charlie
4 years ago
parent
c8d355a762
commit
a0efc139fd
@ -0,0 +1,570 @@
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import math
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import random
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import uuid
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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from django.http import HttpResponse
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PREFIX = 'media/'
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DEFAULT_FORMAT = '.jpg'
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SUPPORT_FILE_FORMAT = ['png', 'jpg', 'bmp', 'jpeg', 'jpe', 'dib', 'pbm', 'pgm', 'ppm', 'tiff', 'tif']
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def get_image_name():
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return uuid.uuid1().__str__()
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def get_image(request):
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file = request.FILES.get('image')
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suffix = file.name[file.name.rfind('.') + 1:].lower()
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if suffix not in SUPPORT_FILE_FORMAT:
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return ''
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filename = get_image_name() + '.' + suffix
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img = open(PREFIX + filename, 'wb+')
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for chunk in file.chunks():
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img.write(chunk)
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img.close()
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return filename
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def process_bg():
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img = np.zeros((800, 800, 3), np.uint8)
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return save_image(img)
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def process_line(para):
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name, start, end, color, thickness = para['img'], para['start'], para['end'], para['color'], para['thickness']
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img = cv2.imread(PREFIX + name)
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cv2.line(img, tuple(start), tuple(end), tuple(color), thickness)
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cv2.imwrite(PREFIX + name, img)
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return name
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def process_rectangle(para):
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name, start, end, color, thickness = para['img'], para['start'], para['end'], para['color'], para['thickness']
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img = cv2.imread(PREFIX + name)
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cv2.rectangle(img, tuple(start), tuple(end), tuple(color), thickness)
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cv2.imwrite(PREFIX + name, img)
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return name
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def process_circle(para):
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name, center, radius, color, thickness = para['img'], para['center'], para['radius'], para['color'], para[
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'thickness']
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img = cv2.imread(PREFIX + name)
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cv2.circle(img, tuple(center), radius, tuple(color), thickness)
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cv2.imwrite(PREFIX + name, img)
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return name
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def process_ellipse(para):
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name, center, axes, angle, startangle, endangle, color, thickness = para['img'], para['center'], para['axes'], para[
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'angle'], para['startangle'], para['endangle'], para['color'], para['thickness']
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img = cv2.imread(PREFIX + name)
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cv2.ellipse(img, tuple(center), tuple(axes), angle, startangle, endangle, tuple(color), thickness)
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cv2.imwrite(PREFIX + name, img)
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return name
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def process_polylines(para):
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name, point, isclosed, color = para['img'], para['point'], para['isclosed'], para['color']
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img = cv2.imread(PREFIX + name)
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pts = np.array(point, np.int32)
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pts = pts.reshape((-1, 1, 2))
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cv2.polylines(img, [pts], isclosed, tuple(color))
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cv2.imwrite(PREFIX + name, img)
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return name
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def process_text(para):
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name, text, org, fontsize, color = para['img'], para['text'], para['org'], para['fontsize'], para['color']
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img = cv2.imread(PREFIX + name)
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cv2.putText(img, text, org, fontFace=cv2.FONT_HERSHEY_SCRIPT_COMPLEX, fontScale=fontsize, color=tuple(color))
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cv2.imwrite(PREFIX + name, img)
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return name
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def process_filter_rgb(para, data):
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limit = para.get('limit', 100)
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c_range = para.get('range', [90, 230])
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if np.max(data) > limit:
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data = data * (255 / np.max(data))
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tmp = np.zeros_like(data)
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tmp[((data > c_range[0]) & (data <= c_range[1]))] = 255
