├── .gitignore ├── README.md ├── models ├── anchors.csv ├── hand_landmark.tflite └── palm_detection_without_custom_op.tflite ├── res.gif ├── run.py ├── src ├── __init__.py ├── hand_tracker.py └── non_maximum_suppression.py └── test ├── __init__.py ├── hand_detector_nms_test.py └── test_non_maximum_suppression.py /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Hand tracking 2 | 3 | ### 1. File Description 4 | - `palm_detection_without_custom_op.tflite` (Palm detection) Model file: Downloaded from the [*mediapipe-models*] repository. 5 | - `hand_landmark.tflite` (Landmark detection) Model file: Downloaded from the [*mediapipe*] repository. 6 | - `anchors.csv`,`hand_tracker.py` files：Downloaded from the [*hand_tracking*] repository. 7 | 8 | ### 2. Setup 9 | ```sh 10 | pip install opencv-python tensorflow 11 | ``` 12 | 13 | ### 3. Implementation 14 | - ```python run.py --3d True``` for 3D Hand Tracking. 15 | - ```python run.py --3d False``` for just Palm Tracking. 16 | 17 | ### 4. Results 18 | #### Palm tracking: 19 | ![Result](/res.gif?raw=true "Result: Palm Tracking") 20 | 21 | ### 5. Acknowledgements 22 | - Thanks to @metalwhale for the python implementation of the mediapipe models. 23 | - Thanks to mediapipe for opensourcing these models. 24 | 25 | [*mediapipe-models*]: https://github.com/junhwanjang/mediapipe-models/tree/master/palm_detection/mediapipe_models 26 | [*mediapipe*]: https://github.com/google/mediapipe/tree/master/mediapipe/models 27 | [*hand_tracking*]: https://github.com/wolterlw/hand_tracking 28 | -------------------------------------------------------------------------------- /models/anchors.csv: -------------------------------------------------------------------------------- 1 | 0.015625,0.015625,1,1 2 | 0.015625,0.015625,1,1 3 | 0.046875,0.015625,1,1 4 | 0.046875,0.015625,1,1 5 | 0.078125,0.015625,1,1 6 | 0.078125,0.015625,1,1 7 | 0.109375,0.015625,1,1 8 | 0.109375,0.015625,1,1 9 | 0.140625,0.015625,1,1 10 | 0.140625,0.015625,1,1 11 | 0.171875,0.015625,1,1 12 | 0.171875,0.015625,1,1 13 | 0.203125,0.015625,1,1 14 | 0.203125,0.015625,1,1 15 | 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0.9375,0.8125,1,1 2895 | 0.9375,0.8125,1,1 2896 | 0.9375,0.8125,1,1 2897 | 0.0625,0.9375,1,1 2898 | 0.0625,0.9375,1,1 2899 | 0.0625,0.9375,1,1 2900 | 0.0625,0.9375,1,1 2901 | 0.0625,0.9375,1,1 2902 | 0.0625,0.9375,1,1 2903 | 0.1875,0.9375,1,1 2904 | 0.1875,0.9375,1,1 2905 | 0.1875,0.9375,1,1 2906 | 0.1875,0.9375,1,1 2907 | 0.1875,0.9375,1,1 2908 | 0.1875,0.9375,1,1 2909 | 0.3125,0.9375,1,1 2910 | 0.3125,0.9375,1,1 2911 | 0.3125,0.9375,1,1 2912 | 0.3125,0.9375,1,1 2913 | 0.3125,0.9375,1,1 2914 | 0.3125,0.9375,1,1 2915 | 0.4375,0.9375,1,1 2916 | 0.4375,0.9375,1,1 2917 | 0.4375,0.9375,1,1 2918 | 0.4375,0.9375,1,1 2919 | 0.4375,0.9375,1,1 2920 | 0.4375,0.9375,1,1 2921 | 0.5625,0.9375,1,1 2922 | 0.5625,0.9375,1,1 2923 | 0.5625,0.9375,1,1 2924 | 0.5625,0.9375,1,1 2925 | 0.5625,0.9375,1,1 2926 | 0.5625,0.9375,1,1 2927 | 0.6875,0.9375,1,1 2928 | 0.6875,0.9375,1,1 2929 | 0.6875,0.9375,1,1 2930 | 0.6875,0.9375,1,1 2931 | 0.6875,0.9375,1,1 2932 | 0.6875,0.9375,1,1 2933 | 0.8125,0.9375,1,1 2934 | 0.8125,0.9375,1,1 2935 | 0.8125,0.9375,1,1 2936 | 0.8125,0.9375,1,1 2937 | 0.8125,0.9375,1,1 2938 | 0.8125,0.9375,1,1 2939 | 0.9375,0.9375,1,1 2940 | 0.9375,0.9375,1,1 2941 | 0.9375,0.9375,1,1 2942 | 0.9375,0.9375,1,1 2943 | 0.9375,0.9375,1,1 2944 | 0.9375,0.9375,1,1 2945 | -------------------------------------------------------------------------------- /models/hand_landmark.