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Author: kamkyu94

1. utils/ext_frame.py

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+import os
+import cv2
+
+modes = ['train', 'validation']
+basic_path = '../../dataset/AIC21_Track3/'
+
+for mode in modes:
+    # Data Path
+    data_path = basic_path + mode + '/'
+
+    # Extract frame
+    scenes = os.listdir(data_path)
+    for scene in scenes:
+        cams = os.listdir(data_path + scene)
+        for cam in cams:
+            # Set paths
+            cam_path = data_path + scene + '/' + cam + '/'
+            video_path, frame_path = cam_path + 'vdo.avi', cam_path + 'frame/'
+
+            # Create frame directory
+            if not os.path.exists(frame_path):
+                os.mkdir(frame_path)
+
+            # Read video
+            video = cv2.VideoCapture(video_path)
+
+            # Save each frame
+            f_num = 1
+            while 1:
+                success, image = video.read()
+                if not success:
+                    break
+                cv2.imwrite(frame_path + '%s_f%04d.jpg' % (cam, f_num), image)
+                f_num += 1
+
+            # Print current status
+            print('%s_%s Finished' % (scene, cam))

1. utils/gen_te_det_info.py

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+import os
+import cv2
+import copy
+import pickle
+import numpy as np
+import img2gps as img2gps
+import pre_process as pre_process
+
+# Set
+basic_path = '../../dataset/AIC21_Track3/test/S06/'
+
+# Visualize and save
+det_info = {'S06': {}}
+cams = os.listdir(basic_path)
+for cam in cams:
+    # Newly add
+    if cam not in det_info.keys():
+        det_info['S06'][cam] = {}
+
+    # Open detection file
+    det_file_path = basic_path + '/%s/det/det_mask_rcnn.txt' % cam
+    det_file = np.loadtxt(det_file_path, dtype=np.float32, delimiter=',').reshape(-1, 10)
+
+    # Read RoI
+    roi = cv2.imread(basic_path + cam + '/roi.jpg').astype('float32')[:, :, 0] / 255
+    img_h, img_w = roi.shape[0], roi.shape[1]
+
+    # Filter with objectiveness score and size
+    det_file = det_file[det_file[:, 6] >= 0.2, :]
+    det_file = det_file[det_file[:, 4] * det_file[:, 5] >= 660, :]
+
+    # For each frame
+    for f_num in range(1, int(np.max(det_file[:, 0] + 1))):
+        # Newly add
+        if f_num not in det_info['S06'][cam].keys():
+            det_info['S06'][cam][f_num] = []
+
+        # Select detection results
+        det_results = copy.deepcopy(det_file[det_file[:, 0] == f_num, :])
+
+        # Pre-process with IoU
+        det_results = det_results[:, 2:7]
+        det_results = [det_result for det_result in det_results]
+        det_results = pre_process.merge_boxes(det_results)
+
+        # Save each bbox
+        for det_result in det_results:
+            # Read result
+            left, top, w, h, score = det_result
+
+            # Calculate gps
+            gps = img2gps.to_gps_coord('test', 'S06', cam, [left, top, w, h])
+
+            # Calculate info
+            x, y = left + w * 0.5, top + h * 0.8
+
+            # Save info
+            save_info = ['S06', cam, f_num, 0, left, top, w, h, img_w, img_h, x, y, gps[0], gps[1], score]
+            det_info['S06'][cam][f_num].append(save_info)
+
+    # print current status
+    print('S06_%s Finished' % cam)
+
+
+# Save save_info
+with open('../outputs/1. det/mask_rcnn_0.2/det_small.pickle', 'wb') as f:
+    pickle.dump(det_info, f, pickle.HIGHEST_PROTOCOL)

1. utils/img2gps.py

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+import numpy as np
+from numpy.linalg import inv
+
+
+def to_gps_coord(mode, scene, cam, bbox):
+    # Read calibration information
+    cal_file_path = '../../dataset/AIC21_Track3/%s/%s/%s/calibration.txt' % (mode, scene, cam)
+    cal_file = open(cal_file_path, 'r')
+    cal_lines = cal_file.readlines()
+
+    # Create Homography matrix
+    H = cal_lines[0].split(':')[-1]
+    H = H.replace(';', ' ').split()
+    H = [float(i) for i in H]
+    H = np.reshape(H, (3, 3))
+    H = inv(H)
+
+    # Transform
+    left, top, w, h = bbox
+    center_pt = np.asarray([bbox[0] + 0.5 * w, bbox[1] + 0.5 * h, 1])
+    center_pt = np.reshape(center_pt, (3, 1))
+    gps_pt = np.matmul(H, center_pt)
+    gps_pt = gps_pt / gps_pt[-1]
+
+    # Close
+    cal_file.close()
+
+    return gps_pt[:2].squeeze()

1. utils/pre_process.py

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+import numpy as np
+
+
+def calc_iou(a, b):
+    # Decode information
+    a_left, a_top, a_w, a_h, _ = a
+    b_left, b_top, b_w, b_h, _ = b
+
+    # Calculate iou
+    left, top = max(a_left, b_left), max(a_top, b_top)
+    right, bot = min(a_left + a_w, b_left + b_w), min(a_top + a_h, b_top + b_h)
+    inter = max(0, right - left) * max(0, bot - top)
+
+    # Form
+    iou = np.zeros((3,))
+    iou[0] = inter / (a_w * a_h + b_w * b_h - inter)
+    iou[1] = inter / (a_w * a_h)
+    iou[2] = inter / (b_w * b_h)
+
+    return iou
+
+
+def check_iou(a_boxes, b_boxes):
+    # Check iou between boxes 'a' and boxes 'b'
+    iou = np.zeros((len(a_boxes), len(b_boxes), 3))
+    for idx, a in enumerate(a_boxes):
+        for jdx, b in enumerate(b_boxes):
+            iou[idx, jdx, :] = calc_iou(a, b)
+    return iou
+
+
+# Filter over detected boxes
+def merge_boxes(boxes):
+    while True:
+        # Check connectivity of boxes
+        iou = check_iou(boxes, boxes)
+        con = 0.5 <= iou[:, :, 0]
+        con += 0.66 <= iou[:, :, 1]
+        con += 0.66 <= iou[:, :, 2]
+        con = 1 <= con
+
+        # End condition
+        if np.sum(con) == len(boxes):
+            break
+
+        # Pick idx to delete
+        del_idx_can = np.where(np.sum(con, axis=0) >= 2)[0]
+        scores = [boxes[d_idx_c][-1] for d_idx_c in del_idx_can]
+        del_idx = del_idx_can[scores.index(min(scores))]
+        boxes.pop(del_idx)
+
+    return boxes