Upload ML model

es.py

+import cv2
+import numpy as np
+from keras import backend as K
+
+# K.set_image_dim_ordering('th')
+K.set_image_data_format('channels_first')
+from keras.models import model_from_json
+
+json_file = open('equation_solver/model_final.json', 'r')
+loaded_model_json = json_file.read()
+json_file.close()
+loaded_model = model_from_json(loaded_model_json)
+# load weights into new model
+loaded_model.load_weights("equation_solver/model_final.h5")
+
+
+def get_ans_from_image(img):
+    if img is not None:
+        img = ~img
+        ret, thresh = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
+        ctrs, ret = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+        cnt = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
+        w = int(28)
+        h = int(28)
+        train_data = []
+        # print(len(cnt))
+        rects = []
+        for c in cnt:
+            x, y, w, h = cv2.boundingRect(c)
+            rect = [x, y, w, h]
+            rects.append(rect)
+        # print(rects)
+        bool_rect = []
+        for r in rects:
+            l = []
+            for rec in rects:
+                flag = 0
+                if rec != r:
+                    if r[0] < (rec[0] + rec[2] + 10) and rec[0] < (r[0] + r[2] + 10) and r[1] < (
+                            rec[1] + rec[3] + 10) and \
+                            rec[1] < (r[1] + r[3] + 10):
+                        flag = 1
+                    l.append(flag)
+                if rec == r:
+                    l.append(0)
+            bool_rect.append(l)
+        # print(bool_rect)
+        dump_rect = []
+        for i in range(0, len(cnt)):
+            for j in range(0, len(cnt)):
+                if bool_rect[i][j] == 1:
+                    area1 = rects[i][2] * rects[i][3]
+                    area2 = rects[j][2] * rects[j][3]
+                    if area1 == min(area1, area2):
+                        dump_rect.append(rects[i])
+        # print(len(dump_rect))
+        final_rect = [i for i in rects if i not in dump_rect]
+        # print(final_rect)
+        for r in final_rect:
+            x = r[0]
+            y = r[1]
+            w = r[2]
+            h = r[3]
+            im_crop = thresh[y:y + h + 10, x:x + w + 10]
+
+            im_resize = cv2.resize(im_crop, (28, 28))
+
+            im_resize = np.reshape(im_resize, (1, 28, 28))
+            train_data.append(im_resize)
+
+    s = ''
+    for i in range(len(train_data)):
+        train_data[i] = np.array(train_data[i])
+        train_data[i] = train_data[i].reshape(1, 1, 28, 28)
+        result = loaded_model.predict_classes(train_data[i])
+        if result[0] == 10:
+            s = s + '-'
+        if result[0] == 11:
+            s = s + '+'
+        if result[0] == 12:
+            s = s + '*'
+        if result[0] == 0:
+            s = s + '0'
+        if result[0] == 1:
+            s = s + '1'
+        if result[0] == 2:
+            s = s + '2'
+        if result[0] == 3:
+            s = s + '3'
+        if result[0] == 4:
+            s = s + '4'
+        if result[0] == 5:
+            s = s + '5'
+        if result[0] == 6:
+            s = s + '6'
+        if result[0] == 7:
+            s = s + '7'
+        if result[0] == 8:
+            s = s + '8'
+        if result[0] == 9:
+            s = s + '9'
+
+    return eval(s)
+
+
+# img = cv2.imread('test.jpeg', cv2.IMREAD_GRAYSCALE)
+# print(get_ans_from_image(img))