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return tmp
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def base2read(img, has_color):
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img[0], img[1] = img[1], img[0]
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img1 = cv2.imread(PREFIX + img[0], has_color)
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img2 = cv2.imread(PREFIX + img[1], has_color)
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rows, cols = img1.shape[:2]
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img2 = cv2.resize(img2, (cols, rows), interpolation=cv2.INTER_CUBIC)
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return img1, img2
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def logic_and(img, has_color, base):
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img1 = None
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img2 = None
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if base == 2:
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img1, img2 = base2read(img, has_color)
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ret = img1 & img2
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return save_image(ret)
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def logic_or(img, has_color, base):
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img1 = None
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img2 = None
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if base == 2:
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img1, img2 = base2read(img, has_color)
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ret = img1 | img2
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return save_image(ret)
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def logic_not(img, has_color, base):
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if base == 2:
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img[0], img[1] = img[1], img[0]
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image = cv2.imread(PREFIX + img[0], has_color)
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ret = ~image
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return save_image(ret)
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def arithmetic_add(img, has_color, base):
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img1 = None
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img2 = None
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if base == 2:
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img1, img2 = base2read(img, has_color)
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ret = cv2.add(img1, img2)
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return save_image(ret)
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def base2read_choice(img, has_color, base):
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img1 = cv2.imread(PREFIX + img[0], has_color)
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img2 = cv2.imread(PREFIX + img[1], has_color)
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if base == 1:
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rows, cols = img1.shape[:2]
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img2 = cv2.resize(img2, (cols, rows), interpolation=cv2.INTER_CUBIC)
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elif base == 2:
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rows, cols = img2.shape[:2]
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img1 = cv2.resize(img1, (cols, rows), interpolation=cv2.INTER_CUBIC)
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return img1, img2
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def arithmetic_sub(img, has_color, base):
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img1, img2 = base2read_choice(img, has_color, base)
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ret = cv2.subtract(img1, img2)
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return save_image(ret)
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def arithmetic_multi(img, has_color, base):
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img1 = None
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img2 = None
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if base == 2:
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img1, img2 = base2read(img, has_color)
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ret = cv2.multiply(img1, img2)
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return save_image(ret)
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def arithmetic_div(img, has_color, base):
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img1, img2 = base2read_choice(img, has_color, base)
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ret = cv2.divide(img1, img2)
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return save_image(ret)
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def rotate_bound(image, angle):
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(h, w) = image.shape[:2]
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(cX, cY) = (w // 2, h // 2)
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M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
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cos = np.abs(M[0, 0])
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sin = np.abs(M[0, 1])
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nW = int((h * sin) + (w * cos))
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nH = int((h * cos) + (w * sin))
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M[0, 2] += (nW / 2) - cX
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M[1, 2] += (nH / 2) - cY
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return cv2.warpAffine(image, M, (nW, nH))
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def hist_cover_gray(img, fileName):
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plt.figure(fileName, figsize=(16, 8))
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hist = cv2.calcHist([img], [0], None, [256], [0, 255])
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plt.plot(hist)
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plt.xlim([0, 255])
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plt.savefig(fileName)
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def hist_cover_rgb(img, fileName):
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color = ["r", "g", "b"]
