def get_final_image(self):
"""
Simple merge operation - just splat the annotation layer on top of
original image. That's what is displayed, so it should be fine.
:return: pillow Image
"""
img = ImageQt.fromqimage(self.bg_img)
annotations = ImageQt.fromqimage(self.canvas_img)
img.paste(annotations, mask=annotations)
# OR: convert to RGBA and .alpha_composite
# but this is good enough!
return img
def magma(self):
image = ImageQt.fromqimage(self.will_change_img)
img = self.img_to_cv(image)
kernel = np.array([[1, 1, 0], [1, 0, -1], [0, -1, -1]])
new_image = cv2.filter2D(img, -1, kernel)
new_image = cv2.applyColorMap(new_image, cv2.COLORMAP_HOT)
self.update_img(new_image)
def predict(self):
image = self.__paintBoard.getImage()
pil_img = ImageQt.fromqimage(image)
pil_img = pil_img.resize((28, 28), Image.ANTIALIAS)
# pil_img.save('./images/test66.png')
# pil_img.show()
img_array = np.array(pil_img.convert('L')).reshape(784)
# print(img_array.shape) # (784,)
# display image
# print(img_array)
plt.imshow(img_array.reshape(28, 28), cmap="binary")
plt.show()
# fig = plt.figure(figsize=(6, 6))
# fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
# # 绘制数字：每张图像8*8像素点
# for i in range(64):
# ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
# ax.imshow(self.xtest[i].reshape(28, 28), cmap=plt.cm.binary, interpolation='nearest')
# # 用目标值标记图像
# ax.text(0, 7, str(self.ytest[i]))
# plt.show()
self.__result = self.model.predict(img_array)
print("result: %d" % self.__result)
self.__lb_Result.setText("%d" % self.__result)
def contrast(self):
image = ImageQt.fromqimage(self.will_change_img)
if image.mode != "RGB":
image.convert("RGB")
width, height = image.size
pixels = image.load()
avg = 0
for x in range(width):
for y in range(height):
r, g, b = pixels[x, y]
avg += r * 0.299 + g * 0.587 + b * 0.114
avg /= image.size[0] * image.size[1]
palette = []
for i in range(256):
temp = int(avg + 2 * (i - avg))
if temp < 0:
temp = 0
elif temp > 255:
temp = 255
palette.append(temp)
for x in range(width):
for y in range(height):
r, g, b = pixels[x, y]
image.putpixel((x, y), (palette[r], palette[g], palette[b]))
image = self.img_to_cv(image)
self.update_img(image)
def MatchFirst(ImgList):
ImgCutpictures = []
#ImgGetPictures = []
ImgCutpictures = walkFile("..\cutPictures")
#ImgGetPictures = walkFile(".\getPictures")
#print(ImgCutpictures)
#print(ImgGetPictures)
print("ImgList=" + str(ImgList))
MatchFiles = {}
for i in ImgList:
#img1 = Image.open(i)
image1 = pm.make_regalur_image(ImageQt.fromqimage(i.toImage()))
for j in ImgCutpictures:
img2 = Image.open(j)
#print(img2)
image2 = pm.make_regalur_image(img2)
if pm.calc_similar(image1, image2) == 1.0:
print(str(i) + "找到匹配了" + j)
#找到图片最大匹配的文件夹
j = j.replace("..\cutPictures\\", "")
print(j)
if (not MatchFiles.get(j[0:j.index("\\")])):
MatchFiles[j[0:j.index("\\")]] = 1
else:
MatchFiles[j[0:j.index("\\")]] += 1
print(pm.calc_similar(image1, image2))
break
print(MatchFiles)
#print(maxMatch(MatchFiles))
return maxMatch(MatchFiles)
def dither(self):
image = ImageQt.fromqimage(self.will_change_img)
width, height = image.size
img = self.create_image(width, height)
pixels = img.load()
for i in range(0, width, 2):
for j in range(0, height, 2):
# Get Pixels
p1 = self.get_pixel(image, i, j)
p2 = self.get_pixel(image, i, j + 1)
p3 = self.get_pixel(image, i + 1, j)
p4 = self.get_pixel(image, i + 1, j + 1)
red = (p1[0] + p2[0] + p3[0] + p4[0]) / 4
green = (p1[1] + p2[1] + p3[1] + p4[1]) / 4
blue = (p1[2] + p2[2] + p3[2] + p4[2]) / 4
r = [0, 0, 0, 0]
