Cup Group C stats & predictions

Russia vs. Spain Preview

The clash between Russia and Spain is set to be one of the most anticipated matches of Group C. Both teams have demonstrated strong defensive capabilities, which suggests that this could be a tightly contested affair. Russia's recent performance has been impressive, with their ability to absorb pressure and launch effective counter-attacks being key factors in their success. On the other hand, Spain's possession-based approach aims to control the game tempo and exploit any weaknesses in Russia's defense through intricate passing combinations. ### Key Factors: - **Russia's Defense:** Known for their resilience, Russia has managed to keep clean sheets in several recent matches. - **Spain's Midfield Creativity:** Players like Pedri are expected to orchestrate Spain's play from the midfield. - **Set-Piece Opportunities:** Both teams have shown proficiency in converting set-pieces into goals. ### Betting Angle: Given the defensive nature of both teams, consider placing a bet on "Under 2.5 Goals" or "Draw No Bet" markets. ### Expert Prediction: While Spain may have slight edge due to their technical superiority, Russia's defensive organization could lead to a draw.

France vs. Germany Preview

This fixture promises fireworks as France seeks redemption from past disappointments while Germany looks to assert themselves under Hansi Flick's guidance. France boasts an array of attacking talent with Mbappé leading the charge alongside Griezmann and Benzema. Their ability to switch from defense to attack seamlessly makes them unpredictable opponents. ### Key Factors: - **France's Attacking Trio:** The combination of Mbappé, Griezmann, and Benzema poses a significant threat. - **Germany's Tactical Discipline:** Under Flick, Germany has shown improved organization and resilience. - **Psychological Edge:** France has been building momentum with consecutive wins. ### Betting Angle: With both teams capable of scoring multiple goals, consider betting on "Over 2.5 Goals" or backing France outright. ### Expert Prediction: France is expected to capitalize on their attacking prowess and secure a convincing victory.

Tactical Analysis: Russia vs. Spain

Russia will likely adopt a compact 4-5-1 formation designed to frustrate Spain's midfielders while remaining poised for counter-attacks led by Dzyuba up front. Spain might opt for their classic 4-3-3 setup aimed at controlling possession through midfield dominance. #### Key Tactics: - **Russia:** Focus on counter-attacks exploiting spaces left by Spain’s attacking full-backs. - **Spain:** Use quick interchanges between midfielders and forwards to break down Russia’s defense. #### Defensive Strategy: Both teams will need solid defensive strategies; Russia must prevent space exploitation by Pedri while Spain must handle Dzyuba’s physical presence. #### Offensive Strategy: Russia relies on quick transitions while Spain depends on sustained pressure through ball retention.

Tactical Analysis: France vs. Germany

In this high-stakes match-up, France might employ an aggressive 4-3-3 formation leveraging Mbappé’s speed on the wings supported by creative midfielders like Pogba or Kanté depending on their roles. #### Key Tactics: - **France:** Quick transitions from defense into attack utilizing wide areas. - **Germany:** Structured build-up play focusing on maintaining shape before launching attacks. #### Defensive Strategy: France will need tight marking on key German players like Havertz or Werner; Germany must manage space effectively against France’s dynamic attackers. #### Offensive Strategy: France focuses on exploiting spaces behind German defenders using overlapping full-backs; Germany aims for patient buildup through central channels.

Betting Market Trends & Statistics Overview

Analyzing past trends provides valuable insights into potential outcomes for tomorrow’s fixtures in Group C. ### Recent Trends: - **Russia Matches:** Historically strong defensive performances leading often towards low-scoring games. - **Spain Matches:** Consistent scoring across multiple games with high ball possession stats. - **France Matches:** Notable trend towards high-scoring games due mainly due offensive prowess. - **Germany Matches:** Balanced performance with an emphasis on solid defensive metrics lately. ### Statistical Highlights: #### Goals Scored:

