image.rgb_to_grayscale(img.get_pixel(blob.x(), blob.y()))-blobUtopleft)+abs(image.rgb_to_grayscale(img.get_pixel(blob.cx(), blob.cy()))-blobUcenter))/4)
img.draw_rectangle(blob.rect())
if(red_blobfound==True):
print('Red')
uart.write('R')
elif(yellow_blobfound==True):
print('Yellow')
uart.write('Y')
elif(green_blobfound==True):
print('Green')
uart.write('G')
elif(letter_blobfound==True):
if(SimilarityS>=SimilarityH and SimilarityS>=SimilarityU):
print('S')
def unittest(data_path, temp_path):
import image
gs = image.rgb_to_grayscale((120, 200, 120))
return (gs == 182)
REGRESSION_GRAY = False
REGRESSION_SEGMENTED = True
# Setting upp thresholds function
# find_line_segments instead of get_line_regression?
while True:
clock.tick()
fps = clock.fps()
img = sensor.snapshot().histeq()
if REGRESSION_COLOR:
line = img.get_regression((COLOR_THRESHOLD), pixels_threshold = PIXELS_THRESHOLD, area_threshold = AREA_THRESHOLD, robust = True, roi = LANE_ROI)
elif REGRESSION_GRAY:
line = image.rgb_to_grayscale(img).get_regression((GRAYSCALE_THRESHOLDS), pixels_threshold = PIXELS_THRESHOLD, area_threshold = AREA_THRESHOLD, robust = True, roi = LANE_ROI)
elif REGRESSION_SEGMENTED:
Lines = []
for l in img.find_line_segments(merge_distance=0 max_theta_difference=15):
img.draw_line(l.line(), color=(255, 0, 0), thickness=2)
parameters = np.polyfit((l.line(x1),l.line(x2)), (l.line(y1),l.line(y2)), 1)
slope = parameters[0]
intercept = parameters[1]
Lines.append((slope, intercept))
LinesAverage = np.average(Lines, axis=0)
# slope, intercept = l.line()
# # y1 = IMG_HEIGHT
# # y2 = int(IMG_HEIGHT*(3.25/5))
# x1 = int((IMG_HEIGHT-intercept)/slope)
# x2 = int((0-intercept)/slope)
def unittest(data_path, temp_path):
import image
gs = image.rgb_to_grayscale((120, 200, 120))
return (gs == 182)
#time.sleep(100)
#roi = (10,10,10,10)
#thres = (49,174)
while (True):
#clock.tick()
#img_gray = sensor.snapshot()
#a = img.get_pixel(10,10)
#i = rgb2gray(img)
#time.sleep(100)
#img.draw_rectangle(roi)
#img = image.Image(path,copy_to_fb = True)
#a = img.get_pixel(10,10)
#b = image.rgb_to_grayscale(a)
#bloc = img.find_blobs(thres)
#roi = blob.rect()
#img.draw_rectangle(roi)
#a = img.width()
for i in range(0, img.width()):
for j in range(0, img.height()):
a = img.get_pixel(i, j)
g = image.rgb_to_grayscale(a)
img_gray.set_pixel(i, j, g)
iii = image.copy(img_gray)
b = img_gray.get_pixel(10, 10)
print(b)