g = [0, 0, 0, 0]
b = [0, 0, 0, 0]
for x in range(0, 4):
r[x] = self.get_saturation(red, x)
g[x] = self.get_saturation(green, x)
b[x] = self.get_saturation(blue, x)
pixels[i, j] = (r[0], g[0], b[0])
pixels[i, j + 1] = (r[1], g[1], b[1])
pixels[i + 1, j] = (r[2], g[2], b[2])
pixels[i + 1, j + 1] = (r[3], g[3], b[3])
image = self.img_to_cv(img)
self.update_img(image)
return img
def burgundy(self):
image = ImageQt.fromqimage(self.will_change_img)
img_rgb = self.img_to_cv(image)
aspect_ratio = img_rgb.shape[1] / img_rgb.shape[0]
window_width = 500 / aspect_ratio
image = cv2.resize(img_rgb, (500, int(window_width)))
img_color = image
newImage = img_color.copy()
i, j, k = img_color.shape
for x in range(i):
for y in range(j):
R = img_color[x, y, 2] * 0.125 + img_color[x, y, 1] * 0.102 + img_color[x, y, 0] * 0.135
G = img_color[x, y, 2] * 0.256 + img_color[x, y, 1] * 0.106 + img_color[x, y, 0] * 0.96
B = img_color[x, y, 2] * 0.565 + img_color[x, y, 1] * 0.300 + img_color[x, y, 0] * 0.206
if R > 255:
newImage[x, y, 2] = 102
else:
newImage[x, y, 2] = B
if G > 255:
newImage[x, y, 1] = 102
else:
newImage[x, y, 1] = R
if B > 255:
newImage[x, y, 0] = 102
else:
newImage[x, y, 0] = R
self.update_img(newImage)
def test_sanity(self):
for mode in ('RGB', 'RGBA', 'L', 'P', '1'):
src = hopper(mode)
data = ImageQt.toqimage(src)
self.assertIsInstance(data, QImage)
self.assertFalse(data.isNull())
# reload directly from the qimage
rt = ImageQt.fromqimage(data)
if mode in ('L', 'P', '1'):
self.assert_image_equal(rt, src.convert('RGB'))
else:
self.assert_image_equal(rt, src)
if mode == '1':
# BW appears to not save correctly on QT4 and QT5
# kicks out errors on console:
# libpng warning: Invalid color type/bit depth combination
# in IHDR
# libpng error: Invalid IHDR data
continue
# Test saving the file
tempfile = self.tempfile('temp_{}.png'.format(mode))
data.save(tempfile)
# Check that it actually worked.
reloaded = Image.open(tempfile)
# Gray images appear to come back in palette mode.
# They're roughly equivalent
if QT_VERSION == 4 and mode == 'L':
src = src.convert('P')
self.assert_image_equal(reloaded, src)
def cartoon(self):
samp = 2
filternum = 50
image = ImageQt.fromqimage(self.will_change_img)
img = self.img_to_cv(image)
for _ in range(samp):
img = cv2.pyrDown(img)
for _ in range(filternum):
img = cv2.bilateralFilter(img, 9, 9, 7)
for _ in range(samp):
img = cv2.pyrUp(img)
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img_blur = cv2.medianBlur(img_gray, 3)
img_edge = cv2.adaptiveThreshold(img_blur, 255,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 9, 2)
(x, y, z) = img.shape
img_edge = cv2.resize(img_edge, (y, x))
img_edge = cv2.cvtColor(img_edge, cv2.COLOR_GRAY2RGB)
self.temp = cv2.bitwise_and(img, img_edge)
self.update_img(self.temp)
def test_sanity(self):
for mode in ('RGB', 'RGBA', 'L', 'P', '1'):
src = hopper(mode)
data = ImageQt.toqimage(src)
self.assertIsInstance(data, QImage)
self.assertFalse(data.isNull())
# reload directly from the qimage
rt = ImageQt.fromqimage(data)
if mode in ('L', 'P', '1'):
self.assert_image_equal(rt, src.convert('RGB'))
else:
self.assert_image_equal(rt, src)
if mode == '1':
# BW appears to not save correctly on QT4 and QT5
# kicks out errors on console:
# libpng warning: Invalid color type/bit depth combination
# in IHDR
# libpng error: Invalid IHDR data
continue
# Test saving the file
tempfile = self.tempfile('temp_{}.png'.format(mode))
data.save(tempfile)
# Check that it actually worked.
reloaded = Image.open(tempfile)
