import numpy as np import glob import cv2 import sys,os correspondence = [0,1,2,3,4,5,6,7,14,10,11,12,13] gt_indices = [1,2,3,4,5,6,7,8,9,10,11,12,13] correspondence = [1,0,3,2,5,4,7,6,14,11,10,13,12] def drawCar(img,keypoints,bb=None): # if keypoints[0,2] >50 and keypoints[2,2] >50: # cv2.line(img,tuple(keypoints[0,0:2]),tuple(keypoints[2,0:2]),(255,0,0),5) # if keypoints[1,2] >50 and keypoints[3,2] >50: # cv2.line(img,tuple(keypoints[1,0:2]),tuple(keypoints[3,0:2]),(0,0,255),5) # if keypoints[0,2] >50 and keypoints[1,2] >50: # cv2.line(img,tuple(keypoints[0,0:2]),tuple(keypoints[1,0:2]),(0,255,0),5) # if keypoints[3,2] >50 and keypoints[2,2] >50: # cv2.line(img,tuple(keypoints[3,0:2]),tuple(keypoints[2,0:2]),(128,128,0),5) threshold = 20 # wheels if keypoints[1,2] >threshold: cv2.circle(img,tuple(keypoints[1,0:2]),3,(0,255,0),3) if keypoints[2,2] >threshold: cv2.circle(img,tuple(keypoints[2,0:2]),3,(64,255,255),3) if keypoints[3,2] >threshold: cv2.circle(img,tuple(keypoints[3,0:2]),3,(128,255,128),3) if keypoints[0,2] >threshold: cv2.circle(img,tuple(keypoints[0,0:2]),3,(128,255,0),3) if keypoints[0,2] >threshold and keypoints[2,2] >threshold: cv2.line(img,tuple(keypoints[0,0:2]),tuple(keypoints[2,0:2]),(255,0,0),2) if keypoints[1,2] >threshold and keypoints[3,2] >threshold: cv2.line(img,tuple(keypoints[1,0:2]),tuple(keypoints[3,0:2]),(255,0,0),2) if keypoints[0,2] >threshold and keypoints[1,2] >threshold: cv2.line(img,tuple(keypoints[0,0:2]),tuple(keypoints[1,0:2]),(255,0,0),2) if keypoints[2,2] >threshold and keypoints[3,2] >threshold: cv2.line(img,tuple(keypoints[2,0:2]),tuple(keypoints[3,0:2]),(255,0,0),2) # top of car if keypoints[10,2] >threshold: cv2.circle(img,tuple(keypoints[10,0:2]),3,(255,128,128),3) if keypoints[11,2] >threshold: cv2.circle(img,tuple(keypoints[11,0:2]),3,(128,128,128),3) if keypoints[12,2] >threshold: cv2.circle(img,tuple(keypoints[12,0:2]),3,(0,128,255),3) if keypoints[13,2] >threshold: cv2.circle(img,tuple(keypoints[13,0:2]),3,(0,255,255),3) if keypoints[10,2] >threshold and keypoints[12,2] >threshold: cv2.line(img,tuple(keypoints[10,0:2]),tuple(keypoints[12,0:2]),(0,255,0),2) if keypoints[11,2] >threshold and keypoints[13,2] >threshold: cv2.line(img,tuple(keypoints[11,0:2]),tuple(keypoints[13,0:2]),(0,255,0),2) if keypoints[10,2] >threshold and keypoints[11,2] >threshold: cv2.line(img,tuple(keypoints[10,0:2]),tuple(keypoints[11,0:2]),(0,255,0),2) if keypoints[12,2] >threshold and keypoints[13,2] >threshold: cv2.line(img,tuple(keypoints[12,0:2]),tuple(keypoints[13,0:2]),(0,255,0),2) # front head lights if keypoints[4,2] >threshold: cv2.circle(img,tuple(keypoints[4,0:2]),3,(0,255,0),3) if keypoints[0,2] >threshold and keypoints[4,2] >threshold: cv2.line(img,tuple(keypoints[0,0:2]),tuple(keypoints[4,0:2]),(0,0,255),2) if keypoints[10,2] >threshold and keypoints[4,2] >threshold: cv2.line(img,tuple(keypoints[10,0:2]),tuple(keypoints[4,0:2]),(0,0,255),2) if keypoints[5,2] >threshold: cv2.circle(img,tuple(keypoints[5,0:2]),3,(128,0,0),3) if keypoints[1,2] >threshold and keypoints[5,2] >threshold: cv2.line(img,tuple(keypoints[1,0:2]),tuple(keypoints[5,0:2]),(0,0,255),2) if keypoints[11,2] >threshold and keypoints[5,2] >threshold: