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270 lines
10 KiB
270 lines
10 KiB
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from removebg import RemoveBg
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from django.http import JsonResponse
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from django.views.decorators.http import require_POST,require_GET
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from matplotlib import pyplot as plt
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import sys
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import imutils
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from PIL import Image
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sys.path.append("../")
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import urllib.request
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import base64
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import cv2
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import numpy as np
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import numba as nb
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from pyzbar.pyzbar import decode
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# base64转cv
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def base64tocv(imagecode):
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strList = str(imagecode).split(',')
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b64_img = ''
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for i in strList[1:]:
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b64_img += i
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imgString = base64.b64decode(b64_img)
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nparr = np.fromstring(imgString, np.uint8)
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image = cv2.imdecode(nparr, cv2.COLOR_RGB2BGR)
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# image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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return image
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# #cv 转 base64
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# def cvtobase64(img):
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# #
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# # image = cv2.imencode('.jpg', img)[1]
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# # res_bs64 = str(base64.b64encode(image))[2:-1]
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# res_b = cv2.imencode('.jpg', img)[1].tostring()
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# res_bs64 = base64.b64encode(res_b)
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# return res_bs64
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#InMemoryUploadedFile 转 ndarray
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def getNdarraybyInMemoryUploadedFile(uploaded_file):
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fileinbytes = uploaded_file.file
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img_np_arr = np.frombuffer(fileinbytes.read(), np.uint8)
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return cv2.imdecode(img_np_arr, cv2.IMREAD_COLOR)
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#ndarray 转 base64
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def getbase64byndarray(pic_img):
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retval, buffer = cv2.imencode('.jpg', pic_img)
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pic_str = base64.b64encode(buffer)
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return pic_str.decode()
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# 获取图像并返回
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@require_POST
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def getPicture(request):
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if(not request.FILES):
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response = urllib.request.urlopen(request.POST.get("files"))
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img_array = np.array(bytearray(response.read()), dtype=np.uint8)
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cv_image = cv2.imdecode(img_array, -1)
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elif(not request.POST):
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print(request.FILES)
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image = request.FILES.get('files')
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cv_image = getNdarraybyInMemoryUploadedFile(image)
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height,width,color = cv_image.shape
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base64_image=getbase64byndarray(cv_image)
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return JsonResponse(data={"image": base64_image,"height": height,"width": width}, json_dumps_params={'ensure_ascii': False})
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# 放大/缩小图片
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@require_POST
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def resizePicture(request):
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image = request.POST.get('files')
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cv_image = base64tocv(image);
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height,width,colors=cv_image.shape
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if(request.POST.get("height") is None):
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base64_image=getbase64byndarray(cv_image)
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return JsonResponse(data={"image": base64_image,"height":height,"width":width}, json_dumps_params={'ensure_ascii': False})
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height=request.POST.get("height")
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width=request.POST.get("width")
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resize_image = cv2.resize(cv_image, (int(width), int(height)),interpolation=cv2.INTER_LINEAR)
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height, width, colors = resize_image.shape
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resize_image=getbase64byndarray(resize_image)
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return JsonResponse(data={"image": resize_image,"height":height,"width":width}, json_dumps_params={'ensure_ascii': False})
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# 翻转图片
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@require_POST
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def mirrorPicture(request):
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image = request.POST.get('files')
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cv_image = base64tocv(image);
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direction=request.POST.get('direction')
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if(direction=='0'):
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cv_image = cv2.flip(cv_image, 1, dst=None)
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elif(direction=='1'):
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cv_image = cv2.flip(cv_image,0,dst=None)
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elif(direction=='2'):
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cv_image = cv2.flip(cv_image,-1,dst=None)
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mirror_image=getbase64byndarray(cv_image)
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return JsonResponse(data={"image": mirror_image}, json_dumps_params={'ensure_ascii': False})
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# 旋转图片
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@require_POST
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def rotatePicture(request):
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image = request.POST.get('files')
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cv_image = base64tocv(image)
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rows, cols, depth = cv_image.shape
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(cX, cY) = (cols // 2, rows // 2)
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direction=request.POST.get('direction')
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if(direction=='left'):
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M = cv2.getRotationMatrix2D((cols / 2, rows / 2), 90, 1)
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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((rows * sin) + (cols * cos))
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nH = int((rows * cos) + (cols * 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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cv_image = cv2.warpAffine(cv_image, M, (nW, nH),borderValue=(255,255,255))
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elif(direction=='right'):
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M = cv2.getRotationMatrix2D((cols / 2, rows / 2), -90, 1)
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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((rows * sin) + (cols * cos))
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nH = int((rows * cos) + (cols * 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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cv_image = cv2.warpAffine(cv_image, M, (nW, nH),borderValue=(255,255,255))
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rotate_image=getbase64byndarray(cv_image)
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return JsonResponse(data={"image": rotate_image}, json_dumps_params={'ensure_ascii': False})
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#抠图
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@require_POST
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def keying(request):
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rmbg = RemoveBg("XHfvSY3phCVna4NZj8Tr8Wnz", "error.log")
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image = request.POST.get('files')
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rmbg.remove_background_from_base64_img(image,new_file_name="output.jpg",bg_color=(0,0,0))
