{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport cv2\nimport numpy as np\nimport os","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-12-01T18:07:48.102469Z","iopub.execute_input":"2022-12-01T18:07:48.102837Z","iopub.status.idle":"2022-12-01T18:07:48.108466Z","shell.execute_reply.started":"2022-12-01T18:07:48.102801Z","shell.execute_reply":"2022-12-01T18:07:48.107168Z"},"trusted":true},"execution_count":411,"outputs":[]},{"cell_type":"code","source":"def load_dataset(path, classes):\n    class_images = []\n    \n    for cls in classes :\n        cls_imgs = []\n        img_names = os.listdir(path + cls + \"/\")\n        i=0\n        for img_name in img_names :\n            cls_imgs.append(cv2.resize(cv2.imread(path + cls + \"/\" + img_name), (300, 200)))\n            if(i==100): break\n            i+=1\n        class_images.append(np.array(cls_imgs))\n    return class_images","metadata":{"execution":{"iopub.status.busy":"2022-12-01T18:07:48.109475Z","iopub.execute_input":"2022-12-01T18:07:48.109792Z","iopub.status.idle":"2022-12-01T18:07:48.122545Z","shell.execute_reply.started":"2022-12-01T18:07:48.109764Z","shell.execute_reply":"2022-12-01T18:07:48.121377Z"},"trusted":true},"execution_count":412,"outputs":[]},{"cell_type":"code","source":"data = load_dataset(\"/kaggle/input/cell-images-for-detecting-malaria/cell_images/\", [\"Parasitized\"])\n","metadata":{"execution":{"iopub.status.busy":"2022-12-01T18:07:48.124834Z","iopub.execute_input":"2022-12-01T18:07:48.125194Z","iopub.status.idle":"2022-12-01T18:07:48.357798Z","shell.execute_reply.started":"2022-12-01T18:07:48.125163Z","shell.execute_reply":"2022-12-01T18:07:48.356868Z"},"trusted":true},"execution_count":413,"outputs":[]},{"cell_type":"code","source":"def change_contrast(image,alpha = 1.5,beta=25):\n    # Contrast control (1.0-3.0)\n    # Brightness control (0-100)\n    adjusted = cv2.convertScaleAbs(image, alpha=alpha, beta=beta)\n    return adjusted","metadata":{"execution":{"iopub.status.busy":"2022-12-01T18:07:48.359161Z","iopub.execute_input":"2022-12-01T18:07:48.360223Z","iopub.status.idle":"2022-12-01T18:07:48.365844Z","shell.execute_reply.started":"2022-12-01T18:07:48.360187Z","shell.execute_reply":"2022-12-01T18:07:48.364442Z"},"trusted":true},"execution_count":414,"outputs":[]},{"cell_type":"code","source":"def image_cleanup(image):\n    blurred = cv2.GaussianBlur(image, (3, 3), cv2.BORDER_DEFAULT)\n    thresh = cv2.threshold(blurred, 175, 250, cv2.THRESH_BINARY)[1]\n    return thresh","metadata":{"execution":{"iopub.status.busy":"2022-12-01T18:07:48.366976Z","iopub.execute_input":"2022-12-01T18:07:48.367270Z","iopub.status.idle":"2022-12-01T18:07:48.380959Z","shell.execute_reply.started":"2022-12-01T18:07:48.367243Z","shell.execute_reply":"2022-12-01T18:07:48.379868Z"},"trusted":true},"execution_count":415,"outputs":[]},{"cell_type":"code","source":"def remove_noisy_regions(image):\n    gray = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) \n    element = cv2.getStructuringElement(cv2.MORPH_RECT, (1,1))\n    mask = cv2.erode(gray, element, iterations = 50)\n    mask = cv2.dilate(mask, element, iterations = 50)\n    element = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))\n    mask = cv2.erode(gray, element, iterations = 1)\n    mask = cv2.dilate(mask, element, iterations = 1)\n    mask = cv2.erode(mask, element)\n    gray = cv2.cvtColor(mask, cv2.COLOR_HSV2BGR)\n    return