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aashish2000/hand_tracking/589aa56bba204889e204d6f0c647f5c43c142168/models/hand_landmark.tflite -------------------------------------------------------------------------------- /models/palm_detection_without_custom_op.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aashish2000/hand_tracking/589aa56bba204889e204d6f0c647f5c43c142168/models/palm_detection_without_custom_op.tflite -------------------------------------------------------------------------------- /res.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aashish2000/hand_tracking/589aa56bba204889e204d6f0c647f5c43c142168/res.gif -------------------------------------------------------------------------------- /run.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import argparse 3 | from src.hand_tracker import HandTracker 4 | 5 | # USAGE: python run.py --3d [true/false] 6 | ap = argparse.ArgumentParser() 7 | ap.add_argument("--3d", required=True, 8 | help="Check for type of detection") 9 | args = vars(ap.parse_args()) 10 | 11 | WINDOW = "Hand Tracking" 12 | PALM_MODEL_PATH = "models/palm_detection_without_custom_op.tflite" 13 | LANDMARK_MODEL_PATH = "models/hand_landmark.tflite" 14 | ANCHORS_PATH = "models/anchors.csv" 15 | 16 | POINT_COLOR = (0, 255, 0) 17 | CONNECTION_COLOR = (255, 0, 0) 18 | THICKNESS = 2 19 | 20 | cv2.namedWindow(WINDOW) 21 | capture = cv2.VideoCapture(0) 22 | 23 | if capture.isOpened(): 24 | hasFrame, frame = capture.read() 25 | else: 26 | hasFrame = False 27 | 28 | # 8 12 16 20 29 | # | | | | 30 | # 7 11 15 19 31 | # 4 | | | | 32 | # | 6 10 14 18 33 | # 3 | | | | 34 | # | 5---9---13--17 35 | # 2 \ / 36 | # \ \ / 37 | # 1 \ / 38 | # \ \ / 39 | # ------0- 40 | connections = [ 41 | (0, 1), (1, 2), (2, 3), (3, 4), 42 | (5, 6), (6, 7), (7, 8), 43 | (9, 10), (10, 11), (11, 12), 44 | (13, 14), (14, 15), (15, 16), 45 | (17, 18), (18, 19), (19, 20), 46 | (0, 5), (5, 9), (9, 13), (13, 17), (0, 17) 47 | ] 48 | 49 | hand_3d = args["3d"] 50 | 51 | detector = HandTracker( 52 | hand_3d, 53 | PALM_MODEL_PATH, 54 | LANDMARK_MODEL_PATH, 55 | ANCHORS_PATH, 56 | box_shift=0.2, 57 | box_enlarge=1 58 | ) 59 | 60 | while hasFrame: 61 | image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 62 | points, bbox = detector(image) 63 | if points is not None: 64 | if hand_3d == "True": 65 | for point in points: 66 | x, y = point 67 | cv2.circle(frame, (int(x), int(y)), THICKNESS * 2, POINT_COLOR, THICKNESS) 68 | for connection in connections: 69 | x0, y0 = points[connection[0]] 70 | x1, y1 = points[connection[1]] 71 | cv2.line(frame, (int(x0), int(y0)), (int(x1), int(y1)), CONNECTION_COLOR, THICKNESS) 72 | else: 73 | cv2.line(frame, (int(bbox[0][0]), int(bbox[0][1])), (int(bbox[1][0]), int(bbox[1][1])), CONNECTION_COLOR, THICKNESS) 74 | cv2.line(frame, (int(bbox[1][0]), int(bbox[1][1])), (int(bbox[2][0]), int(bbox[2][1])), CONNECTION_COLOR, THICKNESS) 75 | cv2.line(frame, (int(bbox[2][0]), int(bbox[2][1])), (int(bbox[3][0]), int(bbox[3][1])), CONNECTION_COLOR, THICKNESS) 76 | cv2.line(frame, (int(bbox[3][0]), int(bbox[3][1])), (int(bbox[0][0]), int(bbox[0][1])), CONNECTION_COLOR, THICKNESS) 77 | 78 | cv2.imshow(WINDOW, frame) 79 | hasFrame, frame = capture.read() 80 | key = cv2.waitKey(1) 81 | if key == 27: 82 | break 83 | 84 | capture.release() 85 | cv2.destroyAllWindows() 86 | -------------------------------------------------------------------------------- /src/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aashish2000/hand_tracking/589aa56bba204889e204d6f0c647f5c43c142168/src/__init__.py -------------------------------------------------------------------------------- /src/hand_tracker.py: -------------------------------------------------------------------------------- 1 | import csv 2 | import cv2 3 | import numpy as np 4 | import tensorflow as tf 5 | 6 | from src.non_maximum_suppression import non_max_suppression_fast 7 | 8 | 9 | class HandTracker(): 10 | r""" 11 | Class to use Google's Mediapipe HandTracking pipeline from Python. 12 | So far only detection of a single hand is supported. 13 | Any any image size and aspect ratio supported. 14 | 15 | Args: 16 | palm_model: path to the palm_detection.tflite 17 | joint_model: path to the hand_landmark.tflite 18 | anchors_path: path to the csv containing SSD anchors 19 | Ourput: 20 | (21,2) array of hand joints. 21 | Examples:: 22 | >>> det = HandTracker(path1, path2, path3) 23 | >>> input_img = np.random.randint(0,255, 256*256*3).reshape(256,256,3) 24 | >>> keypoints, bbox = det(input_img) 25 | """ 26 | 27 | def __init__(self, hand_3d, palm_model, joint_model, anchors_path, 28 | box_enlarge=1.5, box_shift=0.2): 29 | self.box_shift = box_shift 30 | self.box_enlarge = box_enlarge 31 | self.hand_3d = hand_3d 32 | 33 | self.interp_palm = tf.lite.Interpreter(palm_model) 34 | self.interp_palm.allocate_tensors() 35 | 36 | 37 | # reading the SSD anchors 38 | with open(anchors_path, "r") as csv_f: 39 | self.anchors = np.r_[ 40 | [x for x in csv.reader(csv_f, quoting=csv.QUOTE_NONNUMERIC)] 41 | ] 42 | # reading tflite model paramteres 43 | output_details = self.interp_palm.get_output_details() 44 | input_details = self.interp_palm.get_input_details() 45 | 46 | self.in_idx = input_details[0]['index'] 47 | self.out_reg_idx = output_details[0]['index'] 48 | self.out_clf_idx = output_details[1]['index'] 49 | 50 | # in case of 3d hand tracking, run hand_landmark model 51 | if(self.hand_3d == "True"): 52 | self.interp_joint = tf.lite.Interpreter(joint_model) 53 | self.interp_joint.allocate_tensors() 54 | 55 | self.in_idx_joint = self.interp_joint.get_input_details()[0]['index'] 56 | self.out_idx_joint = self.interp_joint.get_output_details()[0]['index'] 57 | 58 | # 90° rotation matrix used to create the alignment trianlge 59 | self.R90 = np.r_[[[0,1],[-1,0]]] 60 | 61 | # trianlge target coordinates used to move the detected hand 62 | # into the right position 63 | self._target_triangle = np.float32([ 64 | [128, 128], 65 | [128, 0], 66 | [ 0, 128] 67 | ]) 68 | self._target_box = np.float32([ 69 | [ 0, 0, 1], 70 | [256, 0, 1], 71 | [256, 256, 1], 72 | [ 0, 256, 1], 73 | ]) 74 | 75 | def _get_triangle(self, kp0, kp2, dist=1): 76 | """get a triangle used to calculate Affine transformation matrix""" 77 | 78 | dir_v = kp2 - kp0 79 | dir_v /= np.linalg.norm(dir_v) 