# Gray images appear to come back in palette mode.
# They're roughly equivalent
if QT_VERSION == 4 and mode == 'L':
src = src.convert('P')
self.assert_image_equal(reloaded, src)
def run(self):
hwnd = 0
while True:
try:
if hwnd == 0:
hwnd = get_hwnd()
if hwnd == -1:
self.update_rec.emit('句柄无效，请打开AirPlayer并连接')
time.sleep(1)
hwnd = get_hwnd()
continue
screen = QApplication.primaryScreen()
try:
raw_image = screen.grabWindow(hwnd).toImage()
PIL_image = ImageQt.fromqimage(raw_image)
CV_image = cv2.cvtColor(np.asarray(PIL_image),
cv2.COLOR_RGB2BGR)
except Exception as e:
hwnd = 0
continue
labeled_image = self.image_process(CV_image)
labeled_image = Image.fromarray(
cv2.cvtColor(labeled_image, cv2.COLOR_BGR2RGB))
labeled_image = ImageQt.ImageQt(labeled_image)
self.update_img.emit(labeled_image.copy())
time.sleep(1 / 15)
except Exception as e:
print(e)
def on_btn_outport_clicked(self):
if len(self.filelist)==0:
self.fWindow.statusBar().showMessage('请先导入图片!')
return
self.fWindow.statusBar().showMessage('开始保存!')
config = configparser.ConfigParser()
config.read("init.ini", encoding="utf8")
outportPath=config.get("IMG",'imgsavepath')
if not os.path.exists(outportPath):
os.makedirs(outportPath)
for i in self.filelist:
# img=QImage(i)
cvimg = self.cv_imread(i.filepath,1)
img = self.CVMat2QImage(cvimg)
img = self.drawImage(img, i.text,i.textsize,i.textoffset,
i.colorr,i.colorg,i.colorb)
#img.save(outportPath+'/'+i.filename.split('.')[0]+'.png')
pimg=ImageQt.fromqimage(img)
pimg.save(outportPath+'/'+i.filename)
self.fWindow.statusBar().showMessage('保存结束!')
def pbtPredict_Callback(self):
__img, img_array =[],[] # 将图像统一从qimage->pil image -> np.array [1, 1, 28, 28]
# 获取qimage格式图像
if self.mode == MODE_MNIST:
__img = self.lbDataArea.pixmap() # label内若无图像返回None
if __img == None: # 无图像则用纯黑代替
# __img = QImage(224, 224, QImage.Format_Grayscale8)
__img = ImageQt.ImageQt(Image.fromarray(np.uint8(np.zeros([224,224]))))
else: __img = __img.toImage()
elif self.mode == MODE_WRITE:
__img = self.paintBoard.getContentAsQImage()
# 转换成pil image类型处理
pil_img = ImageQt.fromqimage(__img)
pil_img = pil_img.resize((28, 28), Image.ANTIALIAS)
# pil_img.save('test.png')
img_array = np.array(pil_img.convert('L')).reshape(1,1,28, 28) / 255.0
# img_array = np.where(img_array>0.5, 1, 0)
# reshape成网络输入类型
__result = network.predict(img_array) # shape:[1, 10]
# print (__result)
# 将预测结果使用softmax输出
__result = softmax(__result)
self.result[0] = np.argmax(__result) # 预测的数字
self.result[1] = __result[0, self.result[0]] # 置信度
self.lbResult.setText("%d" % (self.result[0]))
self.lbCofidence.setText("%.8f" % (self.result[1]))
def test_sanity(self):
for mode in ("RGB", "RGBA", "L", "P", "1"):
src = hopper(mode)
data = ImageQt.toqimage(src)
assert isinstance(data, QImage)
assert not data.isNull()
# reload directly from the qimage
rt = ImageQt.fromqimage(data)
if mode in ("L", "P", "1"):
assert_image_equal(rt, src.convert("RGB"))
else:
assert_image_equal(rt, src)
if mode == "1":
# BW appears to not save correctly on QT4 and QT5
# kicks out errors on console:
# libpng warning: Invalid color type/bit depth combination
# in IHDR
# libpng error: Invalid IHDR data
continue
# Test saving the file
tempfile = self.tempfile("temp_{}.png".format(mode))
data.save(tempfile)