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b, g, r = cv2.split(img)
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img = cv2.merge([r, g, b])
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for index, c in enumerate(color):
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hist = cv2.calcHist([img], [index], None, [256], [0, 255])
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plt.plot(hist, color=c)
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plt.xlim([0, 255])
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plt.savefig(fileName)
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def grayHist(img, filename):
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plt.figure(filename, figsize=(16, 8))
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h, w = img.shape[:2]
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pixelSequence = img.reshape(h * w, 1)
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numberBins = 256
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histogram_, bins, patch = plt.hist(pixelSequence, numberBins)
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plt.xlabel("gray label")
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plt.ylabel("number of pixels")
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plt.axis([0, 255, 0, np.max(histogram_)])
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plt.savefig(filename)
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def process_LoG(img):
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grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = cv2.copyMakeBorder(grayImage, 2, 2, 2, 2, borderType=cv2.BORDER_REPLICATE)
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img = cv2.GaussianBlur(img, (3, 3), 0, 0)
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m1 = np.array(
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[[0, 0, -1, 0, 0], [0, -1, -2, -1, 0], [-1, -2, 16, -2, -1], [0, -1, -2, -1, 0], [0, 0, -1, 0, 0]],
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dtype=np.int32)
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image1 = np.zeros(img.shape).astype(np.int32)
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h, w, _ = img.shape
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for i in range(2, h - 2):
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for j in range(2, w - 2):
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image1[i, j] = np.sum(m1 * img[i - 2:i + 3, j - 2:j + 3, 1])
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return cv2.convertScaleAbs(image1)
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def process_Canny(img):
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tmp = cv2.GaussianBlur(img, (3, 3), 0)
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gradx = cv2.Sobel(tmp, cv2.CV_16SC1, 1, 0)
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grady = cv2.Sobel(tmp, cv2.CV_16SC1, 0, 1)
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return cv2.Canny(gradx, grady, 50, 150)
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def process_enhance(img):
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h, w = img.shape
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gradient = np.zeros((h, w))
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img = img.astype('float')
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for i in range(h - 1):
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for j in range(w - 1):
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gx = abs(img[i + 1, j] - img[i, j])
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gy = abs(img[i, j + 1] - img[i, j])
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gradient[i, j] = gx + gy
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sharp = img + gradient
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sharp = np.where(sharp > 255, 255, sharp)
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sharp = np.where(sharp < 0, 0, sharp)
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gradient = gradient.astype('uint8')
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sharp = sharp.astype('uint8')
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ret = [{"sharp": save_image(sharp), "gradient": save_image(gradient)}]
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return HttpResponse(ret)
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def process_lcd_with_p(edges, img):
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linesP = cv2.HoughLinesP(edges, 1, np.pi / 180, 80, 200, 15)
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for i_P in linesP:
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for x1, y1, x2, y2 in i_P:
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cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 3)
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return img
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def process_lcd_without_p(edges, img):
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lines = cv2.HoughLines(edges, 1, np.pi / 2, 118)
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for i_line in lines:
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for line in i_line:
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rho = line[0]
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theta = line[1]
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if (theta < (np.pi / 4.)) or (theta > (3. * np.pi / 4.0)): # 垂直直线
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pt1 = (int(rho / np.cos(theta)), 0)
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pt2 = (int((rho - img.shape[0] * np.sin(theta)) / np.cos(theta)), img.shape[0])
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cv2.line(img, pt1, pt2, (0, 0, 255))
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else:
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pt1 = (0, int(rho / np.sin(theta)))
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pt2 = (img.shape[1], int((rho - img.shape[1] * np.cos(theta)) / np.sin(theta)))
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cv2.line(img, pt1, pt2, (0, 0, 255), 1)
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return img
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def ideal_low_filter(img, D0):
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h, w = img.shape[:2]
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filter_img = np.ones((h, w))
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u = np.fix(h / 2)