80 | 81 | dir_v_r = dir_v @ self.R90.T 82 | return np.float32([kp2, kp2+dir_v*dist, kp2 + dir_v_r*dist]) 83 | 84 | @staticmethod 85 | def _triangle_to_bbox(source): 86 | # plain old vector arithmetics 87 | bbox = np.c_[ 88 | [source[2] - source[0] + source[1]], 89 | [source[1] + source[0] - source[2]], 90 | [3 * source[0] - source[1] - source[2]], 91 | [source[2] - source[1] + source[0]], 92 | ].reshape(-1,2) 93 | return bbox 94 | 95 | @staticmethod 96 | def _im_normalize(img): 97 | return np.ascontiguousarray( 98 | 2 * ((img / 255) - 0.5 99 | ).astype('float32')) 100 | 101 | @staticmethod 102 | def _sigm(x): 103 | return 1 / (1 + np.exp(-x) ) 104 | 105 | @staticmethod 106 | def _pad1(x): 107 | return np.pad(x, ((0,0),(0,1)), constant_values=1, mode='constant') 108 | 109 | 110 | def predict_joints(self, img_norm): 111 | self.interp_joint.set_tensor( 112 | self.in_idx_joint, img_norm.reshape(1,256,256,3)) 113 | self.interp_joint.invoke() 114 | 115 | joints = self.interp_joint.get_tensor(self.out_idx_joint) 116 | return joints.reshape(-1,2) 117 | 118 | def detect_hand(self, img_norm): 119 | assert -1 <= img_norm.min() and img_norm.max() <= 1,\ 120 | "img_norm should be in range [-1, 1]" 121 | assert img_norm.shape == (256, 256, 3),\ 122 | "img_norm shape must be (256, 256, 3)" 123 | 124 | # predict hand location and 7 initial landmarks 125 | self.interp_palm.set_tensor(self.in_idx, img_norm[None]) 126 | self.interp_palm.invoke() 127 | 128 | """ 129 | out_reg shape is [number of anchors, 18] 130 | Second dimension 0 - 4 are bounding box offset, width and height: dx, dy, w ,h 131 | Second dimension 4 - 18 are 7 hand keypoint x and y coordinates: x1,y1,x2,y2,...x7,y7 132 | """ 133 | out_reg = self.interp_palm.get_tensor(self.out_reg_idx)[0] 134 | """ 135 | out_clf shape is [number of anchors] 136 | it is the classification score if there is a hand for each anchor box 137 | """ 138 | out_clf = self.interp_palm.get_tensor(self.out_clf_idx)[0,:,0] 139 | 140 | # finding the best prediction 141 | probabilities = self._sigm(out_clf) 142 | detecion_mask = probabilities > 0.5 143 | candidate_detect = out_reg[detecion_mask] 144 | candidate_anchors = self.anchors[detecion_mask] 145 | probabilities = probabilities[detecion_mask] 146 | 147 | if candidate_detect.shape[0] == 0: 148 | print("No hands found") 149 | return None, None, None 150 | 151 | # Pick the best bounding box with non maximum suppression 152 | # the boxes must be moved by the corresponding anchor first 153 | moved_candidate_detect = candidate_detect.copy() 154 | moved_candidate_detect[:, :2] = candidate_detect[:, :2] + (candidate_anchors[:, :2] * 256) 155 | box_ids = non_max_suppression_fast(moved_candidate_detect[:, :4], probabilities) 156 | 157 | # Pick the first detected hand. Could be adapted for multi hand recognition 158 | box_ids = box_ids[0] 159 | 160 | # bounding box offsets, width and height 161 | dx,dy,w,h = candidate_detect[box_ids, :4] 162 | center_wo_offst = candidate_anchors[box_ids,:2] * 256 163 | 164 | # 7 initial keypoints 165 | keypoints = center_wo_offst + candidate_detect[box_ids,4:].reshape(-1,2) 166 | side = max(w,h) * self.box_enlarge 167 | 168 | # now we need to move and rotate the detected hand for it to occupy a 169 | # 256x256 square 170 | # line from