# Check that it actually worked.
with Image.open(tempfile) as reloaded:
assert_image_equal(reloaded, src)
def pbtPredict_Callback(self):
__img, img_array = [], [
] # 将图像统一从qimage->pil image -> np.array [1, 1, 28, 28]
# 获取qimage格式图像
if self.mode == MODE_MNIST:
__img = self.lbDataArea.pixmap() # label内若无图像返回None
if __img == None: # 无图像则用纯黑代替
# __img = QImage(224, 224, QImage.Format_Grayscale8)
__img = ImageQt.ImageQt(
Image.fromarray(np.uint8(np.zeros([224, 224]))))
else:
__img = __img.toImage()
elif self.mode == MODE_WRITE:
__img = self.paintBoard.getContentAsQImage()
# 转换成pil image类型处理
pil_img = ImageQt.fromqimage(__img)
pil_img = pil_img.resize((28, 28), Image.ANTIALIAS)
img_array = np.array(pil_img.convert('L')).reshape(1, 1, 28, 28)
img = normalize(img_array)
img = paddle.Tensor(img)
__result = network(img)
argmax__result = paddle.argmax(__result).numpy()
self.result[0] = argmax__result[0] # 置信度
m = F.sigmoid(__result).numpy()
self.result[1] = m[0][self.result[0]]
self.lbResult.setText("%d" % (self.result[0]))
self.lbCofidence.setText("%.8f" % (self.result[1]))
def daVinci(self):
img = ImageQt.fromqimage(self.will_change_img)
pixels = list(img.getdata())
width, height = img.size
image_array = []
for j in range(height):
row = []
for i in range(width):
if i < width - 1 and (abs(pixels[j * width + i][0] - pixels[j * width + i + 1][0])
+ abs(pixels[j * width + i][1] - pixels[j * width + i + 1][1])
+ abs(
pixels[j * width + i][2] - pixels[j * width + i + 1][2])) / 3 > 35:
row.append((255, 140, i // ((width + 255) // 255)))
elif j < height - 1 and (abs(pixels[j * width + i][0] - pixels[(j + 1) * width + i][0])
+ abs(pixels[j * width + i][1] - pixels[(j + 1) * width + i][1])
+ abs(
pixels[j * width + i][2] - pixels[(j + 1) * width + i][0])) / 3 > 35:
row.append((255, 140, i // ((width + 255) // 255)))
else:
row.append((102,102,255))
image_array.append(row)
array = np.array(image_array, dtype=np.uint8)
new_image = Image.fromarray(array)
image = self.img_to_cv(new_image)
self.update_img(image)
def loadImage(self):
if self.area_state: # 画板
image = self.paintboard.getImage()
else: # 图片
image = self.image_area.pixmap().toImage()
image = ImageQt.fromqimage(image)
return image
def pbtPredict_Callback(self):
__img, img_array =[],[] # 将图像统一从qimage->pil image -> np.array [1, 1, 28, 28]
# 获取qimage格式图像
if self.mode == MODE_MNIST: #随机抽取
__img = self.lbDataArea.pixmap() # label内若无图像返回None
if __img == None: # 无图像则用纯黑代替
# __img = QImage(224, 224, QImage.Format_Grayscale8)
__img = ImageQt.ImageQt(Image.fromarray(np.uint8(np.zeros([224,224]))))
else: __img = __img.toImage()
pil_img = ImageQt.fromqimage(__img)
pil_img = pil_img.resize((28, 28), Image.ANTIALIAS)
img_array = np.array(pil_img.convert('L')).reshape(1,1,28, 28) / 255.0
elif self.mode == MODE_WRITE:
__img = self.paintBoard.getContentAsQImage()
pil_img = ImageQt.fromqimage(__img)
pil_img = pil_img.resize((28, 28), Image.ANTIALIAS)
pil_img.save('test.png')
img_array = np.array(pil_img.convert('L')).reshape(1,1,28, 28) / 255.0
elif self.mode == MODE_PICTURE:
pic = image_prepare(self.imgName) # 得到28*28的数组
img_array = pic.reshape(1,1,28, 28) / 255.0 # 转换成神经网络要求的输入格式
'''
关于这部分有一点需要说明：
开始通过image_prepare得到预处理的图片之后，最开始是变成一维数组，然后一直想着变成二维数组并进行灰度反转。
为什么会这样想呢，因为我开始在变成reshape(1,1,28, 28)格式时采用的是网上的另一种方法，以至于格式基本是没有变化的。
其实直接变成一维数组然后进行上面的转换也是一样可以的，这个部分的失误浪费了很长时间！以后debug时需要多注意。