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v = np.fix(w / 2)
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for i in range(h):
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for j in range(w):
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d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
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filter_img[i, j] = 0 if d > D0 else 1
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return filter_img
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def ideal_high_pass_filter(img, D0):
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h, w = img.shape[:2]
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filter_img = np.zeros((h, w))
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u = np.fix(h / 2)
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v = np.fix(w / 2)
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for i in range(h):
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for j in range(w):
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d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
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filter_img[i, j] = 0 if d < D0 else 1
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return filter_img
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def butterworth_low_pass_filter(img, D0, rank):
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h, w = img.shape[:2]
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filter_img = np.zeros((h, w))
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u = np.fix(h / 2)
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v = np.fix(w / 2)
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for i in range(h):
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for j in range(w):
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d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
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filter_img[i, j] = 1 / (1 + ((d / D0) ** (2 * rank)))
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return filter_img
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def butterworth_high_pass_filter(img, D0, rank):
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h, w = img.shape[:2]
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filter_img = np.zeros((h, w))
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u = np.fix(h / 2)
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v = np.fix(w / 2)
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for i in range(h):
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for j in range(w):
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d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
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filter_img[i, j] = 1 / (1 + ((D0 / d) ** (2 * rank)))
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return filter_img
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def gauss_low_pass_filter(img, d0):
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h, w = img.shape[:2]
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filter_img = np.zeros((h, w))
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u = np.fix(h / 2)
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v = np.fix(w / 2)
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for i in range(h):
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for j in range(w):
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d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
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filter_img[i, j] = np.exp(-0.5 * (d ** 2) / (d0 ** 2))
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return filter_img
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def gauss_high_pass_filter(img, d0):
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h, w = img.shape[:2]
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filter_img = np.zeros((h, w))
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u = np.fix(h / 2)
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v = np.fix(w / 2)
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for i in range(h):
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for j in range(w):
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d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
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filter_img[i, j] = 1 - np.exp(-0.5 * (d ** 2) / (d0 ** 2))
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return filter_img
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def filter_use_smooth(img, my_filter):
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# 首先进行傅里叶变换
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f = np.fft.fft2(img)
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f_center = np.fft.fftshift(f)
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# 应用滤波器进行反变换
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S = np.multiply(f_center, my_filter) # 频率相乘——l(u,v)*H(u,v)
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f_origin = np.fft.ifftshift(S) # 将低频移动到原来的位置
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f_origin = np.fft.ifft2(f_origin) # 使用ifft2进行傅里叶的逆变换
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f_origin = np.abs(f_origin) # 设置区间
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return f_origin
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def filter_use_sharpen(img, my_filter):
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f_origin = filter_use_smooth(img, my_filter)
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f_origin = f_origin / np.max(f_origin.all())
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return f_origin
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def process_roberts(img):
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kernelx = np.array([[-1, 0], [0, 1]], dtype=int)
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kernely = np.array([[0, -1], [1, 0]], dtype=int)
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x = cv2.filter2D(img, cv2.CV_16S, kernelx)
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y = cv2.filter2D(img, cv2.CV_16S, kernely)
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# 转uint8
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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return cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
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def process_sobel(img):