wrist keypoint to middle finger keypoint 171 | # should point straight up 172 | # TODO: replace triangle with the bbox directly 173 | source = self._get_triangle(keypoints[0], keypoints[2], side) 174 | source -= (keypoints[0] - keypoints[2]) * self.box_shift 175 | 176 | debug_info = { 177 | "detection_candidates": candidate_detect, 178 | "anchor_candidates": candidate_anchors, 179 | "selected_box_id": box_ids, 180 | } 181 | 182 | return source, keypoints, debug_info 183 | 184 | def preprocess_img(self, img): 185 | # fit the image into a 256x256 square 186 | shape = np.r_[img.shape] 187 | pad = (shape.max() - shape[:2]).astype('uint32') // 2 188 | img_pad = np.pad( 189 | img, 190 | ((pad[0],pad[0]), (pad[1],pad[1]), (0,0)), 191 | mode='constant') 192 | img_small = cv2.resize(img_pad, (256, 256)) 193 | img_small = np.ascontiguousarray(img_small) 194 | 195 | img_norm = self._im_normalize(img_small) 196 | return img_pad, img_norm, pad 197 | 198 | 199 | def __call__(self, img): 200 | img_pad, img_norm, pad = self.preprocess_img(img) 201 | 202 | source, keypoints, _ = self.detect_hand(img_norm) 203 | if source is None: 204 | return None, None 205 | 206 | # calculating transformation from img_pad coords 207 | # to img_landmark coords (cropped hand image) 208 | scale = max(img.shape) / 256 209 | Mtr = cv2.getAffineTransform( 210 | source * scale, 211 | self._target_triangle 212 | ) 213 | if(self.hand_3d=="True"): 214 | img_landmark = cv2.warpAffine( 215 | self._im_normalize(img_pad), Mtr, (256,256) 216 | ) 217 | 218 | # adding the [0,0,1] row to make the matrix square 219 | Mtr = self._pad1(Mtr.T).T 220 | Mtr[2,:2] = 0 221 | 222 | Minv = np.linalg.inv(Mtr) 223 | 224 | kp_orig = [] 225 | if(self.hand_3d=="True"): 226 | joints = self.predict_joints(img_landmark) 227 | kp_orig = (self._pad1(joints) @ Minv.T)[:,:2] 228 | kp_orig -= pad[::-1] 229 | 230 | 231 | # projecting keypoints back into original image coordinate space 232 | 233 | box_orig = (self._target_box @ Minv.T)[:,:2] 234 | box_orig -= pad[::-1] 235 | 236 | return kp_orig, box_orig 237 | -------------------------------------------------------------------------------- /src/non_maximum_suppression.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def non_max_suppression_fast(boxes, probabilities=None, overlap_threshold=0.3): 5 | """ 6 | Algorithm to filter bounding box proposals by removing the ones with a too low confidence score 7 | and with too much overlap. 8 | 9 | Source: https://www.pyimagesearch.com/2015/02/16/faster-non-maximum-suppression-python/ 10 | 11 | :param boxes: List of proposed bounding boxes 12 | :param overlap_threshold: the maximum overlap that is allowed 13 | :return: filtered boxes 14 | """ 15 | # if there are no boxes, return an empty list 16 | if boxes.shape[1] == 0: 17 | return [] 18 | # if the bounding boxes integers, convert them to floats -- 19 | # this is important since we'll be doing a bunch of divisions 20 | if boxes.dtype.kind == "i": 21 | boxes = boxes.astype("float") 22 | # initialize the list of picked indexes 23 | pick = [] 24 | # grab the coordinates of the bounding boxes 25 | x1 = boxes[:, 0] - (boxes[:, 2] / [2]) # center x - width/2 26 | y1 = boxes[:, 1] - (boxes[:, 3] / [2]) # center y - height/2 27 | x2 = boxes[:, 0] + (boxes[:, 