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How to use transform_head method in pandera

Best Python code snippet using pandera_python

Image_Processing_Project.py

Source:Image_Processing_Project.py

1import cv22import numpy as np3import dlib4cap = cv2.VideoCapture('video.mp4')5# cap = cv2.VideoCapture('walking_man.mp4')6detector = dlib.get_frontal_face_detector()7predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")8#ë³¼ì°ê¸°9def cheek(imgorg):10    img_cheek = imgorg.copy()11    for face in faces:12        landmarks = predictor(imgorg, face)13        #ì¼ë³¼14        x1,x2 = landmarks.part(36).x, landmarks.part(48).x15        y1,y2 = landmarks.part(36).y, landmarks.part(48).y16        x3 = landmarks.part(31).x17        cen_x, cen_y = int((x1+x2)/2), int((y1+y2)/2)18        size = int((x3-cen_x)/2)19        cv2.circle(img_cheek,(cen_x,cen_y),size,(153,70,252),cv2.FILLED)20        #ì¤ë¥¸ë³¼21        x1,x2 = landmarks.part(45).x, landmarks.part(54).x22        y1,y2 = landmarks.part(45).y, landmarks.part(54).y23        right_x, right_y = int((x1 + x2) / 2), int((y1 + y2) / 2)24        cv2.circle(img_cheek, (right_x, right_y), size, (153, 70, 252), cv2.FILLED)25    return img_cheek26def sunglasses(imgorg,sunglass_img):27    img_sun = imgorg.copy()28    point0 = (int(sunglass_img.shape[1] * 0.2), int(sunglass_img.shape[0] * 0.5))29    point2 = (int(sunglass_img.shape[1] * 0.8), int(sunglass_img.shape[0] * 0.5))30    Src = [(point0[0], point0[1]), (point2[0], point2[1])] #ì ê¸ë¼ì¤ ì´ë¯¸ì§ì ê¸°ì¤ì 31    sunglass_gray = cv2.cvtColor(sunglass_img, cv2.COLOR_BGR2GRAY)32    ret, mask = cv2.threshold(sunglass_gray, 170, 255, cv2.THRESH_BINARY_INV)33    mask = cv2.merge((mask, mask, mask))34    mask_inv = cv2.bitwise_not(mask)35    sunglass_mask = cv2.bitwise_and(sunglass_img, mask)  # ì ê¸ë¼ì¤36    eye_mask = cv2.bitwise_and(sunglass_img, mask_inv)  # ìê²½ë§ì¤í¬ì ë37    eye_mask = cv2.bitwise_not(eye_mask)38    for face in faces:39        landmarks = predictor(imgorg, face)40        left_eye_x, left_eye_y = landmarks.part(36).x, landmarks.part(36).y41        right_eye_x, right_eye_y = landmarks.part(45).x, landmarks.part(45).y42        # ì ê¸ë¼ì¤ ì´ë¯¸ì§ì ëìì  ì ì¥43        Dst = [(left_eye_x, left_eye_y), (right_eye_x, right_eye_y)]44        # 2Dë³í íë ¬ ìì±45        ret = cv2.estimateAffinePartial2D(np.array([Src]), np.array([Dst]))46        transform_matrix = ret[0]47        # ì ê¸ë¼ì¤ ìì¹ ì´ë ë³í48        transform_sunglass = cv2.warpAffine(sunglass_mask, transform_matrix, (imgorg.shape[1], img_sun.shape[0]))49        # ë ë§ì¤í¬ ì´ë¯¸ì§ ìì¹ í¬ê¸° ì¡°ì 50        transform_eye = cv2.warpAffine(eye_mask, transform_matrix, (img_sun.shape[1], img_sun.shape[0]))51        sun_face = cv2.bitwise_and(img_sun, transform_eye)52        sun_face = cv2.addWeighted(sun_face, 0.5, transform_sunglass, 0.4, 0)53        face_without_eye = cv2.bitwise_and(img_sun, cv2.bitwise_not(transform_eye))54        img_sun = cv2.add(sun_face, face_without_eye)55    return img_sun56def head(imgorg,hat_img):57    img_head = imgorg.copy()58    point0 = (int(hat_img.shape[1] * 0.1), int(hat_img.shape[0] * 0.5)) #ë¨¸ë¦¬ë  ì´ë¯¸ì§ì ê¸°ì¤ì 59    point2 = (int(hat_img.shape[1] * 0.9), int(hat_img.shape[0] * 0.5))60    Src = [(point0[0]-40, point0[1]), (point2[0], point2[1])]61    head_gray = cv2.cvtColor(hat_img, cv2.COLOR_BGR2GRAY)62    ret, mask = cv2.threshold(head_gray, 180, 255, cv2.THRESH_BINARY_INV)63    mask = cv2.merge((mask, mask, mask))64    mask_inv = cv2.bitwise_not(mask)65    hat_mask = cv2.bitwise_and(hat_img, mask)  # ë¨¸ë¦¬ë 66    head_mask = cv2.bitwise_and(hat_img, mask_inv)  # ë¨¸ë¦¬ë ë§ì¤í¬67    head_mask = cv2.bitwise_not(head_mask)68    for face in faces:69        # ì¼êµ´ìì ì¢í70        landmarks = predictor(imgorg, face)71        x1, y1 = face.left(), face.top()72        x2, y2 = face.right(), face.bottom()73        y3 = int(y1 + ((y1 - y2) * 0.3))74        # ì ê¸ë¼ì¤ ì´ë¯¸ì§ì ëìì  ì ì¥75        Dst = [(x1 - 40, y3), (x2 + 10, y3)]76        # 2Dë³í íë ¬ ìì±77        ret = cv2.estimateAffinePartial2D(np.array([Src]), np.array([Dst]))78        transform_matrix = ret[0]79        # ë¨¸ë¦¬ë  ìì¹ ì´ë ë³í80        transform_hat = cv2.warpAffine(hat_mask, transform_matrix, (imgorg.shape[1], img_head.shape[0]))81        # ë ê²ì¶ ë§ì¤í¬ ì´ë¯¸ì§ ìì¹ í¬ê¸° ì¡°ì 82        transform_head = cv2.warpAffine(head_mask, transform_matrix, (img_head.shape[1], img_head.shape[0]))83        head_face = cv2.bitwise_and(img_head, transform_head)84        head_face = cv2.addWeighted(head_face, 0.0, transform_hat, 1.0, 0)85        face_without_eye = cv2.bitwise_and(img_head, cv2.bitwise_not(transform_head))86        img_hat = cv2.add(head_face, face_without_eye)87    return img_hat88def faceblur(imgorg):89    # ì¼êµ´ ììì ê²ì¶í´ ì¼êµ´ìì íëë¦¬ë¥¼ íìíê³  ê·¸ ë¶ë¶ë§ blur ì²ë¦¬í©ëë¤90    for face in faces:91        x1, y1 = face.left(), face.top()92        x2, y2 = face.right(), face.bottom()93        cv2.rectangle(imgorg, (x1, y1), (x2, y2), (0, 0, 0), 2)94        b_face = imgorg[y1:y2, x1:x2]95        b_face = cv2.blur(b_face, (10, 10))96        imgorg[y1:y2, x1:x2] = b_face97    return imgorg98while True:99    ret, img = cap.read()100    if not ret:101        break102    hat_img = cv2.imread("./images/headpin.png") #ë¨¸ë¦¬ë  ì´ë¯¸ì§103    sunglass_img = cv2.imread("./images/sunglass.png") #ì ê¸ë¼ì¤ ì´ë¯¸ì§104    scaler = 0.3 # ìì ì¬ì´ì¦ì¡°ì 105    img = cv2.resize(img, (int(img.shape[1] * scaler), int(img.shape[0] * scaler)))106    imgorg = img.copy()107    faces = detector(imgorg)108    #í¨ìí¸ì¶109    cheek_face = cheek(imgorg)110    sunglass_face = sunglasses(imgorg,sunglass_img)111    hat_face = head(imgorg, hat_img)112    blur_face = faceblur(imgorg)113    # cv2.imshow("img", img)114    cv2.imshow("cheek_face", cheek_face)115    cv2.imshow("sunglass_face", sunglass_face)116    cv2.imshow("hat_face", hat_face)117    cv2.imshow("blur_img",blur_face)...