'''
# reshape成网络输入类型
__result = network.predict(img_array) # shape:[1, 10]
# print (__result)
# 将预测结果使用softmax输出，得到输出结果
__result = softmax(__result)
self.result[0] = np.argmax(__result) # 预测的数字
self.result[1] = __result[0, self.result[0]] # 置信度
# 结果显示在界面上
self.lbResult.setText("%d" % (self.result[0]))
self.lbCofidence.setText("%.8f" % (self.result[1]))
def crop(self):
try:
self.preview_img_data = image_processing.Crop(QtGui.QPixmap.fromImage(self.image))
self.image = self.preview_img_data.toImage()
self.imgData = ImageQt.fromqimage(self.image)
self.Set_Filter_Label()
self.Show()
except:
pass
def save_clicked_callback(self):
image = self.writeBoard.getContentAsQImage()
# 转换成pil image类型处理
pil_img = ImageQt.fromqimage(image)
#pil_img = pil_img.resize((28, 28), Image.ANTIALIAS)
print(pil_img.size)
#pil_img.save('test.png')
#pil_img = pil_img.convert('1')
GetFeature(pil_img)
def _writeFrame(self, surface: QImage):
w = self.stream.width
h = self.stream.height
surface = surface.scaled(w, h) if self.scale else surface
frame = av.VideoFrame.from_image(ImageQt.fromqimage(surface))
# Draw the mouse pointer. Render mouse clicks?
p = QPainter(surface)
p.setBrush(QColor.fromRgb(255, 255, 0, 180))
(x, y) = self.mouse
p.drawEllipse(x, y, 5, 5)
p.end()
# Output frame.
frame = av.VideoFrame.from_image(ImageQt.fromqimage(surface))
for packet in self.stream.encode(frame):
if self.progress:
self.progress()
self.mp4.mux(packet)
def dream(self):
image = ImageQt.fromqimage(self.will_change_img)
img = self.img_to_cv(image)
element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (6, 6))
b, g, r = cv2.split(img)
rbr_img = cv2.merge((r, b, g))
morphology = cv2.morphologyEx(rbr_img, cv2.MORPH_OPEN, element)
canvas = cv2.normalize(morphology, None, 20, 255, cv2.NORM_MINMAX)
new_image = cv2.stylization(canvas, sigma_s=60, sigma_r=0.6)
self.update_img(new_image)
def roundtrip(self, expected):
# PIL -> Qt
intermediate = expected.toqimage()
# Qt -> PIL
result = ImageQt.fromqimage(intermediate)
if intermediate.hasAlphaChannel():
self.assert_image_equal(result, expected.convert('RGBA'))
else:
self.assert_image_equal(result, expected.convert('RGB'))
def roundtrip(self, expected):
# PIL -> Qt
intermediate = expected.toqimage()
# Qt -> PIL
result = ImageQt.fromqimage(intermediate)
if intermediate.hasAlphaChannel():
self.assert_image_equal(result, expected.convert("RGBA"))
else:
self.assert_image_equal(result, expected.convert("RGB"))
def cotton_candy(self):
image = ImageQt.fromqimage(self.will_change_img)
img = self.img_to_cv(image)
b, g, r = cv2.split(img)
b = b * 1.35
g = g * 0.5
r = r * 2.25
rbr_img = cv2.merge((r, g, b))
image = cv2.convertScaleAbs(rbr_img, alpha=1.2, beta=-20)
image = cv2.bilateralFilter(image, 9, 75, 75)
self.update_img(image)
def brightness_img(self):
bright_val = self.brightness_slider.value()
self.brightness_text.setText(str(bright_val))
image = ImageQt.fromqimage(self.will_change_img)
if image.mode != 'RGB':
image = image.convert('RGB')
enhancer = ImageEnhance.Brightness(image)
image = enhancer.enhance(bright_val / 10)
image = self.img_to_cv(image)
self.update_img(image)
def handDraw(self):
image = ImageQt.fromqimage(self.will_change_img)
imga = self.img_to_cv(image)
gray = cv2.cvtColor(imga, cv2.COLOR_BGR2GRAY)
invert = cv2.bitwise_not(gray)
