transfer analyzer code.

backend/video_to_photo.py

@@ -44,11 +44,12 @@ def split_video(path):
 
 
 def process_photo(name, c):
-    print("process_photo"+name)
+    print("process_photo" + name)
     for i in range(1, c):
         run_openpose_for_normal(i, name)
         # run_openpose_for_normal(i)
 
+
 # 保存跑完模型后的视频
 def gather_video(path):
     video_path = path
@@ -306,7 +307,7 @@ def ignore_data_gaotaitui(result):
     panduan = {}
     for i in result_ig:
         if (float(result_ig[i]["l_d"]) < 30):
-            if (float(result_ig[i]["knee_ankle_r_angle"]) < 60 and float(result_ig[i]["hip_knee_r_angle"]) >120):
+            if (float(result_ig[i]["knee_ankle_r_angle"]) < 60 and float(result_ig[i]["hip_knee_r_angle"]) > 120):
                 panduan[i] = result_ig[i]["r_d"]
     f = 0
     # ma=0
@@ -451,8 +452,6 @@ def write1(result, name):
     return name
 
 
-
-
 def run(video_path, mode, need_split):
     path = video_path
     os.path.split(path)

backend_refactor/service/photo_analyzer.py

@@ -342,11 +342,12 @@ def analyse_npy_side(npy_side, height):
 
 class StandingPhotoAnalyzer(BasePhotoAnalyzer):
 
-    def __init__(self, *args):
+    def __init__(self, data: dict):
         super().__init__()
-        assert len(args) == 3, "Invalid arguments: expect 2 photo uuids and height."
-        self.args = args[:2]
-        self.height = args[2]
+        assert len(data) == 3, "Invalid arguments: expect 2 photo uuids and height."
+        self.front = data['front']
+        self.right = data['right']
+        self.height = data['height']
 
     def analyze(self, callback: Callable) -> None:
         callback("RUNNING")
@@ -354,7 +355,7 @@ class StandingPhotoAnalyzer(BasePhotoAnalyzer):
             photos = []
             npys = []
 
-            for index, i in enumerate(self.args):
+            for index, i in enumerate(self.front, self.right):
                 test_image = UPLOAD_DIR + '{}.jpg'.format(i)
 
                 ori_img = cv2.imread(test_image)  # B,G,R order

backend_refactor/service/video_analyzer.py

@@ -1,3 +1,4 @@
+import math
 import os
 from typing import Callable
 from config import UPLOAD_DIR
@@ -35,6 +36,37 @@ def split_video(video_uuid):
     cap.release()
 
 
+def gather_video(video_uuid):
+    video_path = UPLOAD_DIR + video_uuid + ".mp4"
+    frames_dir = UPLOAD_DIR + "frames/" + video_uuid + "/"
+
+    # Just to get a fps
+    cap = cv2.VideoCapture(video_path)
+    fps = cap.get(5)
+    cap.close()
+
+    frames = [name for name in os.listdir(frames_dir) if
+              name.endswith(".jpg") and "npy" not in name and "result" not in name]
+    num_frames = len(frames)
+
+    img_array = []
+    for i in range(0, num_frames + 1):
+        frame_path = frames_dir + str(i) + ".jpg"
+        img = cv2.imread(frame_path)
+        if img is None:
+            continue
+        img_array.append(img)
+
+    logging.debug("Frames total: %d" % (len(img_array)))
+
+    result_path = UPLOAD_DIR + video_uuid + ".result.mp4"
+
+    out = cv2.VideoWriter(result_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, img_array[0].size)
+    for i in img_array:
+        out.write(i)
+    out.release()
+
+
 from service.aiutil.torch_openpose import torch_openpose
 from service.aiutil.util import draw_bodypose
 
@@ -62,8 +94,8 @@ def run_openpose_for_frames(video_uuid):
 
 
 class BaseVideoAnalyzer:
-    def __init__(self, video_uuid):
-        self.video_uuid = video_uuid
+    def __init__(self, data: dict):
+        self.video_uuid = data['video_uuid']
 
     def _do_analyze(self):
         assert False, "Internal error: You should override this with a subclass!"
@@ -73,12 +105,232 @@ class BaseVideoAnalyzer:
             callback("RUNNING")
             split_video(self.video_uuid)
             run_openpose_for_frames(self.video_uuid)
+            gather_video(self.video_uuid)
             callback("FINISHED", result=self._do_analyze())
         except Exception as e:
             callback("ERROR", error=e)
             raise e
 
