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import json
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from django.shortcuts import render
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from django.views.decorators.csrf import csrf_exempt
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from pywt import dwt2, idwt2
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from .helper import *
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PREFIX = 'media/'
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DEFAULT_FORMAT = '.jpg'
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@csrf_exempt
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def upload(request):
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if request.method == 'POST':
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imageName = get_image(request)
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if imageName == '':
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return HttpResponse('Not supported image format')
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return HttpResponse(imageName)
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return HttpResponse('Please use POST')
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@csrf_exempt
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def basic_drawing(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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draw_type = para['type']
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if draw_type == 'bg':
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return HttpResponse(process_bg())
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else:
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if draw_type == 'line':
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return HttpResponse(process_line(para))
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elif draw_type == 'rectangle':
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return HttpResponse(process_rectangle(para))
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elif draw_type == 'circle':
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return HttpResponse(process_circle(para))
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elif draw_type == 'ellipse':
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return HttpResponse(process_ellipse(para))
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elif draw_type == 'polylines':
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return HttpResponse(process_polylines(para))
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elif draw_type == 'text':
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return HttpResponse(process_text(para))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def rgb_color_space(request, color):
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if request.method == 'POST':
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para = json.loads(request.body)
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image_name = para['img']
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img = cv2.imread(PREFIX + image_name)
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data = None
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if color == 'r':
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data = img[:, :, 2]
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elif color == 'g':
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data = img[:, :, 1]
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elif color == 'b':
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data = img[:, :, 0]
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if para.get('filter', False):
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data = process_filter_rgb(para, data)
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filename = color + '-' + image_name
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cv2.imwrite(PREFIX + filename, data)
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return HttpResponse(filename)
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return HttpResponse('Please use POST')
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@csrf_exempt
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def hsv_color_space(request, color):
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if request.method == 'POST':
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para = json.loads(request.body)
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image_name = para['img']
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img = cv2.imread(PREFIX + image_name)
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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data = None
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if color == 'h':
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data = hsv[:, :, 0]
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elif color == 's':
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data = hsv[:, :, 1]
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elif color == 'v':
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data = hsv[:, :, 2]
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elif color == 'filter':
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upper_bound = para.get('ub', [0, 0, 0])
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lower_bound = para.get('lb', [200, 40, 100])
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data = cv2.inRange(hsv, np.array(upper_bound), np.array(lower_bound))
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filename = color + '-' + image_name
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cv2.imwrite(PREFIX + filename, data)
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return HttpResponse(filename)
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return HttpResponse('Please use POST')
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@csrf_exempt
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def basic_operation(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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operator = para['operator']
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img = para['img']
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has_color = para['color']
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base = para['base']
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if operator == 'and':
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return HttpResponse(logic_and(img, has_color, base))
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elif operator == 'or':
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return HttpResponse(logic_or(img, has_color, base))
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elif operator == 'not':
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return HttpResponse(logic_not(img, has_color, base))
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elif operator == 'add':
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return HttpResponse(arithmetic_add(img, has_color, base))
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elif operator == 'sub':
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return HttpResponse(arithmetic_sub(img, has_color, base))
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elif operator == 'multi':
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return HttpResponse(arithmetic_multi(img, has_color, base))
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elif operator == 'div':
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return HttpResponse(arithmetic_div(img, has_color, base))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def resize(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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img = cv2.imread(PREFIX + image)
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size = para.get('size', None)
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if size is not None:
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height, width = img.shape[:2]
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if width * height >= size[0] * size[1]:
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interpolation = cv2.INTER_AREA
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else:
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interpolation = cv2.INTER_CUBIC
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img = cv2.resize(img, tuple(size), interpolation)
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else:
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multiple = para['multiple']
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if multiple < 1:
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interpolation = cv2.INTER_AREA
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else:
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interpolation = cv2.INTER_CUBIC
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img = cv2.resize(img, (0, 0), fx=multiple, fy=multiple, interpolation=interpolation)
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return HttpResponse(save_image(img))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def rotate(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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img = cv2.imread(PREFIX + image)
