Cancer-Detection---Deep-Learning_Project-using-python

import numpy as np from keras.preprocessing import image from keras.models import load_model from PIL import Image import keras.backend as K K.set_image_data_format('channels_last') import os

modelSavePath = 'D:\LIVE WIRE - DS\Project\my_model3.h5' numOfTestPoints = 2 batchSize = 16 numOfEpoches = 10

classes = []

def mean_pred(y_true, y_pred): return K.mean(y_pred)

def getCropImgs(img, needRotations=False): z = np.asarray(img, dtype=np.int8) c = [] for i in range(3): for j in range(4): crop = z[512 * i:512 * (i + 1), 512 * j:512 * (j + 1), :]

        c.append(crop)
        if needRotations:
            c.append(np.rot90(np.rot90(crop)))
return c

def getAsSoftmax(fname): if (fname == 'b'): return [1, 0, 0, 0] elif (fname == 'is'): return [0, 1, 0, 0] elif (fname == 'iv'): return [0, 0, 1, 0] else: return [0, 0, 0, 1]

def get_imgs_frm_folder(path): x = [] y = [] cnt = 0 for foldname in os.listdir(path): for filename in os.listdir(os.path.join(path, foldname)): img = Image.open(os.path.join(os.path.join(path, foldname), filename)) # img.show() crpImgs = getCropImgs(img) cnt += 1 if cnt % 10 == 0: print(str(cnt) + " Images loaded") for im in crpImgs: x.append(np.divide(np.asarray(im, np.float16), 255.)) y.append(getAsSoftmax(foldname)) print("Images cropped") print("Loading as array") return x, y, cnt

def predict(img, savedModelPath, showImg=True): model = load_model(savedModelPath) # if showImg: # Image.fromarray(np.array(img, np.float16), 'RGB').show()

x = img
if showImg:
    Image.fromarray(np.array(img, np.float16), 'RGB').show()
x = np.expand_dims(x, axis=0)

softMaxPred = model.predict(x)
print("prediction from Algo: " + str(softMaxPred) + "\n")
probs = softmaxToProbs(softMaxPred)

# plot_model(model, to_file='Model.png')
# SVG(model_to_dot(model).create(prog='dot', format='svg'))
maxprob = 0
maxI = 0
for j in range(len(probs)):
    # print(str(j) + " : " + str(round(probs[j], 4)))
    if probs[j] > maxprob:
        maxprob = probs[j]
        maxI = j
# print(softMaxPred)
print("prediction index: " + str(maxI))
return maxI, probs

def softmaxToProbs(soft): z_exp = [np.math.exp(i) for i in soft[0]] sum_z_exp = sum(z_exp) return [(i / sum_z_exp) * 100 for i in z_exp]

def predictImage(img_path, arrayImg=None, printData=True): crops = [] if arrayImg == None: img = image.load_img(img_path) crops = np.array(getCropImgs(img, needRotations=False), np.float16) crops = np.divide(crops, 255.) Image.fromarray(np.array(crops[0]), "RGB").show()

classes = []
classes.append("Benign")
classes.append("InSitu")
classes.append("Invasive")
classes.append("Normal")

compProbs = []
compProbs.append(0)
compProbs.append(0)
compProbs.append(0)
compProbs.append(0)

for i in range(len(crops)):
    if printData:
        print("\n\nCrop " + str(i + 1) + " prediction:\n")

    ___, probs = predict(crops[i], modelSavePath, showImg=False)

    for j in range(len(classes)):
        if printData:
            print(str(classes[j]) + " : " + str(round(probs[j], 4)) + "%")
        compProbs[j] += probs[j]

if printData:
    print("\n\nAverage from all crops\n")

for j in range(len(classes)):
    if printData:
        print(str(classes[j]) + " : " + str(round(compProbs[j] / 12, 4)) + "%")

import tkinter.filedialog as f import tkinter win=tkinter.Tk() predictImage(f.askopenfilename(filetypes=())) win.destroy()