 
+def angle1(v1, v2):
+    dx1 = v1[2] - v1[0]
+    dy1 = v1[3] - v1[1]
+    angle1 = float(math.atan2(dy1, dx1))
+    angle2 = abs(round(angle1 * 180.0 / math.pi, 2))
+    return angle2
+
+
+# calculate the angle between 3 points under the coordinates
+# params: list, item [x,y]
+# return: the angle value of b
+def cal_angle(point_a, point_b, point_c):
+    a_x, b_x, c_x = point_a[0], point_b[0], point_c[0]
+    a_y, b_y, c_y = point_a[1], point_b[1], point_c[1]
+    a_z, b_z, c_z = 0, 0, 0
+    #  m=(x1,y1,z1), n=(x2,y2,z2)
+    x1, y1, z1 = (a_x - b_x), (a_y - b_y), (a_z - b_z)
+    x2, y2, z2 = (c_x - b_x), (c_y - b_y), (c_z - b_z)
+
+    cos_b = (x1 * x2 + y1 * y2 + z1 * z2) / (
+            math.sqrt(x1 ** 2 + y1 ** 2 + z1 ** 2) * (math.sqrt(x2 ** 2 + y2 ** 2 + z2 ** 2)))
+    B = math.degrees(math.acos(cos_b))
+    return round(B, 2)
+
+
+def analyse_npy_crunch_side(data):
+    result = {}
+
+    print(len(data))
+    assert len(data) == 25, "error"
+
+    nose_x = data[0][0]
+    nose_y = data[0][1]
+    shoulder_r_x = data[2][0]
+    shoulder_r_y = data[2][1]
+    elbow_r_x = data[3][0]
+    elbow_r_y = data[3][1]
+    wrist_r_x = data[4][0]
+    wrist_r_y = data[4][1]
+
+    hip_r_x = data[9][0]
+    hip_r_y = data[9][1]
+    knee_r_x = data[10][0]
+    knee_r_y = data[10][1]
+    neck_x = data[1][0]
+    neck_y = data[1][1]
+    ankle_r_x = data[11][0]
+    ankle_r_y = data[11][1]
+    leg_heigh = data[11][1]
+    neck_hip_angle = round(angle1([neck_x, neck_y, hip_r_x, hip_r_y], [0, 0, 0, -1]), 2)
+    nose_neck_angle = round(angle1([nose_x, nose_y, neck_x, neck_y], [0, 0, 0, -1]), 2)
+    hip_knee_angle = round(angle1([hip_r_x, hip_r_y, knee_r_x, knee_r_y], [0, 0, 0, -1]), 2)
+    knee_ankle_angle = round(angle1([knee_r_x, knee_r_y, ankle_r_x, ankle_r_y], [0, 0, 0, -1]), 2)
+    result["neck_hip_angle"] = neck_hip_angle
+    result["nose_neck_angle"] = nose_neck_angle
+    result["hip_knee_angle"] = hip_knee_angle
+    result["knee_ankle_angle"] = knee_ankle_angle
+    return result
+
+
+def analyse_npy_side_pingban(data):
+    result = {}
+    assert len(data) >= 15, "error"
+
+    shoulder_r_x = data[2][0]
+    shoulder_r_y = data[2][1]
+    elbow_r_x = data[3][0]
+    elbow_r_y = data[3][1]
+    wrist_r_x = data[4][0]
+    wrist_r_y = data[4][1]
+
+    hip_r_x = data[9][0]
+    hip_r_y = data[9][1]
+    knee_r_x = data[10][0]
+    knee_r_y = data[10][1]
+    neck_x = data[1][0]
+    neck_y = data[1][1]
+    ankle_r_x = data[11][0]
+    ankle_r_y = data[11][1]
+    bigToe_r_x = data[22][0]
+    bigToe_r_y = data[22][1]
+    knee_ankle_angle = round(angle1([knee_r_x, knee_r_y, ankle_r_x, ankle_r_y], [0, 0, 0, -1]), 2)
+    leg_heigh = data[11][1]
+    neck_hip_angle = round(angle1([neck_x, neck_y, hip_r_x, hip_r_y], [0, 0, 0, -1]), 2)