gray","metadata":{"execution":{"iopub.status.busy":"2022-12-01T18:07:48.382126Z","iopub.execute_input":"2022-12-01T18:07:48.382432Z","iopub.status.idle":"2022-12-01T18:07:48.391958Z","shell.execute_reply.started":"2022-12-01T18:07:48.382404Z","shell.execute_reply":"2022-12-01T18:07:48.391101Z"},"trusted":true},"execution_count":416,"outputs":[]},{"cell_type":"code","source":"def find_contours_and_centers(img_input):\n    contours_raw, hierarchy = cv2.findContours(img_input, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n    contours = [i for i in contours_raw]\n    contour_centers = []\n    \n    for idx, c in enumerate(contours):\n        M = cv2.moments(c)\n        try:\n            cX = int(M[\"m10\"] / M[\"m00\"])\n            cY = int(M[\"m01\"] / M[\"m00\"])\n        except:\n            cX = int(M[\"m10\"] / (M[\"m00\"] + 0.0001))\n            cY = int(M[\"m01\"] / (M[\"m00\"] + 0.0001))\n        samp_bounds = cv2.boundingRect(c)\n        contour_centers.append(((cX,cY), samp_bounds))\n    contour_centers = sorted(contour_centers, key=lambda x: x[0])\n\n    return (contours, contour_centers)","metadata":{"execution":{"iopub.status.busy":"2022-12-01T18:07:48.393006Z","iopub.execute_input":"2022-12-01T18:07:48.394447Z","iopub.status.idle":"2022-12-01T18:07:48.403874Z","shell.execute_reply.started":"2022-12-01T18:07:48.394260Z","shell.execute_reply":"2022-12-01T18:07:48.403062Z"},"trusted":true},"execution_count":417,"outputs":[]},{"cell_type":"code","source":"def find_contour_and_area(img_input):\n    lll=img_input\n    i1 = change_contrast(lll,1.5,10)\n    i2 = image_cleanup(i1)\n    img = remove_noisy_regions(i2)\n    h,s,v = cv2.split(img)\n    ab = cv2.subtract(h,s)\n    fig,asx = plt.subplots(1,3)\n    asx[0].imshow(h,cmap=\"gray\")\n    asx[1].imshow(s,cmap=\"gray\")\n    asx[2].imshow(ab,cmap=\"gray\")\n    fig.tight_layout()\n    #plt.savefig(\"processing.png\",dpi=300)\n    conts, cents = find_contours_and_centers(s)\n    # circles = [i for i in conts if np.logical_and((cv2.contourArea(i) > 650),(cv2.contourArea(i) < 4000))]\n    print(len(cents))\n    img = lll.copy()\n    cv2.drawContours(img, conts, -1, (0,255,0), 2)\n    # show_image(img)\n\n\n    fig,asx = plt.subplots(1,2)\n    asx[0].imshow(lll)\n    asx[1].imshow(img,cmap=\"gray\")\n    fig.tight_layout()\n    #plt.savefig(\"contour_infected.png\",dpi=300)\n    if(len(cents)<2):\n        ratio=0\n    if(len(cents)==2):\n        ratio=cv2.contourArea(conts[1])/cv2.contourArea(conts[0])*100\n    elif (len(cents)>2):\n        area=0\n        for i in range(len(cents)):\n            if (i==0):continue\n            area+=cv2.contourArea(conts[i])\n        ratio=area/cv2.contourArea(conts[0])*100\n    \n    return ratio","metadata":{"execution":{"iopub.status.busy":"2022-12-01T18:07:48.405482Z","iopub.execute_input":"2022-12-01T18:07:48.406306Z","iopub.status.idle":"2022-12-01T18:07:48.419296Z","shell.execute_reply.started":"2022-12-01T18:07:48.406263Z","shell.execute_reply":"2022-12-01T18:07:48.418274Z"},"trusted":true},"execution_count":418,"outputs":[]},{"cell_type":"code","source":"find_contour_and_area(data[0][5])","metadata":{"execution":{"iopub.status.busy":"2022-12-01T18:09:08.980097Z","iopub.execute_input":"2022-12-01T18:09:08.981040Z","iopub.status.idle":"2022-12-01T18:09:09.734082Z","shell.execute_reply.started":"2022-12-01T18:09:08.980978Z","shell.execute_reply":"2022-12-01T18:09:09.732947Z"},"trusted":true},"execution_count":422,"outputs":[{"name":"stdout","text":"3\n","output_type":"stream"},{"execution_count":422,"output_type":"execute_result","data":{"text/plain":"1.7710270065862035"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 3 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]}]}