2] / [2]) # center x + width/2 28 | y2 = boxes[:, 1] + (boxes[:, 3] / [2]) # center y + height/2 29 | 30 | # compute the area of the bounding boxes and grab the indexes to sort 31 | # (in the case that no probabilities are provided, simply sort on the 32 | # bottom-left y-coordinate) 33 | area = boxes[:, 2] * boxes[:, 3] # width * height 34 | idxs = y2 35 | 36 | 37 | # if probabilities are provided, sort on them instead 38 | if probabilities is not None: 39 | idxs = probabilities 40 | 41 | # sort the indexes 42 | idxs = np.argsort(idxs) 43 | # keep looping while some indexes still remain in the indexes 44 | # list 45 | while len(idxs) > 0: 46 | # grab the last index in the indexes list and add the 47 | # index value to the list of picked indexes 48 | last = len(idxs) - 1 49 | i = idxs[last] 50 | pick.append(i) 51 | # find the largest (x, y) coordinates for the start of 52 | # the bounding box and the smallest (x, y) coordinates 53 | # for the end of the bounding box 54 | xx1 = np.maximum(x1[i], x1[idxs[:last]]) 55 | yy1 = np.maximum(y1[i], y1[idxs[:last]]) 56 | xx2 = np.minimum(x2[i], x2[idxs[:last]]) 57 | yy2 = np.minimum(y2[i], y2[idxs[:last]]) 58 | # compute the width and height of the bounding box 59 | w = np.maximum(0, xx2 - xx1 + 1) 60 | h = np.maximum(0, yy2 - yy1 + 1) 61 | # compute the ratio of overlap 62 | overlap = (w * h) / area[idxs[:last]] 63 | # delete all indexes from the index list that have 64 | idxs = np.delete(idxs, np.concatenate(([last], 65 | np.where(overlap > overlap_threshold)[0]))) 66 | # return only the bounding boxes that were picked 67 | return pick 68 | -------------------------------------------------------------------------------- /test/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aashish2000/hand_tracking/589aa56bba204889e204d6f0c647f5c43c142168/test/__init__.py -------------------------------------------------------------------------------- /test/hand_detector_nms_test.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | 4 | from src.hand_tracker import HandTracker 5 | 6 | ESCAPE_KEY_CODE = 27 7 | 8 | WINDOW = "Hand Tracking" 9 | PALM_MODEL_PATH = "models/palm_detection_without_custom_op.tflite" 10 | LANDMARK_MODEL_PATH = "models/hand_landmark.tflite" 11 | ANCHORS_PATH = "models/anchors.csv" 12 | 13 | 14 | DRAW_ANCHORS = False 15 | DRAW_DETECTION_BOXES = True 16 | DRAW_BEST_DETECTION_BOX_NMS = True 17 | DRAW_HAND_KEYPOINTS = True 18 | 19 | 20 | def main(): 21 | cv2.namedWindow(WINDOW) 22 | capture = cv2.VideoCapture(0) 23 | 24 | if capture.isOpened(): 25 | hasFrame, frame = capture.read() 26 | else: 27 | hasFrame = False 28 | 29 | 30 | detector = HandTracker( 31 | PALM_MODEL_PATH, 32 | LANDMARK_MODEL_PATH, 33 | ANCHORS_PATH, 34 | box_shift=0.2, 35 | box_enlarge=1.3 36 | ) 37 | 38 | while hasFrame: 39 | image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 40 | scale = np.array(np.max(frame.shape[:2]) / 256.0) 41 | padding = [0, 280] 42 | 43 | img_pad, img_norm, pad = detector.preprocess_img(image) 44 | source, keypoints, debug_info = detector.detect_hand(img_norm) 45 | 46 | if debug_info is not None: 47 | candidate_detect = debug_info["detection_candidates"] 48 | candidate_anchors = debug_info["anchor_candidates"] 49 | selected_box_id = debug_info["selected_box_id"] 50 | 51 | if DRAW_ANCHORS