Analyzing current sportsbook odds offers additional layers for making informed betting decisions: ### Current Odds Snapshot: #### Match Winner Odds: | Outcome | Odds | |------------------|----------| | Russia Win | +210 | | Draw | +180 | | España Win | +185 | #### Total Goals Odds: | Over/Under | Odds | |------------------|----------| | Over 1.5 Goals | +125 | | Under 1 <|repo_name|>chrispyang/LearningToPaint<|file_sep|>/scripts/make_annotation_images.py import os import os.path as osp import json from PIL import Image def get_bbox_list(ann_file): """Parse bboxes from annotation json file""" with open(ann_file) as f: ann = json.load(f) imgs = ann['images'] annotations = ann['annotations'] bbox_list = [] for anno in annotations: if anno['num_keypoints'] == 0: continue bbox = anno['bbox'] bbox[0] -= bbox[2] / 2 bbox[1] -= bbox[3] / 2 bbox_list.append(bbox) return bbox_list if __name__ == '__main__': coco_root = '/home/chris/datasets/coco' train_ann_file = osp.join(coco_root,'annotations/person_keypoints_train2017.json') val_ann_file = osp.join(coco_root,'annotations/person_keypoints_val2017.json') train_img_dir = osp.join(coco_root,'train2017') val_img_dir = osp.join(coco_root,'val2017') def save_bbox(img_dir,img_id,bbox_list): img_path = osp.join(img_dir,'%012d.jpg' % img_id) img = Image.open(img_path) img_arr = np.array(img) for bbox in bbox_list: img_arr[int(bbox[1]):int(bbox[1]+bbox[3]),int(bbox[0]):int(bbox[0]+bbox[2]),:] = [255.,0.,0.] new_img_path = img_path.replace('.jpg','_bbox.jpg') Image.fromarray(img_arr).save(new_img_path) print('processing train images...') for img in tqdm.tqdm(train_imgs): save_bbox(train_img_dir,img['id'],get_bbox_list(img)) print('processing val images...') for img in tqdm.tqdm(val_imgs): save_bbox(val_img_dir,img['id'],get_bbox_list(img))<|repo_name|>chrispyang/LearningToPaint<|file_sep|>/scripts/gen_coco_dataset.py import os import os.path as osp import glob from PIL import Image import json coco_root = '/home/chris/datasets/coco' train_img_dir = osp.join(coco_root,'train2017') val_img_dir = osp.join(coco_root,'val2017') def get_image_id(filename): return int(filename.split('.')[0]) def get_image_info(img_id,img_path): width,height=Image.open(img_path).size return {'id':img_id,'file_name':filename,'width':width,'height':height} train_annotations_file=osp.join(coco_root,'annotations/person_keypoints_train2017.json') val_annotations_file=osp.join(coco_root,'annotations/person_keypoints_val2017.json') with open(train_annotations_file) as f: train_annotations=json.load(f) with open(val_annotations_file) as f: val_annotations=json.load(f) train_imgs=train_annotations['images'] val_imgs=val_annotations['images'] all_imgs=[] all_imgs.extend(train_imgs) all_imgs.extend(val_imgs) imgs_by_id={img['id']:img for img in all_imgs} def get_keypoint_info(kpt,kpt_num): x=kpt[0] y=kpt[1] v=kpt[2] return {'x':x,'y':y,'is_visible':v==2} def get_annotation_info(anno,img_id): num_keypoints=anno['num_keypoints'] keypoints=anno['keypoints'] kpts=[] for i in range(num_keypoints): kpts.append(get_keypoint_info(keypoints[i*3:(i+1)*3],i)) return {'id':anno['id'],'image_id':img_id, 'iscrowd':anno['iscrowd'],'num_keypoints':num_keypoints, 'keypoints':kpts,'category_id':anno['category_id']} def gen_annotation_json(anno_dir,output_json_file): img_ids=os.listdir(anno_dir) img_ids.sort() all_anno_infos=[] for img_id_str in tqdm.tqdm(img_ids): img_id=int(img_id_str) filename='%012d.jpg'%img_id if filename not in imgs_by_id.keys(): continue img_info=imgs_by_id[filename] filepath=osp.join(anno_dir,filename) with open(filepath) as f: data=json.load(f) if len(data)==0: # no annotations continue for anno_data in data: if 'person' not in anno_data.keys(): # non-person annotations continue person_anno=anno_data['person'] human_anno=get_annotation_info(person_anno,img_id) all_anno_infos.append(human_anno) coco_format={'info':{'year':'2020','version':'v1','description':'COCO format annotation'}, 'licenses':[{'url':'https://creativecommons.org/licenses/by-sa/4.0/', 'id':1, 'name':'Attribution-ShareAlike License'}], 'images':all_imgs, 'annotations':all_anno_infos, 'categories':[{'supercategory':'person','id

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