--- a
+++ b/detect.py
@@ -0,0 +1,112 @@
+import cv2
+import time
+from utils import iou
+from scipy import spatial
+from darkflow.net.build import TFNet
+
+options = {'model': 'cfg/tiny-yolo-voc-3c.cfg',
+           'load': 3750,
+           'threshold': 0.1,
+           'gpu': 0.7}
+
+tfnet = TFNet(options)
+
+pred_bb = []  # predicted bounding box
+pred_cls = []  # predicted class
+pred_conf = []  # predicted class confidence
+
+
+def blood_cell_count(file_name):
+    rbc = 0
+    wbc = 0
+    platelets = 0
+
+    cell = []
+    cls = []
+    conf = []
+
+    record = []
+    tl_ = []
+    br_ = []
+    iou_ = []
+    iou_value = 0
+
+    tic = time.time()
+    image = cv2.imread('data/' + file_name)
+    output = tfnet.return_predict(image)
+
+    for prediction in output:
+        label = prediction['label']
+        confidence = prediction['confidence']
+        tl = (prediction['topleft']['x'], prediction['topleft']['y'])
+        br = (prediction['bottomright']['x'], prediction['bottomright']['y'])
+
+        if label == 'RBC' and confidence < .5:
+            continue
+        if label == 'WBC' and confidence < .25:
+            continue
+        if label == 'Platelets' and confidence < .25:
+            continue
+
+        # clearing up overlapped same platelets
+        if label == 'Platelets':
+            if record:
+                tree = spatial.cKDTree(record)
+                index = tree.query(tl)[1]
+                iou_value = iou(tl + br, tl_[index] + br_[index])
+                iou_.append(iou_value)
+
+            if iou_value > 0.1:
+                continue
+
+            record.append(tl)
+            tl_.append(tl)
+            br_.append(br)
+
+        center_x = int((tl[0] + br[0]) / 2)
+        center_y = int((tl[1] + br[1]) / 2)
+        center = (center_x, center_y)
+
+        if label == 'RBC':
+            color = (255, 0, 0)
+            rbc = rbc + 1
+        if label == 'WBC':
+            color = (0, 255, 0)
+            wbc = wbc + 1
+        if label == 'Platelets':
+            color = (0, 0, 255)
+            platelets = platelets + 1
+
+        radius = int((br[0] - tl[0]) / 2)
+        image = cv2.circle(image, center, radius, color, 2)
+        font = cv2.FONT_HERSHEY_COMPLEX
+        image = cv2.putText(image, label, (center_x - 15, center_y + 5), font, .5, color, 1)
+        cell.append([tl[0], tl[1], br[0], br[1]])
+
+        if label == 'RBC':
+            cls.append(0)
+        if label == 'WBC':
+            cls.append(1)
+        if label == 'Platelets':
+            cls.append(2)
+
+        conf.append(confidence)
+
+    toc = time.time()
+    pred_bb.append(cell)
+    pred_cls.append(cls)
+    pred_conf.append(conf)
+    avg_time = (toc - tic) * 1000
+    print('{0:.5}'.format(avg_time), 'ms')
+
+    cv2.imwrite('output/' + file_name, image)
+    cv2.imshow('Total RBC: ' + str(rbc) + ', WBC: ' + str(wbc) + ', Platelets: ' + str(platelets), image)
+    print('Press "ESC" to close . . .')
+    if cv2.waitKey(0) & 0xff == 27:
+        cv2.destroyAllWindows()
+
+
+image_name = 'image_001.jpg'
+blood_cell_count(image_name)
+
+print('All Done!')