FLANN and template matching

Author: magesh-technovator

modifiedFLANNAlgo.py

@@ -8,6 +8,8 @@
 import matplotlib.pyplot as plt
 import os
 import numpy as np
+import json
+from tqdm import tqdm
 
 base_dir = os.getcwd()
 data_folder = os.path.join(base_dir, "Dataset")
@@ -16,6 +18,7 @@
 crops_folder = os.path.join(data_folder, "Crops")
 sample_testset = os.path.join(data_folder, "sample_testset")
 
+sample_json_result = os.path.join(sample_testset, "sample_result.json")
 sample_images = os.path.join(sample_testset, "images")
 sample_crops = os.path.join(sample_testset, "crops")
 
@@ -32,9 +35,23 @@ def ModifiedFLANN(img1, img2):
  kp2, des2 = sift.detectAndCompute(img2, None)
 
  orgBorder = None
+ flannMatch = True
 
  if (des1 is None) or (des2 is None):
- return orgBorder
+ flannMatch = False
+ 
+ if (img2.shape[0] > img1.shape[0]) and (img2[1].shape[1] > img1.shape[1]):
+ res = cv2.matchTemplate(img2,img1, cv2.TM_CCOEFF)
+ min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
+ h, w, c = img1.shape
+ 
+ pts = [int(min_loc[0]), int(min_loc[0]) + w,
+ int(max_loc[0]), int(max_loc[1])]
+ 
+ return flannMatch, pts
+
+ return flannMatch, orgBorder
+ 
  flann = cv2.FlannBasedMatcher(index_params, search_params)
  matches = flann.knnMatch(des1, des2, k=2)
 
@@ -58,37 +75,33 @@ def ModifiedFLANN(img1, img2):
 
  H, status = cv2.findHomography(cropImg, orgImg, cv2.RANSAC, 3.0)
 
+ if H is None:
+ return flannMatch, orgBorder
+ 
  h, w, c = img1.shape
 
  cropBorder = np.float32([[[0,0], [0,h-1], [w-1,h-1], [w-1,0]]])
+ 
  orgBorder = cv2.perspectiveTransform(cropBorder, H)
 
  cv2.polylines(img2, [np.int32(orgBorder)], True, (0, 255, 0), 5)
- 
- else:
- print("No Good Matches Found")
- 
- matched = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
- plt.imshow(matched)
- plt.show()
- 
- return orgBorder
+ 
+ return flannMatch, orgBorder
 
 def findMinMax(border):
  x, y = np.transpose(border)[0], np.transpose(border)[1]
 
- x1, x2 = x.min(), x.max()
+ x1, x2 = int(x.min()), int(x.max())
 
- y1, y2 = y.min(), x.max()
+ y1, y2 = int(y.min()), int(x.max())
 
- return (x1, y1, x2, y2)
+ return [x1, y1, x2, y2]
 
 completeTracker = {}
 
-allImages = os.listdir(sample_images)
-allcropImages = os.listdir(sample_crops)
+noAssociationCropImages = os.listdir(sample_crops)
 
-for imagefile in os.listdir(sample_images):
+for imagefile in tqdm(os.listdir(sample_images)):
  img = cv2.imread(os.path.join(sample_images,
  imagefile))
  imageTracker = []
@@ -97,20 +110,31 @@ def findMinMax(border):
  crop_img = cv2.imread(os.path.join(sample_crops,
  cropfile))
 
- crop_border = ModifiedFLANN(crop_img, img)
- 
- if crop_border is None:
- print("Images are Not associated")
+ flannMatch, crop_border = ModifiedFLANN(crop_img, img)
 
+ if flannMatch:
+ if crop_border is not None:
+ pts = findMinMax(crop_border[0])
+ imageTracker.append((cropfile.replace(".jpg", ""), pts))
+ if cropfile in noAssociationCropImages:
+ noAssociationCropImages.remove(cropfile)
  else:
- print("images are Associated")
- pts = findMinMax(crop_border[0])
- imageTracker.append((cropfile.replace(".jpg", ""), pts))
+ if crop_border is not None:
+ imageTracker.append((cropfile.replace(".jpg", ""), crop_border))
+ if cropfile in noAssociationCropImages:
+ noAssociationCropImages.remove(cropfile)
 
  completeTracker[imagefile.replace(".jpg", "")] = imageTracker
-# =============================================================================
-# plt.imshow(cv2.cvtColor(crop_img, cv2.COLOR_BGR2RGB))
-# plt.show()
-# 
-# =============================================================================
 
+NA_Crops = []
+
+for crop in noAssociationCropImages:
+ NA_Crops.append([crop.replace(".jpg", ""), []])
+ 
+completeTracker["NA"] = NA_Crops
+
+with open(sample_json_result, "w") as f:
+ json.dump(completeTracker, f)
+
+
+print("Output Json File is generated")