learn_pyltp.py

Source:learn_pyltp.py

...68    arcs = get_arcs(words)  # å¥æ³åæ69    #print("\t".join("%d:%s" % (arc.head,arc.relation) for arc in arcs))70    parse = [arc.relation for arc in arcs]71    head = [arc.head for arc in arcs]72    head = transform_head(head)73    return parse,head74    75    76def get_role(words):77    postags = get_postag(words)# è¯æ§æ æ³¨78    arcs = get_arcs(words)  # å¥æ³åæ79    roles = labeller.label(words, postags, arcs)  # è¯ä¹è§è²æ æ³¨80    for role in roles:81        print (role.index, "".join(["%s:(%d,%d)" % \82                                    (arg.name, arg.range.start, arg.range.end)\83                                    for arg in role.arguments]))84    85    labeller.release()  # éæ¾æ¨¡å86def get_recognize(words):87    postags = get_postag(words)88    netags = recognizer.recognize(words, postags)  # å½åå®ä½è¯å«89    return netags90    recognizer.release()  # éæ¾æ¨¡å91def transform_head(head):92    '''93    å ä¸ºltpçè¯æ³å³ç³»æ¯ä»rootèç¹å¼å§çï¼94    0A,1Båæäº0rootï¼1A,2B95    ä¸ºäºåæä½¿ç¨æ¹ä¾¿ï¼æä»¬å¯ä»¥éè¿ä¸ä¸ªè½¬æ¢ï¼è®©è¯æ³å³ç³»ä¸è¯è¡¨çè¯ä¸ä¸å¯¹åº96    '''97    head_new = []98    for i in head:99        if i == 0:100            i=0101        else:102            i = i-1103        head_new.append(i)104    return head_new105if __name__ == '__main__':...

marker_roi_heads.py

Source:marker_roi_heads.py

...31        self.decoder_head = decoder_head32    @classmethod33    def from_config(cls, cfg, input_shape):34        ret = super().from_config(cfg)35        transform_head = build_transform_head(cfg, input_shape)36        ret["transform_head"] = transform_head37        corner_input_shape, decoder_input_shape = transform_head.output_shape38        ret["corner_head"] = build_corner_head(cfg, corner_input_shape)39        ret["decoder_head"] = build_decoder_head(cfg, decoder_input_shape)40        return ret41    @property42    def device(self):43        return self.corner_head.device44    45    def forward(46        self,47        images: ImageList,48        features: Dict[str, torch.Tensor],49        proposals: List[Instances],50        targets: Optional[List[Instances]] = None,51    ) -> Tuple[List[Dict], Dict]:52        # del images53        if self.training:54            assert targets55            proposals = self.label_and_sample_proposals(proposals, targets)56        # del targets57        58        if self.training:59            corner_features, decoding_features, sample_locations_batch, losses = self.transform_head(images, features, proposals, targets)60            losses.update(self.corner_head(corner_features, proposals))61            losses.update(self.decoder_head(decoding_features, proposals))62            del images, targets63            return [], losses64        65        corner_features, decoding_features, sample_locations_batch, _ = self.transform_head(images, features, proposals, targets)66        corners_batch = self.corner_head(corner_features, proposals)67        obj_scores_batch, decoded_messages_batch = self.decoder_head(decoding_features, proposals)68        results = []69        for i in range(len(proposals)):70            output = {71                "corners": corners_batch[i], "obj_scores": obj_scores_batch[i],72                "decoded_messages": decoded_messages_batch[i],73                "image_shape": proposals[i].image_size}74            if sample_locations_batch:75                output["sample_locations"] = sample_locations_batch[i]76            results.append(output)...

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