+    hip_knee_angle = round(angle1([hip_r_x, hip_r_y, knee_r_x, knee_r_y], [0, 0, 0, -1]), 2)
+    s_e_angle = round(angle1([shoulder_r_x, shoulder_r_y, elbow_r_x, elbow_r_y], []), 2)
+    e_w_angle = round(angle1([elbow_r_x, elbow_r_y, wrist_r_x, wrist_r_y], []), 2)
+    hip_heigh = hip_r_y
+
+    result["knee_ankle_angle"] = knee_ankle_angle
+    result["leg_heigh"] = leg_heigh
+    result["neck_hip_angle"] = neck_hip_angle
+    result["hip_knee_angle"] = hip_knee_angle
+    result["hip_heigh"] = hip_heigh
+    result["s_e_angle"] = s_e_angle
+    result["e_w_angle"] = e_w_angle
+
+    # print(npy_side)
+    return result
+
+
+def analyse_npy_side_jump(data):
+    result = {}
+
+    assert len(data) == 25, "error"
+
+    # should-elbow-wrist r2-3-4 l5-6-7
+    # neck-hip-knee r1-9-10 l1-12-13
+    # hip-knee-ankle r9-10-11 l12-13-14
+    # knee-ankle-bigToe r10-11-22 l13-14-19
+    result["elbow_right_angle"] = cal_angle(data[2], data[3], data[4])
+    result["elbow_left_angle"] = cal_angle(data[5], data[6], data[7])
+    result["hip_right_angle"] = cal_angle(data[1], data[9], data[10])
+    result["hip_left_angle"] = cal_angle(data[1], data[12], data[13])
+    result["knee_right_angle"] = cal_angle(data[9], data[10], data[11])
+    result["knee_left_angle"] = cal_angle(data[12], data[13], data[14])
+    result["ankle_right_angle"] = cal_angle(data[10], data[11], data[12])
+    result["ankle_left_angle"] = cal_angle(data[13], data[14], data[19])
+    result["leg_heigh"] = data[11][1]  # ankle
+    result["hip_heigh"] = data[9][1]  # hip
+    result["wrist_heigh"] = data[4][1]
+    result["neck_heigh"] = data[1][1]
+
+    # knee_ankle_angle = round(angle1([knee_r_x, knee_r_y, ankle_r_x, ankle_r_y], [0, 0, 0, -1]), 2)
+    # neck_hip_angle = round(angle1([neck_x, neck_y, hip_r_x, hip_r_y], [0, 0, 0, -1]), 2)
+    # hip_knee_angle = round(angle1([hip_r_x, hip_r_y, knee_r_x, knee_r_y], [0, 0, 0, -1]), 2)
+    # hip_heigh = hip_r_y
+    # result["knee_ankle_angle"] = knee_ankle_angle
+    # result["leg_heigh"] = leg_heigh
+    # result["neck_hip_angle"] = neck_hip_angle
+    # result["hip_knee_angle"] = hip_knee_angle
+    # result["hip_heigh"] = hip_heigh
+
+    return result
+
+
+def analyse_npy_side_gaotaitui(data):
+    result = {}
+
+    assert len(data) == 25, "error"
+    # print(data)
+    hip_r_x = data[9][0]
+    hip_r_y = data[9][1]
+    knee_r_x = data[10][0]
+    knee_r_y = data[10][1]
+
+    hip_l_x = data[12][0]
+    hip_l_y = data[12][1]
+    knee_l_x = data[13][0]
+    knee_l_y = data[13][1]
+    # print(head_forward_level)
+    # upper part of body
+    up_risk_level = ""
+    up_state = ""
+    neck_x = data[1][0]
+    neck_y = data[1][1]
+    ankle_r_x = data[11][0]