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result=cv2.imread("output.jpg")
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result=getbase64byndarray(result)
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return JsonResponse(data={"image": result}, json_dumps_params={'ensure_ascii': False})
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#图像平滑
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@require_POST
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def smooth(request):
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base_image = request.POST.get('files')
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cv_image=base64tocv(base_image)
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imgarray = np.array(cv_image)
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height, width = imgarray.shape[0], imgarray.shape[1]
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edge = int((3 - 1) / 2)
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if height - 1 - edge <= edge or width - 1 - edge <= edge:
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print("the kenerl is to long")
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return None
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for i in range(height):
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for j in range(width):
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if i <= edge - 1 or i >= height - 1 - edge or j <= edge - 1 or j >= height - 1 - edge:
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imgarray[i][j] = imgarray[i][j]
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else:
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num = []
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for m in range(i - edge, i + edge + 1):
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for n in range(j - edge, j + edge + 1):
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num.append((imgarray[m][n])[0]) # 这里通过彩色图像第一个值计算中值也可以改为第二个或者第三个
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temp = np.median(num)
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idex_tem = num.index(temp) # 获取中值在数组中的坐标
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l1 = int((idex_tem / 3)) - edge + i # 根据进制转换反推出中值在图像中的坐标
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l2 = (idex_tem % 3) - edge + j
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imgarray[i][j] = imgarray[l1][l2] # 赋值
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# num1 = np.sort(num)[int(self.k * self.k / 2)]
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# print(idex_tem)
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# temp = np.median(imgarray[i][j], imgarray[i - edge][j], imgarray[i - edge][j])
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# imgarray[i][j] = np.median(imgarray[i - edge:i + edge + 1, j -edge:j+edge + 1])
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new_img = Image.fromarray(imgarray)
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result=getbase64byndarray(imgarray)
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return JsonResponse(data={"image": result}, json_dumps_params={'ensure_ascii': False})
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#曝光调整
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def exposure(request):
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img = request.POST.get('files')
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image = base64tocv(img)
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# chans = cv2.split(image)
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# colors = ("b", "g", "r")
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# for (chan, color) in zip(chans, colors):
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# hist = cv2.calcHist([chan], [0], None, [256], [0, 256])
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# # plt.plot(hist, color=color)
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# # plt.xlim([0, 256])
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# plt.subplot(4, 2, 5)
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equalizeOver = np.zeros(image.shape, image.dtype)
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equalizeOver[:, :, 0] = cv2.equalizeHist(image[:, :, 0])
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equalizeOver[:, :, 1] = cv2.equalizeHist(image[:, :, 1])
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equalizeOver[:, :, 2] = cv2.equalizeHist(image[:, :, 2])
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output = getbase64byndarray(equalizeOver)
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return JsonResponse(data={"image": output}, json_dumps_params={'ensure_ascii': False})
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#锐化
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def sharpen(request):
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img = request.POST.get('files')
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image = base64tocv(img)
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sharpen_op = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], dtype=np.float32)
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# sharpen_op = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]], dtype=np.float32)
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sharpen_image = cv2.filter2D(image, cv2.CV_32F, sharpen_op)
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sharpen_image = cv2.convertScaleAbs(sharpen_image)
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output = getbase64byndarray(sharpen_image)
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return JsonResponse(data={"image": output}, json_dumps_params={'ensure_ascii': False})
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#识别条形码
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def barCode(request):
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img = request.POST.get('files')
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image = base64tocv(img)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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cv2.imshow("Image", gray)
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cv2.waitKey(0)
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# 计算图片x和y方向的Scharr梯度大小
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ddepth = cv2.cv.CV_32F if imutils.is_cv2() else cv2.CV_32F
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gradX = cv2.Sobel(gray, ddepth=ddepth, dx=1, dy=0, ksize=-1)
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gradY = cv2.Sobel(gray, ddepth=ddepth, dx=0, dy=1, ksize=-1)
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# 用x方向的梯度减去y方向的梯度
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gradient = cv2.subtract(gradX, gradY)
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gradient = cv2.convertScaleAbs(gradient)
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cv2.imshow("gradient", gradient)
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cv2.waitKey(0)
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blurred = cv2.blur(gradient, (9, 9))
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(_, thresh) = cv2.threshold(blurred, 180, 255, cv2.THRESH_BINARY)
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cv2.imshow("blur", blurred)
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cv2.waitKey(0)
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cv2.imshow("thresh", thresh)
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cv2.waitKey(0)
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# 构造一个闭合核并应用于阈值图片
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 10))
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closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
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cv2.imshow("closed", closed)
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cv2.waitKey(0)
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# 执行一系列的腐蚀和膨胀操作
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closed = cv2.dilate(closed, None, iterations=4)
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closed = cv2.erode(closed, None, iterations=4)
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cv2.imshow("Image", closed)
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cv2.waitKey(0)
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# 找到阈值化后图片中的轮廓，然后进行根据区域进行排序，仅保留最大区域
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cnts = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cnts = imutils.grab_contours(cnts)
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c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
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# 计算最大轮廓的旋转边界框
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rect = cv2.minAreaRect(c)
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box = cv2.cv.BoxPoints(rect) if imutils.is_cv2() else cv2.boxPoints(rect)
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box = np.int0(box)
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# 在检测到的条形码周围绘制边界框并显示图片
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cv2.drawContours(image, [box], -1, (0, 255, 0), 3)
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result=getbase64byndarray(image)
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return JsonResponse(data={"image": result}, json_dumps_params={'ensure_ascii': False})
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#噪声去除
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def removeNoise(request):
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img = request.POST.get('files')
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img = base64tocv(img)
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specular = cv2.fastNlMeansDenoisingColored(img, None, 10, 5, 7, 21)
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result = getbase64byndarray(specular)
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return JsonResponse(data={"image": result}, json_dumps_params={'ensure_ascii': False})
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