smooth = cv2.GaussianBlur(invert, (21,21),sigmaX=0, sigmaY=0)
def dodge(x, y):
return cv2.divide(x, 255 - y, scale=256)
img = dodge(gray, smooth)
self.update_img(img)
def change_contrast(self):
contrast_value = self.contrast_slider.value()
self.contrast_text.setText(str(contrast_value))
image = ImageQt.fromqimage(self.will_change_img)
if image.mode != 'RGB':
image = image.convert('RGB')
enhancer = ImageEnhance.Contrast(image)
image = enhancer.enhance(contrast_value / 10)
image = self.img_to_cv(image)
self.update_img(image)
def predict(self):
image = self.__paintBoard.getImage()
pil_img = ImageQt.fromqimage(image)
pil_img = pil_img.resize((28, 28), Image.ANTIALIAS)
img_array = np.array(pil_img.convert('L')).reshape(784)
plt.imshow(img_array.reshape(28, 28), cmap="binary")
self.__result = self.model.predict(img_array)
print("result: %d" % self.__result)
self.__lb_Result.setText("%d" % self.__result)
plt.show()
def test_image():
modes = ["1", "RGB", "RGBA", "L", "P"]
qt_format = ImageQt.QImage.Format if ImageQt.qt_version == "6" else ImageQt.QImage
if hasattr(qt_format, "Format_Grayscale16"): # Qt 5.13+
modes.append("I;16")
for mode in modes:
im = hopper(mode)
roundtripped_im = ImageQt.fromqimage(ImageQt.ImageQt(im))
if mode not in ("RGB", "RGBA"):
im = im.convert("RGB")
assert_image_similar(roundtripped_im, im, 1)
def focus(self):
image = ImageQt.fromqimage(self.will_change_img)
image = self.img_to_cv(image)
rows, cols = image.shape[:2]
X_kernel = cv2.getGaussianKernel(cols, 200)
Y_kernel = cv2.getGaussianKernel(rows, 200)
kernel = Y_kernel * X_kernel.T
mask = 255 * kernel / np.linalg.norm(kernel)
output = np.copy(image)
for i in range(3):
output[:, :, i] = output[:, :, i] * mask
self.update_img(output)
def beedril_filter(self):
image = ImageQt.fromqimage(self.will_change_img)
if image.mode != "RGBA":
image.convert("RGBA")
width, height = image.size
pixels = image.load()
for i in range(0, width, 2):
for j in range(0, height, 2):
p1 = self.get_pixel(image, i, j)
p2 = self.get_pixel(image, i, j + 1)
p3 = self.get_pixel(image, i + 1, j)
p4 = self.get_pixel(image, i + 1, j + 1)
# to grayscale
gray1 = (p1[0] * 0.299) + (p1[1] * 0.587) + (p1[2] * 0.114)
gray2 = (p2[0] * 0.299) + (p2[1] * 0.587) + (p2[2] * 0.114)
gray3 = (p3[0] * 0.299) + (p3[1] * 0.587) + (p3[2] * 0.114)
gray4 = (p4[0] * 0.299) + (p4[1] * 0.587) + (p4[2] * 0.114)
# Saturation
sat = (gray1 + gray2 + gray3 + gray4) / 4
if sat > 223:
pixels[i, j] = (255, 208, 2)
pixels[i, j + 1] = (255, 208, 2)
pixels[i + 1, j] = (255, 208, 2)
pixels[i + 1, j + 1] = (255, 208, 2)
elif sat > 159:
pixels[i, j] = (255, 255, 255)
pixels[i, j + 1] = (0, 0, 0)
pixels[i + 1, j] = (255, 255, 255)
pixels[i + 1, j + 1] = (255, 255, 255)
elif sat > 95:
pixels[i, j] = (182, 66, 30) # (248, 142, 33) orange
pixels[i, j + 1] = (182, 66, 30)
pixels[i + 1, j] = (182, 66, 30)
pixels[i + 1, j + 1] = (182, 66, 30)
elif sat > 32:
pixels[i, j] = (0, 0, 0)
pixels[i, j + 1] = (255, 255, 255)
pixels[i + 1, j] = (0, 0, 0)
pixels[i + 1, j + 1] = (0, 0, 0)
else:
pixels[i, j] = (0, 0, 0)
pixels[i, j + 1] = (0, 0, 0)
pixels[i + 1, j] = (0, 0, 0)
pixels[i + 1, j + 1] = (0, 0, 0)
image = self.img_to_cv(image)
self.update_img(image)
def roundtrip(self, expected):
result = ImageQt.fromqimage(expected.toqimage())
# Qt saves all images as rgb
self.assert_image_equal(result, expected.convert('RGB'))