+    ankle_r_y = data[11][1]
+    ankle_l_x = data[14][0]
+    ankle_l_y = data[14][1]
+    knee_ankle_r_angle = round(angle1([knee_r_x, knee_r_y, ankle_r_x, ankle_r_y], [0, 0, 0, -1]), 2)  # ϥ�ǵ���
+    knee_ankle_l_angle = round(angle1([knee_l_x, knee_l_y, ankle_l_x, ankle_l_y], [0, 0, 0, -1]), 2)  # ϥ�ǵ���
+
+    hip_knee_r_angle = round(angle1([hip_r_x, hip_r_y, knee_r_x, knee_r_y], [0, 0, 0, -1]), 2)
+    hip_knee_l_angle = round(angle1([hip_l_x, hip_l_y, knee_l_x, knee_l_y], [0, 0, 0, -1]), 2)
+    neck_hip_angle = round(angle1([neck_x, neck_y, hip_r_x, hip_r_y], [0, 0, 0, -1]), 2)
+    # hip_knee_angle=round(angle1([hip_r_x, hip_r_y,knee_r_x, knee_r_y], [0, 0, 0, -1]), 2)
+    hip_heigh = hip_r_y
+
+    result["knee_ankle_r_angle"] = knee_ankle_r_angle
+    result["knee_ankle_l_angle"] = knee_ankle_l_angle
+    result["hip_knee_r_angle"] = hip_knee_r_angle
+    result["hip_knee_l_angle"] = hip_knee_l_angle
+    result["r_d"] = abs(0 - float(hip_knee_r_angle) + float(knee_ankle_r_angle))
+    result["l_d"] = abs(0 + float(knee_ankle_l_angle) - float(hip_knee_l_angle))
+    return result
+
+
+def analyse_npy_side_shendun(data):
+    result = {}
+    assert len(data) == 25, "data length must be 25."
+    hip_r_x = data[9][0]
+    hip_r_y = data[9][1]
+    knee_r_x = data[10][0]
+    knee_r_y = data[10][1]
+
+    hip_l_x = data[12][0]
+    hip_l_y = data[12][1]
+    knee_l_x = data[13][0]
+    knee_l_y = data[13][1]
+    # print(head_forward_level)
+    # upper part of body
+    up_risk_level = ""
+    up_state = ""
+    neck_x = data[1][0]
+    neck_y = data[1][1]
+    ankle_r_x = data[11][0]
+    ankle_r_y = data[11][1]
+    ankle_l_x = data[14][0]
+    ankle_l_y = data[14][1]
+    knee_ankle_r_angle = round(angle1([knee_r_x, knee_r_y, ankle_r_x, ankle_r_y], [0, 0, 0, -1]), 2)
+    knee_ankle_l_angle = round(angle1([knee_l_x, knee_l_y, ankle_l_x, ankle_l_y], [0, 0, 0, -1]), 2)
+
+    hip_knee_r_angle = round(angle1([hip_r_x, hip_r_y, knee_r_x, knee_r_y], [0, 0, 0, -1]), 2)
+    hip_knee_l_angle = round(angle1([hip_l_x, hip_l_y, knee_l_x, knee_l_y], [0, 0, 0, -1]), 2)
+    neck_hip_angle = round(angle1([neck_x, neck_y, hip_r_x, hip_r_y], [0, 0, 0, -1]), 2)
+    # hip_knee_angle=round(angle1([hip_r_x, hip_r_y,knee_r_x, knee_r_y], [0, 0, 0, -1]), 2)
+    hip_heigh = hip_r_y
+
+    result["knee_ankle_r_angle"] = knee_ankle_r_angle
+    result["knee_ankle_l_angle"] = knee_ankle_l_angle
+    result["hip_knee_r_angle"] = hip_knee_r_angle
+    result["hip_knee_l_angle"] = hip_knee_l_angle
+    result["r_d"] = abs(float(result["hip_knee_r_angle"]) - float(result["knee_ankle_r_angle"]))
+
+    return result
+
+
+
 class HighKneesVideoAnalyzer(BaseVideoAnalyzer):
     def _do_analyze(self):
         pass