cv2.line(img,tuple(keypoints[11,0:2]),tuple(keypoints[5,0:2]),(0,0,255),2) if keypoints[4,2] >threshold and keypoints[5,2] >threshold: cv2.line(img,tuple(keypoints[4,0:2]),tuple(keypoints[5,0:2]),(0,0,255),2) # back head lights if keypoints[6,2] >threshold: cv2.circle(img,tuple(keypoints[6,0:2]),3,(255,0,0),3) if keypoints[2,2] >threshold and keypoints[6,2] >threshold: cv2.line(img,tuple(keypoints[2,0:2]),tuple(keypoints[6,0:2]),(255,0,255),2) if keypoints[12,2] >threshold and keypoints[6,2] >threshold: cv2.line(img,tuple(keypoints[12,0:2]),tuple(keypoints[6,0:2]),(255,0,255),2) if keypoints[7,2] >threshold: cv2.circle(img,tuple(keypoints[7,0:2]),3,(255,0,128),5) if keypoints[3,2] >threshold and keypoints[7,2] >threshold: cv2.line(img,tuple(keypoints[3,0:2]),tuple(keypoints[7,0:2]),(255,0,255),2) if keypoints[13,2] >threshold and keypoints[7,2] >threshold: cv2.line(img,tuple(keypoints[13,0:2]),tuple(keypoints[7,0:2]),(255,0,255),2) if keypoints[6,2] >threshold and keypoints[7,2] >threshold: cv2.line(img,tuple(keypoints[6,0:2]),tuple(keypoints[7,0:2]),(255,0,255),2) # mirrror if keypoints[8,2] >threshold: cv2.circle(img,tuple(keypoints[8,0:2]),3,(128,0,128),5) if keypoints[8,2] >threshold and keypoints[4,2] >threshold: cv2.line(img,tuple(keypoints[8,0:2]),tuple(keypoints[4,0:2]),(0,0,255),2) if keypoints[9,2] >threshold: cv2.circle(img,tuple(keypoints[9,0:2]),3,(0,128,128),5) if keypoints[9,2] >threshold and keypoints[5,2] >threshold: cv2.line(img,tuple(keypoints[9,0:2]),tuple(keypoints[5,0:2]),(0,0,255),2) #cv2.line(img,tuple(keypoints[0,0:2]),tuple(keypoints[2,0:2]),(255,0,0),1) #cv2.line(img,tuple(keypoints[4,0:2]),tuple(keypoints[6,0:2]),(0,255,0),1) #cv2.line(img,tuple(keypoints[5,0:2]),tuple(keypoints[7,0:2]),(0,255,0),1) #cv2.line(img,tuple(keypoints[4,0:2]),tuple(keypoints[5,0:2]),(0,255,0),1) #cv2.line(img,tuple(keypoints[6,0:2]),tuple(keypoints[7,0:2]),(0,255,0),1) #cv2.line(img,tuple(keypoints[1]),tuple(keypoints[2]),(0,255,0),5) #cv2.line(img,tuple(keypoints[2]),tuple(keypoints[3]),(0,255,0),5) #cv2.line(img,tuple(keypoints[0]),tuple(keypoints[3]),(0,255,0),5) #print(keypoints[0]- [20,20]) def drawPerson(img,keypoints,bb=None): threshold = 10 # wheels if keypoints[0,2] >threshold: cv2.circle(img,tuple(keypoints[0,0:2]),3,(128,255,0),2) if keypoints[1,2] >threshold: cv2.circle(img,tuple(keypoints[1,0:2]),3,(0,255,0),2) if keypoints[2,2] >threshold: cv2.circle(img,tuple(keypoints[2,0:2]),3,(64,255,255),2) if keypoints[3,2] >threshold: cv2.circle(img,tuple(keypoints[3,0:2]),3,(128,255,128),2) if keypoints[4,2] >threshold: cv2.circle(img,tuple(keypoints[4,0:2]),3,(0,255,0),2) if keypoints[5,2] >threshold: cv2.circle(img,tuple(keypoints[5,0:2]),3,(128,0,0),2) if keypoints[6,2] >threshold: cv2.circle(img,tuple(keypoints[6,0:2]),3,(255,0,0),2) if keypoints[7,2] >threshold: cv2.circle(img,tuple(keypoints[7,0:2]),3,(255,0,128),2) if keypoints[8,2] >threshold: cv2.circle(img,tuple(keypoints[8,0:2]),3,(128,0,128),2) if keypoints[9,2] >threshold: cv2.circle(img,tuple(keypoints[9,0:2]),3,(0,128,128),2) if keypoints[10,2] >threshold: cv2.circle(img,tuple(keypoints[10,0:2]),3,(255,128,128),2) if keypoints[11,2] >threshold: cv2.circle(img,tuple(keypoints[11,0:2]),3,(128,128,128),2) if keypoints[12,2] >threshold: cv2.circle(img,tuple(keypoints[12,0:2]),3,(0,128,255),2) if keypoints[13,2] >threshold: cv2.circle(img,tuple(keypoints[13,0:2]),3,(0,255,255),2) if keypoints[14,2] >threshold: cv2.circle(img,tuple(keypoints[14,0:2]),3,(0,255,64),2) if keypoints[15,2] >threshold: cv2.circle(img,tuple(keypoints[15,0:2]),3,(0,64,255),2) if keypoints[0,2] >threshold and keypoints[1,2] >threshold: cv2.line(img,tuple(keypoints[0,0:2]),tuple(keypoints[1,0:2]),(0,255,0),2) if keypoints[1,2] >threshold and keypoints[2,2] >threshold: cv2.line(img,tuple(keypoints[1,0:2]),tuple(keypoints[2,0:2]),(0,255,0),2) if keypoints[2,2] >threshold and keypoints[6,2] >threshold: cv2.line(img,tuple(keypoints[2,0:2]),tuple(keypoints[6,0:2]),(0,255,0),2) if keypoints[3,2] >threshold and keypoints[4,2] >threshold: cv2.line(img,tuple(keypoints[3,0:2]),tuple(keypoints[4,0:2]),(0,255,0),2) if keypoints[3,2] >threshold and keypoints[6,2] >threshold: cv2.line(img,tuple(keypoints[3,0:2]),tuple(keypoints[6,0:2]),(0,255,0),2) if keypoints[4,2] >threshold and keypoints[5,2] >threshold: cv2.line(img,tuple(keypoints[4,0:2]),tuple(keypoints[5,0:2]),(0,255,0),2) if keypoints[6,2] >threshold and keypoints[8,2] >threshold: cv2.line(img,tuple(keypoints[6,0:2]),tuple(keypoints[8,0:2]),(0,255,0),2) if keypoints[8,2] >threshold and keypoints[9,2] >threshold: cv2.line(img,tuple(keypoints[8,0:2]),tuple(keypoints[9,0:2]),(0,255,0),2) if keypoints[13,2] >threshold and keypoints[8,2] >threshold: cv2.line(img,tuple(keypoints[13,0:2]),tuple(keypoints[8,0:2]),(0,255,0),2) if keypoints[10,2] >threshold and keypoints[11,2] >threshold: cv2.line(img,tuple(keypoints[10,0:2]),tuple(keypoints[11,0:2]),(0,255,0),2) if keypoints[11,2] >threshold and keypoints[12,2] >threshold: cv2.line(img,tuple(keypoints[11,0:2]),tuple(keypoints[12,0:2]),(0,255,0),2) if keypoints[12,2] >threshold and keypoints[8,2] >threshold: cv2.line(img,tuple(keypoints[12,0:2]),tuple(keypoints[8,0:2]),(0,255,0),2) if keypoints[13,2] >threshold and keypoints[14,2] >threshold: cv2.line(img,tuple(keypoints[13,0:2]),tuple(keypoints[14,0:2]),(0,255,0),2) if keypoints[14,2] >threshold and keypoints[15,2] >threshold: cv2.line(img,tuple(keypoints[14,0:2]),tuple(keypoints[15,0:2]),(0,255,0),2) class BoundingBox(object): """ A 2D bounding box """ def __init__(self, points): if len(points) == 0: raise ValueError("Can't compute bounding box of empty list") self.minx, self.miny = float("inf"), float("inf") self.maxx, self.maxy = float("-inf"), float("-inf") for x, y in points: # Set min coords if x < self.minx: self.minx = x if y < self.miny: self.miny = y # Set max coords if x > self.maxx: self.maxx = x elif y > self.maxy: self.maxy = y @property def width(self): return self.maxx - self.minx @property def height(self): return self.maxy - self.miny def __repr__(self): return "BoundingBox({}, {}, {}, {})".format( self.minx, self.maxx, self.miny, self.maxy) # Usage example: def _compute_iou(boxA, boxB): # determine the (x, y)-coordinates of the intersection rectangle xA = max(boxA[0], boxB[0]) yA = max(boxA[1], boxB[1]) xB = min(boxA[0] + boxA[2], boxB[0] + boxB[2]) yB = min(boxA[1] + boxA[3], boxB[1] + boxB[3]) if xA < xB and yA < yB: # compute the area of intersection rectangle interArea = (xB - xA) * (yB - yA) # compute the area of both the prediction and ground-truth # rectangles boxAArea = boxA[2] * boxA[3] boxBArea = boxB[2] * boxB[3] # compute the intersection over union by taking the intersection # area and dividing it by the sum of prediction + ground-truth # areas - the intersection area iou = interArea / float(boxAArea + boxBArea - interArea) else: iou = 0 assert iou >= 0 assert iou <= 1.01 return iou def get_keypoints_gt(b): with open(b, 'r') as content_file: content = content_file.read() keypoints = content.split('\n') kp_float = [] indices = [] for loop,kp in enumerate(keypoints): if len(kp)>0: kp_float.append([float(kp.split(',')[0]),float(kp.split(',')[1]),int(kp.split(',')[2])]) indices.append(int(kp.split(',')[3])) kp_all = [] for loop_kp in range(max(indices)): kp_final = np.ones((16,3))*0 for loop,kp in enumerate(kp_float): if indices[loop] == loop_kp+1: #print(gt_indices,kp[2])#kp_final[t,0:2] = kp[0:2] t = correspondence[gt_indices.index(kp[2])]#kp_final[t,0:2] = kp[0:2] #print(kp[2],t,correspondence[gt_indices.index(kp[2])]) kp_final[t,0:2] = kp[0:2] kp_final[t,2] = 100 kp_all.append(np.round(kp_final.astype(np.float)).astype(np.int)) return kp_all def get_keypoints(name): kp_all_new = [] with open(name) as f: lines = f.readlines() bb = [] points = [] class_name = [] for line in lines: bb.append(np.array(line.split(',')[1:5]).astype(np.float)) points.append(np.array(line.split(',')[5:-1]).astype(np.float)) class_name.append(line.split(',')[-1].split('\n')[0]) for bb_loop,bb_num in enumerate(bb): points_array = np.array(points[bb_loop])#.splitlines()[0].split(',')) points_arranged = points_array.reshape(int(len(points_array)/3),3) kp = points_arranged[:,0:3] kp = np.round(kp.astype(np.float)).astype(np.int) kp[:,0] = bb[bb_loop][0] + kp[:,0]*(bb[bb_loop][2]/64) kp[:,1] = bb[bb_loop][1] + kp[:,1]*(bb[bb_loop][3]/64) kp[:,2] = np.round(points_arranged[:,2].astype(np.float)*100).astype(np.int) kp_all_new.append(kp) return kp_all_new def count_pck(bb_gt,bb_computed,alpha,B): #print(bb_gt,bb_computed) count = 0 count_inliers = 0 for loop,point in enumerate(bb_computed): if point[2]>10 and loop != 8 and loop != 9: dist = np.linalg.norm(point[0:2]-bb_gt[loop,0:2]) #dist = np.linalg.norm(point[0]-bb_gt[loop,0]) #print(point[0:2],bb_gt[loop,0:2],dist) #print(dist,alpha*B) count = count+1 if dist 1: Folder = sys.argv[1] image_name = sys.argv[2] GroundTruth_labels = Folder+'/gt/'+ sys.argv[2] + '.txt' else: Folder = '.' GroundTruth_labels = Folder+'/gt/*.txt' print(GroundTruth_labels) save = 1 display = 1 allfiles = glob.glob(GroundTruth_labels) if save==1: try: os.mkdir(Folder+'/images_labeled') except: print('Folder exists') for a,b in enumerate(allfiles): #b = allfiles[17] filename = b.split('/')[-1] kp_all_GT = get_keypoints_gt(b) image_name = Folder + '/images_jpg/'+filename.split('/')[-1].split('.')[0] + '.jpg' img = cv2.imread(image_name) for a,kp_label in enumerate(kp_all_GT): drawCar(img,kp_label,[0,0,1,1]) if save==1: cv2.imwrite(image_name.replace('images_jpg','images_labeled'),img) if display==1: cv2.namedWindow('image', cv2.WINDOW_NORMAL) cv2.imshow('image',img) cv2.waitKey(0) cv2.destroyAllWindows() if __name__ == "__main__": main() #evaluate_pck()