and debug_info is not None: 52 | for anchor in candidate_anchors: 53 | dx, dy = anchor[:2] * 256 54 | w, h = anchor[2:] * 256 * 0.2 # no idea of 0.2 is the correct size multiplication 55 | box = box_from_dimensions(dx - (w/2), dy - (h/2), h, w) 56 | box *= scale 57 | box -= padding 58 | frame = draw_box(frame, box, color=(200, 0, 0)) 59 | 60 | if DRAW_DETECTION_BOXES and debug_info is not None: 61 | for i, detection in enumerate(candidate_detect): 62 | dx,dy,w,h = detection[:4] 63 | center_wo_offst = candidate_anchors[i, :2] * 256 64 | box = box_from_dimensions(dx - (w/2), dy - (h/2), h, w) 65 | box += center_wo_offst 66 | box *= scale 67 | box -= padding 68 | frame = draw_box(frame, box) 69 | 70 | if DRAW_HAND_KEYPOINTS and debug_info is not None: 71 | detection = candidate_detect[selected_box_id] 72 | center_wo_offst = candidate_anchors[i, :2] * 256 73 | hand_key_points = center_wo_offst + detection[4:].reshape(-1, 2) 74 | for key_point in hand_key_points: 75 | key_point *= scale 76 | key_point -= padding 77 | cv2.circle(frame, tuple(key_point.astype("int")), color=(255, 255, 255), radius=5, thickness=2) 78 | 79 | if DRAW_BEST_DETECTION_BOX_NMS and debug_info is not None: 80 | detection = candidate_detect[selected_box_id] 81 | dx, dy, w, h = detection[:4] 82 | center_wo_offst = candidate_anchors[selected_box_id, :2] * 256 83 | box = box_from_dimensions(dx - (w / 2), dy - (h / 2), h, w) 84 | box += center_wo_offst 85 | box *= scale 86 | box -= padding 87 | frame = draw_box(frame, box, color=(0, 0, 255)) 88 | 89 | cv2.imshow(WINDOW, frame) 90 | hasFrame, frame = capture.read() 91 | key = cv2.waitKey(20) 92 | if key == ESCAPE_KEY_CODE: 93 | break 94 | 95 | capture.release() 96 | cv2.destroyAllWindows() 97 | 98 | 99 | def box_from_dimensions(dx, dy, h, w): 100 | box = [[dx, dy], [dx + w, dy], 101 | [dx + w, dy + h], [dx, dy + h]] 102 | return box 103 | 104 | def from_corners(x1, y1, x2, y2): 105 | return [[x1, y1], [x2, y1], [x2, y2], [x1, y2]] 106 | 107 | def draw_box(image, box, thickness=2, color=(0, 255, 0)): 108 | image = image.copy() 109 | if box is None or len(box) < 4: 110 | return 111 | for i in range(0, len(box)): 112 | i_inc_wrapped = (i + 1) % len(box) 113 | start_x, start_y = box[i] 114 | end_x, end_y = box[i_inc_wrapped] 115 | cv2.line(image, (int(start_x), int(start_y)), (int(end_x), int(end_y)), color, thickness) 116 | return image 117 | 118 | 119 | if __name__ == '__main__': 120 | main() -------------------------------------------------------------------------------- /test/test_non_maximum_suppression.py: -------------------------------------------------------------------------------- 1 | from unittest import TestCase 2 | import numpy as np 3 | 4 | from src.non_maximum_suppression import non_max_suppression_fast 5 | 6 | 7 | class Test(TestCase): 8 | def test_non_max_suppression_fast(self): 9 | 10 | # 3 boxes which overlap at 1,1 and another 3 boxes at 4,4 11 | boxes = np.array([ 12 | [1.0, 1.0, 2, 2], 13 | [1.1, 1.1, 2, 2], 14 | [1.1, 1.1, 1.9, 1.9], 15 | 16 | [4.0, 4.0, 2, 2], 17 | [4.1, 4.1, 2, 2], 18 | [4.1, 4.1, 1.9, 1.9] 19 | ]) 20 | 21 | picks = non_max_suppression_fast(boxes) 22 | self.assertEqual(len(picks), 2) 23 | 24 | --------------------------------------------------------------------------------