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angle = para['angle']
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img = rotate_bound(img, angle)
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return HttpResponse(save_image(img))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def translation(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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img = cv2.imread(PREFIX + image)
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offset = para['offset']
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height, width = img.shape[:2]
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M = np.float32([[1, 0, offset[0]], [0, 1, offset[1]]])
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img = cv2.warpAffine(img, M, (width, height))
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return HttpResponse(save_image(img))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def flip(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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img = cv2.imread(PREFIX + image)
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flip_code = para.get('flip', 1)
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img = cv2.flip(img, flip_code)
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return HttpResponse(save_image(img))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def affine(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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img = cv2.imread(PREFIX + image)
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before = np.float32(para['before'])
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after = np.float32(para['after'])
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rows, cols = img.shape[:2]
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M = cv2.getAffineTransform(before, after)
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img = cv2.warpAffine(img, M, (rows, cols))
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return HttpResponse(save_image(img))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def histogram(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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color = para['color']
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histogram_name = get_image_name() + DEFAULT_FORMAT
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if color:
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img = cv2.imread(PREFIX + image)
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hist_cover_rgb(img, PREFIX + histogram_name)
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else:
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img = cv2.imread(PREFIX + image, 0)
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hist_cover_gray(img, PREFIX + histogram_name)
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return HttpResponse(histogram_name)
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return HttpResponse('Please use POST')
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@csrf_exempt
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def piecewise_linear_transform(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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img = cv2.imread(PREFIX + image, 0)
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funcs = para['func']
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ret_name1 = get_image_name() + DEFAULT_FORMAT
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ret_name2 = get_image_name() + DEFAULT_FORMAT
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out_name = get_image_name() + DEFAULT_FORMAT
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h, w = img.shape[:2]
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out = np.zeros(img.shape, np.uint8)
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for i in range(h):
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for j in range(w):
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pix = img[i][j]
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last_limit = float("-inf")
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for func in funcs:
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limit = func['limit']
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k = func['k']
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b = func['b']
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if limit == -1:
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if pix > last_limit:
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out[i][j] = k * pix + b
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else:
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if last_limit <= pix < limit:
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out[i][j] = k * pix + b
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last_limit = limit
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out = np.around(out).astype(np.uint8)
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cv2.imwrite(PREFIX + out_name, out)
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grayHist(img, PREFIX + ret_name1)
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grayHist(out, PREFIX + ret_name2)
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return HttpResponse([{"orig": image, "hist": ret_name1}, {"orig": out_name, "hist": ret_name2}])
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return HttpResponse('Please use POST')
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@csrf_exempt
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def edge_detection(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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img = cv2.imread(PREFIX + image, 0)
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operator = para['operator']
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if operator == 'Roberts':
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img = process_roberts(img)
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elif operator == 'Sobel':
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img = process_sobel(img)
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elif operator == 'Laplacian':
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img_gaussianBlur = cv2.GaussianBlur(img, (5, 5), 0)
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img = process_laplacian(img_gaussianBlur)
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elif operator == 'LoG':
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img = process_LoG(img)
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elif operator == 'Canny':
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img = process_Canny(img)
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# 这里由于返回格式的不同，会在process_enhance之中直接返回，不在往后执行
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elif operator == 'Enhance':
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process_enhance(img)
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return HttpResponse(save_image(img))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def line_change_detection(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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img = cv2.imread(PREFIX + image)
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use_p = para['use_p']
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img = cv2.GaussianBlur(img, (3, 3), 0)
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edges = cv2.Canny(img, 50, 150, apertureSize=3)
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if use_p:
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img = process_lcd_with_p(edges, img)
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else:
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img = process_lcd_without_p(edges, img)
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return HttpResponse(save_image(img))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def smooth(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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img = cv2.imread(PREFIX + image, 0)
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# frequency domain
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fd = para['fd']
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my_filter = para['filter']
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if fd:
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if my_filter == 'ideal_low_pass_filter':
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d0 = para.get('d0', 20)
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img = filter_use_smooth(img, ideal_low_filter(img, d0))
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elif my_filter == 'butterworth_low_pass_filter':
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d0 = para.get('d0', 20)
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rank = para.get('rank', 2)
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img = filter_use_smooth(img, butterworth_low_pass_filter(img, d0, rank))
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elif my_filter == 'gauss_low_pass_filter':