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x = cv2.Sobel(img, cv2.CV_16S, 1, 0) # 对x求一阶导
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y = cv2.Sobel(img, cv2.CV_16S, 0, 1) # 对y求一阶导
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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return cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
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def process_prewitt(img):
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kernelx = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]], dtype=int)
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kernely = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]], dtype=int)
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x = cv2.filter2D(img, cv2.CV_16S, kernelx)
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y = cv2.filter2D(img, cv2.CV_16S, kernely)
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# 转uint8
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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return cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
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def process_laplacian(img):
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dst = cv2.Laplacian(img, cv2.CV_16S, ksize=3)
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return cv2.convertScaleAbs(dst)
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def process_gauss_noise(img):
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img = np.array(img / 255, dtype=float)
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noise_ = np.random.normal(0, 0.1, img.shape)
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img = img + noise_
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img = np.clip(img, 0.0, 1.0)
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return np.uint8(img * 255)
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def process_salt_and_peper_noise(img, para):
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range_salt = para.get('salt', 0.2)
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range_peper = para.get('peper', 0.8)
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output = np.zeros(img.shape, np.uint8)
|
||||
for i in range(img.shape[0]):
|
||||
for j in range(img.shape[1]):
|
||||
rdn = random.random()
|
||||
if rdn < range_salt:
|
||||
output[i][j] = 0
|
||||
elif rdn > range_peper:
|
||||
output[i][j] = 255
|
||||
else:
|
||||
output[i][j] = img[i][j]
|
||||
return output
|
||||
|
||||
|
||||
def process_geometric_mean(img):
|
||||
output = np.zeros(img.shape, np.uint8)
|
||||
for i in range(img.shape[0]):
|
||||
for j in range(img.shape[1]):
|
||||
ji = 1.0
|
||||
for m in range(-1, 2):
|
||||
if 0 <= j + m < img.shape[1]:
|
||||
ji *= img[i][j + m]
|
||||
output[i][j] = math.pow(ji, 1 / 3)
|
||||
return output
|
||||
|
||||
|
||||
def process_arithmetic_mean(img):
|
||||
output = np.zeros(img.shape, np.uint8)
|
||||
for i in range(img.shape[0]):
|
||||
for j in range(img.shape[1]):
|
||||
my_sum = 0
|
||||
for m in range(-1, 2):
|
||||
for n in range(-1, 2):
|
||||
if 0 <= i + m < img.shape[0] and 0 <= j + n < img.shape[1]:
|
||||
my_sum += img[i + m][j + n]
|
||||
output[i][j] = int(my_sum / 9)
|
||||
return output
|
||||
|
||||
|
||||
def process_harmonic_filter(img):
|
||||
output = np.zeros(img.shape, np.uint8)
|
||||
for i in range(img.shape[0]):
|
||||
for j in range(img.shape[1]):
|
||||
my_sum = 0
|
||||
for m in range(-1, 2):
|
||||
for n in range(-1, 2):
|
||||
if 0 <= i + m < img.shape[0] and 0 <= j + n < img.shape[1]:
|
||||
my_sum += 1 / img[i + m][j + n]
|
||||
output[i][j] = int(9 / my_sum)
|
||||
return output
|
||||
|
||||
|
||||
def get_middle(array):
|
||||
length = len(array)
|
||||
for i in range(length):
|
||||
for j in range(i + 1, length):
|
||||
if array[j] > array[i]:
|
||||
temp = array[j]
|
||||
array[j] = array[i]
|
||||
array[i] = temp
|
||||
return array[int(length / 2)]
|
||||
|
||||
|
||||
def get_array_from_struct_ele(img, i, j):
|
||||
array = []
|
||||
for m in range(-1, 2):
|
||||
for n in range(-1, 2):
|
||||
if 0 <= i + m < img.shape[0] and 0 <= j + n < img.shape[1]:
|
||||
array.append(img[i + m][j + n])
|
||||
return array
|
||||
|
||||
|
||||
def process_max_sort(img):
|
||||
output = np.zeros(img.shape, np.uint8)
|
||||
for i in range(img.shape[0]):
|
||||
for j in range(img.shape[1]):
|
||||
array = get_array_from_struct_ele(img, i, j)
|
||||
array.sort(reverse=True)
|
||||
output[i][j] = max(array[0], img[i][j])
|
||||
return output
|
||||
|
||||
|
||||
def process_min_sort(img):
|
||||
output = np.zeros(img.shape, np.uint8)
|
||||
for i in range(img.shape[0]):
|
||||
for j in range(img.shape[1]):
|
||||
array = get_array_from_struct_ele(img, i, j)
|
||||
array.sort()
|
||||
output[i][j] = min(array[0], img[i][j])
|
||||
return output
|
||||
|
||||
|
||||
def process_median_sort(img):
|
||||
output = np.zeros(img.shape, np.uint8)
|
||||
for i in range(img.shape[0]):
|
||||
for j in range(img.shape[1]):
|
||||
array = get_array_from_struct_ele(img, i, j)
|
||||
output[i][j] = get_middle(array)
|
||||
return output
|
||||
|
||||
|
||||
def process_high_choice(img, limit):
|
||||
output = np.zeros(img.shape, np.uint8)
|
||||
for i in range(img.shape[0]):
|
||||
for j in range(img.shape[1]):
|
||||
if img[i][j] > limit:
|
||||
output[i][j] = img[i][j]
|
||||
else:
|
||||
output[i][j] = 0
|
||||
return output
|
||||
|
||||
|
||||
def process_low_choice(img, limit):
|
||||
output = np.zeros(img.shape, np.uint8)
|
||||
for i in range(img.shape[0]):
|
||||
for j in range(img.shape[1]):
|
||||
if img[i][j] < limit:
|
||||
output[i][j] = img[i][j]
|
||||
else:
|
||||
output[i][j] = 0
|
||||
return output
|
||||
|
||||
|
||||
def process_pass_choice(img, my_min, my_max):
|
||||
output = np.zeros(img.shape, np.uint8)
|
||||
for i in range(img.shape[0]):
|
||||
for j in range(img.shape[1]):
|
||||
if my_min < img[i][j] < my_max:
|
||||
output[i][j] = img[i][j]
|
||||
else:
|
||||
output[i][j] = 255
|
||||
return output
|
||||
|
||||
|
||||
def process_stop_choice(img, my_min, my_max):
|
||||
output = np.zeros(img.shape, np.uint8)
|
||||
for i in range(img.shape[0]):
|
||||
for j in range(img.shape[1]):
|
||||
if my_min < img[i][j] < my_max:
|
||||
output[i][j] = 0
|
||||
else:
|
||||
output[i][j] = img[i][j]
|
||||
return output
|
||||
|
||||
|
||||
def save_image(img):
|
||||
img_name = get_image_name() + DEFAULT_FORMAT
|
||||
cv2.imwrite(PREFIX + img_name, img)
|
||||
return img_name
|
||||
Loading…
Reference in new issue