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d0 = para.get('d0', 20)
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img = filter_use_smooth(img, gauss_low_pass_filter(img, d0))
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else:
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size = para['size']
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if my_filter == 'average':
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img = cv2.blur(img, (size, size))
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elif my_filter == 'median':
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img = cv2.medianBlur(img, size)
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return HttpResponse(save_image(img))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def sharpen(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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img = cv2.imread(PREFIX + image, 0)
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# frequency domain
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fd = para['fd']
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my_filter = para['filter']
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if fd:
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if my_filter == 'ideal_high_pass_filter':
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d0 = para.get('d0', 40)
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img = filter_use_sharpen(img, ideal_high_pass_filter(img, d0))
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elif my_filter == 'butterworth_high_pass_filter':
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d0 = para.get('d0', 20)
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rank = para.get('rank', 2)
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img = filter_use_sharpen(img, butterworth_high_pass_filter(img, d0, rank))
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elif my_filter == 'gauss_high_pass_filter':
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d0 = para.get('d0', 20)
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img = filter_use_sharpen(img, gauss_high_pass_filter(img, d0))
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else:
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if my_filter == 'Roberts':
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img = process_roberts(img)
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elif my_filter == 'Sobel':
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img = process_sobel(img)
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elif my_filter == 'Prewitt':
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img = process_prewitt(img)
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elif my_filter == 'Laplacian':
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img = process_laplacian(img)
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return HttpResponse(save_image(img))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def morphology(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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size = para.get('size', 5)
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img = cv2.imread(PREFIX + image, cv2.IMREAD_UNCHANGED)
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kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (size, size))
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op = para['type']
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if op == 'open':
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img = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
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elif op == 'close':
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img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
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elif op == 'erode':
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img = cv2.erode(img, kernel)
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elif op == 'dilate':
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img = cv2.dilate(img, kernel)
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return HttpResponse(save_image(img))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def noise(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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img = cv2.imread(PREFIX + image)
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op = para['type']
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if op == 'gauss':
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img = process_gauss_noise(img)
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elif op == 'salt_and_pepper':
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img = process_salt_and_peper_noise(img, para)
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return HttpResponse(save_image(img))
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return HttpResponse('Please use POST')
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@csrf_exempt
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def filtration(request):
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if request.method == 'POST':
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para = json.loads(request.body)
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image = para['img']
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img = cv2.imread(PREFIX + image, 0)
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op = para['type']
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detail_type = para['detail_type']
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if op == 'mean':
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if detail_type == 'arithmetic':
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img = process_arithmetic_mean(img)
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elif detail_type == 'geometric':
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img = process_geometric_mean(img)
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elif detail_type == 'harmonic':
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img = process_harmonic_filter(img)
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elif op == 'sort':
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if detail_type == 'max':
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img = process_max_sort(img)
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elif detail_type == 'median':
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img = process_median_sort(img)
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elif detail_type == 'min':
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img = process_min_sort(img)
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elif op == 'choice':
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if detail_type == 'low':
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limit = para.get('limit', 20)
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img = process_low_choice(img, limit)
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elif detail_type == 'high':
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limit = para.get('limit', 200)
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img = process_high_choice(img, limit)
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elif detail_type == 'pass':
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min_r = para.get('min', 20)
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|
|
max_r = para.get('max', 200)
|
|
|
img = process_pass_choice(img, min_r, max_r)
|
|
|
elif detail_type == 'stop':
|
|
|
min_r = para.get('min', 20)
|
|
|
max_r = para.get('max', 200)
|
|
|
img = process_stop_choice(img, min_r, max_r)
|
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|
return HttpResponse(save_image(img))
|
|
|
return HttpResponse('Please use POST')
|
|
|
|
|
|
|
|
|
@csrf_exempt
|
|
|
def wavelet(request):
|
|
|
if request.method == 'POST':
|
|
|
para = json.loads(request.body)
|
|
|
image = para['img']
|
|
|
img = cv2.imread(PREFIX + image, 0)
|
|
|
# 对img进行haar小波变换：
|
|
|
cA, (cH, cV, cD) = dwt2(img, 'haar')
|
|
|
rimg = idwt2((cA, (cH, cV, cD)), 'haar')
|
|
|
ret = [
|
|
|
{"low": save_image(np.uint8(cA / np.max(cA) * 255)),
|
|
|
"horizontal_high": save_image(np.uint8(cH / np.max(cH) * 255)),
|
|
|
"vertical_high": save_image(np.uint8(cV / np.max(cV) * 255)),
|
|
|
"diagonal_high": save_image(np.uint8(cD / np.max(cD) * 255)),
|
|
|
"refactor": save_image(np.uint8(rimg))
|
|
|
}
|
|
|
]
|
|
|
return HttpResponse(ret)
|
|
|
return HttpResponse('Please use POST')
|
|
|
|
|
|
|
|
|
@csrf_exempt
|
|
|
def fourier(request):
|
|
|
if request.method == 'POST':
|
|
|
para = json.loads(request.body)
|
|
|
image = para['img']
|
|
|
img = cv2.imread(PREFIX + image, 0)
|
|
|
# 傅里叶
|
|
|
f = np.fft.fft2(img)
|
|
|
fshift = np.fft.fftshift(f)
|
|
|
magnitude_spectrum = 20 * np.log(np.abs(fshift))
|
|
|
# 逆傅里叶
|
|
|
ishift = np.fft.ifftshift(f)
|
|
|
iimg = np.fft.ifft2(ishift)
|
|
|
iimg = np.abs(iimg)
|
|
|
ret = [{"fourier_transform": save_image(magnitude_spectrum), "inverse": save_image(iimg)}]
|
|
|
return HttpResponse(ret)
|
|
|
return HttpResponse('Please use POST')
|
|
|
|
|
|
|
|
|
def load(request):
|
|
|
return render(request, 'upload.html')
|
